delete rag_api

* added bangla-bert-base

* Apply suggestions from code review

Co-authored-by: Julien Chaumond <chaumond@gmail.com>

.circleci/config.yml

@@ -77,14 +77,14 @@ jobs:
                       - v0.3-torch_and_tf-{{ checksum "setup.py" }}
                       - v0.3-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
-            - run: pip install git+https://github.com/huggingface/nlp
+            - run: pip install git+https://github.com/huggingface/datasets
             - run: pip install .[sklearn,tf-cpu,torch,testing]
             - run: pip install codecov pytest-cov
             - save_cache:
                 key: v0.3-{{ checksum "setup.py" }}
                 paths:
                     - '~/.cache/pip'
-            - run: python -m pytest -n 8 --dist=loadfile -s ./tests/ --cov  | tee output.txt
+            - run: python -m pytest -n 8 --dist=loadfile -rA -s ./tests/ --cov  | tee output.txt
             - run: codecov
             - store_artifacts:
                   path: ~/transformers/output.txt
@@ -104,13 +104,13 @@ jobs:
                       - v0.3-torch-{{ checksum "setup.py" }}
                       - v0.3-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
-            - run: pip install git+https://github.com/huggingface/nlp
+            - run: pip install git+https://github.com/huggingface/datasets
             - run: pip install .[sklearn,torch,testing]
             - save_cache:
                   key: v0.3-torch-{{ checksum "setup.py" }}
                   paths:
                       - '~/.cache/pip'
-            - run: python -m pytest -n 8 --dist=loadfile -s ./tests/ | tee output.txt
+            - run: python -m pytest -n 8 --dist=loadfile -rA -s ./tests/ | tee output.txt
             - store_artifacts:
                   path: ~/transformers/output.txt
                   destination: test_output.txt
@@ -129,13 +129,13 @@ jobs:
                       - v0.3-tf-{{ checksum "setup.py" }}
                       - v0.3-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
-            - run: pip install git+https://github.com/huggingface/nlp
+            - run: pip install git+https://github.com/huggingface/datasets
             - run: pip install .[sklearn,tf-cpu,testing]
             - save_cache:
                   key: v0.3-tf-{{ checksum "setup.py" }}
                   paths:
                       - '~/.cache/pip'
-            - run: python -m pytest -n 8 --dist=loadfile -s ./tests/ | tee output.txt
+            - run: python -m pytest -n 8 --dist=loadfile -rA -s ./tests/ | tee output.txt
             - store_artifacts:
                path: ~/transformers/output.txt
                destination: test_output.txt

.circleci/deploy.sh

@@ -47,4 +47,5 @@ deploy_doc "e7cfc1a" v2.9.0
 deploy_doc "7cb203f" v2.9.1
 deploy_doc "10d7239" v2.10.0 
 deploy_doc "b42586e" v2.11.0
-deploy_doc "7fb8bdf" #v3.0.2 Latest stable release
+deploy_doc "7fb8bdf" v3.0.2
+deploy_doc "4b3ee9c" # v3.1.0 Latest stable release

.github/workflows/self-push.yml

@@ -46,7 +46,7 @@ jobs:
         pip install --upgrade pip
         pip install torch!=1.6.0
         pip install .[sklearn,testing,onnxruntime]
-        pip install git+https://github.com/huggingface/nlp
+        pip install git+https://github.com/huggingface/datasets
 
     - name: Are GPUs recognized by our DL frameworks
       run: |

.github/workflows/self-scheduled.yml

@@ -43,6 +43,7 @@ jobs:
         pip install --upgrade pip
         pip install torch!=1.6.0
         pip install .[sklearn,testing,onnxruntime]
+        pip install git+https://github.com/huggingface/datasets
 
     - name: Are GPUs recognized by our DL frameworks
       run: |

.gitignore

@@ -11,6 +11,7 @@ __pycache__/
 # tests and logs
 tests/fixtures
 logs/
+lightning_logs/
 
 # Distribution / packaging
 .Python
@@ -139,6 +140,7 @@ runs
 /wandb
 /examples/runs
 /examples/**/*.args
+/examples/rag/sweep
 
 # data
 /data

CONTRIBUTING.md

@@ -134,6 +134,18 @@ Follow these steps to start contributing:
    it with `pip uninstall transformers` before reinstalling it in editable
    mode with the `-e` flag.)
 
+   To run the full test suite, you might need the additional dependency on `datasets` which requires a separate source
+   install:
+
+   ```bash
+   $ git clone https://github.com/huggingface/datasets
+   $ cd datasets
+   $ pip install -e .
+   ```
+
+   If you have already cloned that repo, you might need to `git pull` to get the most recent changes in the `datasets`
+   library.
+
 5. Develop the features on your branch.
 
    As you work on the features, you should make sure that the test suite
@@ -165,6 +177,16 @@ Follow these steps to start contributing:
    $ make quality
    ```
 
+   If you're modifying documents under `docs/source`, make sure to validate that
+   they can still be built. This check also runs in CI. To run a local check
+   make sure you have installed the documentation builder requirements, by
+   running `pip install .[tf,torch,docs]` once from the root of this repository
+   and then run:
+
+   ```bash
+   $ make docs
+   ```
+
    Once you're happy with your changes, add changed files using `git add` and
    make a commit with `git commit` to record your changes locally:
 

Makefile

@@ -1,4 +1,4 @@
-.PHONY: quality style test test-examples
+.PHONY: quality style test test-examples docs
 
 # Check that source code meets quality standards
 
@@ -23,3 +23,8 @@ test:
 
 test-examples:
 	python -m pytest -n auto --dist=loadfile -s -v ./examples/
+
+# Check that docs can build
+
+docs:
+	cd docs && make html SPHINXOPTS="-W"

README.md

@@ -172,8 +172,10 @@ for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) by Vladimi
 Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 23. **[Pegasus](https://github.com/google-research/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777)> by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
 24. **[MBart](https://github.com/pytorch/fairseq/tree/master/examples/mbart)** (from Facebook) released with the paper  [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.  
-25. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
-26. Want to contribute a new model? We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder of the repository. Be sure to check the [contributing guidelines](./CONTRIBUTING.md) and contact the maintainers or open an issue to collect feedbacks before starting your PR.
+25. **[LXMERT](https://github.com/airsplay/lxmert)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
+26. **[Funnel Transformer](https://github.com/laiguokun/Funnel-Transformer)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
+27. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
+28. Want to contribute a new model? We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder of the repository. Be sure to check the [contributing guidelines](./CONTRIBUTING.md) and contact the maintainers or open an issue to collect feedbacks before starting your PR.
 
 These implementations have been tested on several datasets (see the example scripts) and should match the performances of the original implementations (e.g. ~93 F1 on SQuAD for BERT Whole-Word-Masking, ~88 F1 on RocStories for OpenAI GPT, ~18.3 perplexity on WikiText 103 for Transformer-XL, ~0.916 Pearson R coefficient on STS-B for XLNet). You can find more details on the performances in the Examples section of the [documentation](https://huggingface.co/transformers/examples.html).
 
@@ -625,7 +627,7 @@ Breaking change in the `from_pretrained()` method:
 
 1. Models are now set in evaluation mode by default when instantiated with the `from_pretrained()` method. To train them, don't forget to set them back in training mode (`model.train()`) to activate the dropout modules.
 
-2. The additional `*input` and `**kwargs` arguments supplied to the `from_pretrained()` method used to be directly passed to the underlying model's class `__init__()` method. They are now used to update the model configuration attribute instead, which can break derived model classes built based on the previous `BertForSequenceClassification` examples. We are working on a way to mitigate this breaking change in [#866](https://github.com/huggingface/transformers/pull/866) by forwarding the the model's `__init__()` method (i) the provided positional arguments and (ii) the keyword arguments which do not match any configuration class attributes.
+2. The additional `*input` and `**kwargs` arguments supplied to the `from_pretrained()` method used to be directly passed to the underlying model's class `__init__()` method. They are now used to update the model configuration attribute instead, which can break derived model classes built based on the previous `BertForSequenceClassification` examples. We are working on a way to mitigate this breaking change in [#866](https://github.com/huggingface/transformers/pull/866) by forwarding the model's `__init__()` method (i) the provided positional arguments and (ii) the keyword arguments which do not match any configuration class attributes.
 
 Also, while not a breaking change, the serialization methods have been standardized and you probably should switch to the new method `save_pretrained(save_directory)` if you were using any other serialization method before.
 

docs/source/_static/js/custom.js

@@ -1,10 +1,11 @@
 // These two things need to be updated at each release for the version selector.
 // Last stable version
-const stableVersion = "v3.0.2"
+const stableVersion = "v3.1.0"
 // Dictionary doc folder to label
 const versionMapping = {
     "master": "master",
-    "": "v3.0.0/v3.0.1/v3.0.2 (stable)",
+    "": "v3.1.0 (stable)",
+    "v3.0.2": "v3.0.0/v3.0.1/v3.0.2 (stable)",
     "v2.11.0": "v2.11.0",
     "v2.10.0": "v2.10.0",
     "v2.9.1": "v2.9.0/v2.9.1",

docs/source/conf.py

@@ -26,7 +26,7 @@ author = u'huggingface'
 # The short X.Y version
 version = u''
 # The full version, including alpha/beta/rc tags
-release = u'3.0.2'
+release = u'3.1.0'
 
 
 # -- General configuration ---------------------------------------------------

docs/source/glossary.rst

@@ -128,7 +128,7 @@ The encoded versions have different lengths:
     >>> len(encoded_sequence_a), len(encoded_sequence_b)
     (8, 19)
 
-Therefore, we can't be put then together in a same tensor as-is. The first sequence needs to be padded up to the length
+Therefore, we can't put them together in the same tensor as-is. The first sequence needs to be padded up to the length
 of the second one, or the second one needs to be truncated down to the length of the first one.
 
 In the first case, the list of IDs will be extended by the padding indices. We can pass a list to the tokenizer and ask

docs/source/index.rst

@@ -128,7 +128,16 @@ conversion utilities for the following models:
     <https://arxiv.org/abs/1912.08777>`_ by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
 24. `MBart <https://github.com/pytorch/fairseq/tree/master/examples/mbart>`_ (from Facebook) released with the paper  `Multilingual Denoising Pre-training for Neural Machine Translation <https://arxiv.org/abs/2001.08210>`_ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov,
     Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.  
-25. `Other community models <https://huggingface.co/models>`_, contributed by the `community
+25. `LXMERT <https://github.com/airsplay/lxmert>`_ (from UNC Chapel Hill) released with the paper `LXMERT: Learning
+    Cross-Modality Encoder Representations from Transformers for Open-Domain Question
+    Answering <https://arxiv.org/abs/1908.07490>`_ by Hao Tan and Mohit Bansal.
+26. `Funnel Transformer <https://github.com/laiguokun/Funnel-Transformer>`_ (from CMU/Google Brain) released with the paper
+    `Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
+    <https://arxiv.org/abs/2006.03236>`_ by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
+27. `Bert For Sequence Generation <https://tfhub.dev/s?module-type=text-generation&subtype=module,placeholder>`_ (from Google) released with the paper
+    `Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
+    <https://arxiv.org/abs/1907.12461>`_ by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
+28. `Other community models <https://huggingface.co/models>`_, contributed by the `community
     <https://huggingface.co/users>`_.
 
 .. toctree::
@@ -185,6 +194,7 @@ conversion utilities for the following models:
     main_classes/trainer
     main_classes/optimizer_schedules
     main_classes/processors
+    main_classes/logging
     model_doc/auto
     model_doc/encoderdecoder
     model_doc/bert
@@ -212,6 +222,9 @@ conversion utilities for the following models:
     model_doc/dpr
     model_doc/pegasus
     model_doc/mbart
+    model_doc/funnel
+    model_doc/lxmert
+    model_doc/bertgeneration
     internal/modeling_utils
     internal/tokenization_utils
     internal/pipelines_utils

docs/source/main_classes/configuration.rst

@@ -1,12 +1,13 @@
 Configuration
 ----------------------------------------------------
 
-The base class ``PretrainedConfig`` implements the common methods for loading/saving a configuration either from a
-local file or directory, or from a pretrained model configuration provided by the library (downloaded from
-HuggingFace's AWS S3 repository).
+The base class :class:`~transformers.PretrainedConfig` implements the common methods for loading/saving a configuration
+either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded
+from HuggingFace's AWS S3 repository).
 
-``PretrainedConfig``
-~~~~~~~~~~~~~~~~~~~~~
+
+PretrainedConfig
+~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.PretrainedConfig
     :members:

docs/source/main_classes/logging.rst

@@ -0,0 +1,52 @@
+Logging
+-------
+
+🤗 Transformers has a centralized logging system, so that you can setup the verbosity of the library easily.
+
+Currently the default verbosity of the library is ``WARNING``.
+
+To change the level of verbosity, just use one of the direct setters. For instance, here is how to change the verbosity to the INFO level.
+
+.. code-block:: python
+
+    import transformers
+    transformers.logging.set_verbosity_info()
+
+You can also use the environment variable ``TRANSFORMERS_VERBOSITY`` to override the default verbosity. You can set it to one of the following: ``debug``, ``info``, ``warning``, ``error``, ``critical``. For example:
+
+.. code-block:: bash
+               
+    TRANSFORMERS_VERBOSITY=error ./myprogram.py
+
+All the methods of this logging module are documented below, the main ones are
+:func:`transformers.logging.get_verbosity` to get the current level of verbosity in the logger and
+:func:`transformers.logging.set_verbosity` to set the verbosity to the level of your choice. In order (from the least
+verbose to the most verbose), those levels (with their corresponding int values in parenthesis) are:
+
+- :obj:`transformers.logging.CRITICAL` or :obj:`transformers.logging.FATAL` (int value, 50): only report the most
+  critical errors.
+- :obj:`transformers.logging.ERROR` (int value, 40): only report errors.
+- :obj:`transformers.logging.WARNING` or :obj:`transformers.logging.WARN` (int value, 30): only reports error and
+  warnings. This the default level used by the library.
+- :obj:`transformers.logging.INFO` (int value, 20): reports error, warnings and basic information.
+- :obj:`transformers.logging.DEBUG` (int value, 10): report all information.
+
+Base setters
+~~~~~~~~~~~~
+
+.. autofunction:: transformers.logging.set_verbosity_error
+
+.. autofunction:: transformers.logging.set_verbosity_warning
+
+.. autofunction:: transformers.logging.set_verbosity_info
+
+.. autofunction:: transformers.logging.set_verbosity_debug
+
+Other functions
+~~~~~~~~~~~~~~~
+
+.. autofunction:: transformers.logging.get_verbosity
+
+.. autofunction:: transformers.logging.set_verbosity
+
+.. autofunction:: transformers.logging.get_logger

docs/source/main_classes/optimizer_schedules.rst

@@ -13,6 +13,11 @@ The ``.optimization`` module provides:
 .. autoclass:: transformers.AdamW
     :members:
 
+``AdaFactor`` (PyTorch)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.Adafactor
+
 ``AdamWeightDecay`` (TensorFlow)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/main_classes/pipelines.rst

@@ -21,6 +21,7 @@ There are two categories of pipeline abstractions to be aware about:
     - :class:`~transformers.TokenClassificationPipeline`
     - :class:`~transformers.TranslationPipeline`
     - :class:`~transformers.ZeroShotClassificationPipeline`
+    - :class:`~transformers.Text2TextGenerationPipeline`
 
 The pipeline abstraction
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -91,6 +92,13 @@ TextGenerationPipeline
     :special-members: __call__
     :members:
 
+Text2TextGenerationPipeline
+==========================================
+
+.. autoclass:: transformers.Text2TextGenerationPipeline
+    :special-members: __call__
+    :members:
+
 TokenClassificationPipeline
 ==========================================
 
@@ -105,7 +113,6 @@ ZeroShotClassificationPipeline
     :special-members: __call__
     :members:
 
-
 Parent class: :obj:`Pipeline`
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/main_classes/trainer.rst

@@ -21,12 +21,25 @@ previous features. To inject custom behavior you can subclass them and override
 - **setup_wandb** -- Setups wandb (see `here <https://docs.wandb.com/huggingface>`__ for more information).
 - **create_optimizer_and_scheduler** -- Setups the optimizer and learning rate scheduler if they were not passed at
   init.
+- **compute_loss** - Computes the loss on a batch of training inputs.
 - **training_step** -- Performs a training step.
 - **prediction_step** -- Performs an evaluation/test step.
 - **run_model** (TensorFlow only) -- Basic pass through the model.
 - **evaluate** -- Runs an evaluation loop and returns metrics.
 - **predict** -- Returns predictions (with metrics if labels are available) on a test set.
 
+Here is an example of how to customize :class:`~transformers.Trainer` using a custom loss function:
+
+.. code-block:: python
+
+    from transformers import Trainer
+    class MyTrainer(Trainer):
+        def compute_loss(self, model, inputs):
+            labels = inputs.pop("labels")
+            outputs = models(**inputs)
+            logits = outputs[0]
+            return my_custom_loss(logits, labels)
+
 
 ``Trainer`` 
 ~~~~~~~~~~~

docs/source/model_doc/bertgeneration.rst

@@ -0,0 +1,82 @@
+BertGeneration
+----------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~
+
+The BertGeneration model is a BERT model that can be leveraged for sequence-to-sequence tasks using :class:`~transformers.EncoderDecoderModel` as proposed in `Leveraging Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
+
+The abstract from the paper is the following:
+
+*Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing. By warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT, GPT-2 and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation, Text Summarization, Sentence Splitting, and Sentence Fusion.*
+
+Usage:
+
+- The model can be used in combination with the :class:`~transformers.EncoderDecoderModel` to leverage two bert pretrained bert checkpoints for subsequent fine-tuning.
+
+::
+  
+  # leverage checkpoints for Bert2Bert model...
+  encoder = BertGenerationEncoder.from_pretrained("bert-large-uncased", bos_token_id=101, eos_token_id=102)  # use BERT's cls token as BOS token and sep token as EOS token
+  decoder = BertGenerationDecoder.from_pretrained("bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102)  # add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
+  bert2bert = EncoderDecoderModel(encoder=encoder, decoder=decoder)
+  
+  # create tokenizer...
+  tokenizer = BertTokenizer.from_pretrained("bert-large-uncased")
+
+  input_ids = tokenizer('This is a long article to summarize', add_special_tokens=False, return_tensors="pt").input_ids
+  labels = tokenizer('This is a short summary', return_tensors="pt").input_ids
+
+  # train...
+  loss = bert2bert(input_ids=input_ids, decoder_input_ids=labels, labels=labels, return_dict=True).loss
+  loss.backward()
+
+
+- Pretrained :class:`~transformers.EncoderDecoderModel` are also directly available in the model hub, *e.g.*:
+
+
+::
+
+  # instantiate sentence fusion model
+  sentence_fuser = EncoderDecoderModel.from_pretrained("google/roberta2roberta_L-24_discofuse")
+  tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_discofuse")
+
+  input_ids = tokenizer('This is the first sentence. This is the second sentence.', add_special_tokens=False, return_tensors="pt").input_ids
+
+  outputs = sentence_fuser.generate(input_ids)
+
+  print(tokenizer.decode(outputs[0]))
+
+
+Tips:
+
+- :class:`~transformers.BertGenerationEncoder` and :class:`~transformers.BertGenerationDecoder`  should be used in combination with :class:`~transformers.EncoderDecoder`.
+- For summarization, sentence splitting, sentence fusion and translation, no special tokens are required for the input. Therefore, no EOS token should be added to the end of the input.
+
+The original code can be found `here <https://tfhub.dev/s?module-type=text-generation&subtype=module,placeholder>`__.
+
+BertGenerationConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BertGenerationConfig
+    :members:
+
+
+BertGenerationTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BertGenerationTokenizer
+    :members: 
+
+BertGenerationEncoder
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BertGenerationEncoder
+    :members:
+
+
+BertGenerationDecoder
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BertGenerationDecoder
+    :members:

docs/source/model_doc/encoderdecoder.rst

@@ -1,12 +1,13 @@
 Encoder Decoder Models
 ------------------------
 
-This class can wrap an encoder model, such as ``BertModel`` and a decoder modeling with a language modeling head, such as ``BertForMaskedLM`` into a encoder-decoder model.
+The :class:`~transformers.EncoderDecoderModel` can be used to initialize a sequence-to-sequence model with any pre-trained autoencoding model as the encoder and any pre-trained autoregressive model as the decoder.
 
-The ``EncoderDecoderModel`` class allows to instantiate a encoder decoder model using the ``from_encoder_decoder_pretrain`` class method taking a pretrained encoder and pretrained decoder model as an input. 
-The ``EncoderDecoderModel`` is saved using the standard ``save_pretrained()`` method and can also again be loaded using the standard ``from_pretrained()`` method. 
+The effectiveness of initializing sequence-to-sequence models with pre-trained checkpoints for sequence generation tasks was shown in `Leveraging Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
 
-An application of this architecture could be *summarization* using two pretrained Bert models as is shown in the paper: `Text Summarization with Pretrained Encoders <https://arxiv.org/abs/1910.13461>`_ by Yang Liu and Mirella Lapata. 
+After such an :class:`~transformers.EncoderDecoderModel` has been trained / fine-tuned, it can be saved / loaded just like any other models (see Examples for more information).
+
+An application of this architecture could be to leverage two pre-trained :obj:`transformers.BertModel` models as the encoder and decoder for a summarization model as was shown in: `Text Summarization with Pretrained Encoders <https://arxiv.org/abs/1910.13461>`_ by Yang Liu and Mirella Lapata. 
 
 
 ``EncoderDecoderConfig``

docs/source/model_doc/funnel.rst

@@ -0,0 +1,185 @@
+Funnel Transformer
+------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~
+
+The Funnel Transformer model was proposed in the paper
+`Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
+<https://arxiv.org/abs/2006.03236>`__.
+It is a bidirectional transformer model, like BERT, but with a pooling operation after each block of layers, a bit
+like in traditional convolutional neural networks (CNN) in computer vision.
+
+The abstract from the paper is the following:
+
+*With the success of language pretraining, it is highly desirable to develop more efficient architectures of good
+scalability that can exploit the abundant unlabeled data at a lower cost. To improve the efficiency, we examine the
+much-overlooked redundancy in maintaining a full-length token-level presentation, especially for tasks that only
+require a single-vector presentation of the sequence. With this intuition, we propose Funnel-Transformer which
+gradually compresses the sequence of hidden states to a shorter one and hence reduces the computation cost. More
+importantly, by re-investing the saved FLOPs from length reduction in constructing a deeper or wider model, we further
+improve the model capacity. In addition, to perform token-level predictions as required by common pretraining
+objectives, Funnel-Transformer is able to recover a deep representation for each token from the reduced hidden sequence
+via a decoder. Empirically, with comparable or fewer FLOPs, Funnel-Transformer outperforms the standard Transformer on
+a wide variety of sequence-level prediction tasks, including text classification, language understanding, and reading
+comprehension.*
+
+Tips:
+
+- Since Funnel Transformer uses pooling, the sequence length of the hidden states changes after each block of layers.
+  The base model therefore has a final sequence length that is a quarter of the original one. This model can be used
+  directly for tasks that just require a sentence summary (like sequence classification or multiple choice). For other
+  tasks, the full model is used; this full model has a decoder that upsamples the final hidden states to the same
+  sequence length as the input.
+- The Funnel Transformer checkpoints are all available with a full version and a base version. The first ones should
+  be used for :class:`~transformers.FunnelModel`, :class:`~transformers.FunnelForPreTraining`,
+  :class:`~transformers.FunnelForMaskedLM`, :class:`~transformers.FunnelForTokenClassification` and
+  class:`~transformers.FunnelForQuestionAnswering`. The second ones should be used for
+  :class:`~transformers.FunnelBaseModel`, :class:`~transformers.FunnelForSequenceClassification` and
+  :class:`~transformers.FunnelForMultipleChoice`.
+
+The original code can be found `here <https://github.com/laiguokun/Funnel-Transformer>`_.
+
+
+FunnelConfig
+~~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelConfig
+    :members:
+
+
+FunnelTokenizer
+~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelTokenizer
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary
+
+
+FunnelTokenizerFast
+~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelTokenizerFast
+    :members:
+
+
+Funnel specific outputs
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_funnel.FunnelForPreTrainingOutput
+    :members:
+
+.. autoclass:: transformers.modeling_tf_funnel.TFFunnelForPreTrainingOutput
+    :members:
+
+
+FunnelBaseModel
+~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelBaseModel
+    :members:
+
+
+FunnelModel
+~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelModel
+    :members:
+
+
+FunnelModelForPreTraining
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelForPreTraining
+    :members:
+
+
+FunnelForMaskedLM
+~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelForMaskedLM
+    :members:
+
+
+FunnelForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelForSequenceClassification
+    :members:
+
+
+FunnelForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelForMultipleChoice
+    :members:
+
+
+FunnelForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelForTokenClassification
+    :members:
+
+
+FunnelForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelForQuestionAnswering
+    :members:
+
+
+TFFunnelBaseModel
+~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFFunnelBaseModel
+    :members:
+
+
+TFFunnelModel
+~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFFunnelModel
+    :members:
+
+
+TFFunnelModelForPreTraining
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFFunnelForPreTraining
+    :members:
+
+
+TFFunnelForMaskedLM
+~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFFunnelForMaskedLM
+    :members:
+
+
+TFFunnelForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFFunnelForSequenceClassification
+    :members:
+
+
+TFFunnelForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFFunnelForMultipleChoice
+    :members:
+
+
+TFFunnelForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFFunnelForTokenClassification
+    :members:
+
+
+TFFunnelForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFFunnelForQuestionAnswering
+    :members:

docs/source/model_doc/lxmert.rst

@@ -0,0 +1,109 @@
+LXMERT
+----------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~
+
+The LXMERT model was proposed in `LXMERT: Learning Cross-Modality Encoder Representations from Transformers <https://arxiv.org/abs/1908.07490>`__
+by Hao Tan & Mohit Bansal. It is a series of bidirectional transformer encoders (one for the vision modality, one for the language modality, and then one to fuse both modalities)
+pre-trained using a combination of masked language modeling, visual-language text alignment, ROI-feature regression, masked visual-attribute modeling, masked visual-object modeling, and visual-question answering objectives.
+The pretraining consists of multiple multi-modal datasets: MSCOCO, Visual-Genome + Visual-Genome Question Answering, VQA 2.0, and GQA.
+
+The abstract from the paper is the following:
+
+*Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two
+modalities. We thus propose the LXMERT
+(Learning Cross-Modality Encoder Representations from Transformers) framework to learn
+these vision-and-language connections. In
+LXMERT, we build a large-scale Transformer
+model that consists of three encoders: an object relationship encoder, a language encoder,
+and a cross-modality encoder. Next, to endow our model with the capability of connecting vision and language semantics, we
+pre-train the model with large amounts of
+image-and-sentence pairs, via five diverse representative pre-training tasks: masked language modeling, masked object prediction
+(feature regression and label classification),
+cross-modality matching, and image question answering. These tasks help in learning both intra-modality and cross-modality relationships. After fine-tuning from our pretrained parameters, our model achieves the
+state-of-the-art results on two visual question answering datasets (i.e., VQA and GQA).
+We also show the generalizability of our pretrained cross-modality model by adapting it to
+a challenging visual-reasoning task, NLVR
+,
+and improve the previous best result by 22%
+absolute (54% to 76%). Lastly, we demonstrate detailed ablation studies to prove that
+both our novel model components and pretraining strategies significantly contribute to
+our strong results; and also present several
+attention visualizations for the different encoders*
+
+Tips:
+
+- Bounding boxes are not necessary to be used in the visual feature embeddings, any kind of visual-spacial features will work.
+- Both the language hidden states and the visual hidden states that LXMERT outputs are passed through the cross-modality layer, so they
+  contain information from both modalities. To access a modality that only attends to itself, select the vision/language hidden states from the first input in the tuple.
+- The bi-directional cross-modality encoder attention only returns attention values when the language modality is used as the input and the vision modality is used as the context vector. Further,
+  while the cross-modality encoder contains self-attention for each respective modality and cross-attention, only the cross attention is returned and both self attention outputs are disregarded.
+
+The code can be found `here <https://github.com/airsplay/lxmert>`__
+
+
+LxmertConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.LxmertConfig
+    :members:
+
+
+LxmertTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.LxmertTokenizer
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary
+
+
+Lxmert specific outputs
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_lxmert.LxmertModelOutput
+    :members:
+
+.. autoclass:: transformers.modeling_lxmert.LxmertForPreTrainingOutput
+    :members:
+
+.. autoclass:: transformers.modeling_lxmert.LxmertForQuestionAnsweringOutput
+    :members:
+
+.. autoclass:: transformers.modeling_tf_lxmert.TFLxmertModelOutput
+    :members:
+
+.. autoclass:: transformers.modeling_tf_lxmert.TFLxmertForPreTrainingOutput
+    :members:
+
+
+LxmertModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.LxmertModel
+    :members:
+
+LxmertForPreTraining
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.LxmertForPreTraining
+    :members:
+
+LxmertForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.LxmertForQuestionAnswering
+    :members:
+
+
+TFLxmertModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFLxmertModel
+    :members:
+
+TFLxmertForPreTraining
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFLxmertForPreTraining
+    :members:

docs/source/model_doc/pegasus.rst

@@ -60,7 +60,7 @@ Usage Example
     batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest').to(torch_device)
     translated = model.generate(**batch)
     tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
-    assert tgt_text[0] == "California's largest electricity provider has turned off power to tens of thousands of customers."
+    assert tgt_text[0] == "California's largest electricity provider has turned off power to hundreds of thousands of customers."
 
 PegasusForConditionalGeneration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/model_doc/xlmroberta.rst

@@ -56,6 +56,13 @@ XLMRobertaModel
     :members:
 
 
+XLMRobertaForCausalLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XLMRobertaForCausalLM
+    :members:
+
+
 XLMRobertaForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -130,4 +137,4 @@ TFXLMRobertaForQuestionAnswering
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLMRobertaForQuestionAnswering
-    :members:
+    :members:

docs/source/model_sharing.rst

@@ -136,6 +136,13 @@ Then log in using the same credentials as on huggingface.co. To upload your mode
 
 This will upload the folder containing the weights, tokenizer and configuration we prepared in the previous section.
 
+By default you will be prompted to confirm that you want these files to be uploaded. If you are uploading multiple models and need to script that process, you can add `-y` to bypass the prompt. For example:
+
+::
+
+    transformers-cli upload -y path/to/awesome-name-you-picked/
+
+
 If you want to upload a single file (a new version of your model, or the other framework checkpoint you want to add),
 just type:
 

docs/source/model_summary.rst

@@ -416,6 +416,38 @@ traditional GAN setting) then the ELECTRA model is trained for a few steps.
 The library provides a version of the model for masked language modeling, token classification and sentence
 classification.
 
+Funnel Transformer
+----------------------------------------------
+
+.. raw:: html
+
+   <a href="https://huggingface.co/models?filter=funnel">
+       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-funnel-blueviolet">
+   </a>
+   <a href="model_doc/funnel.html">
+       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-funnel-blueviolet">
+   </a>
+
+`Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
+<https://arxiv.org/abs/2006.03236>`_, Zihang Dai et al.
+
+Funnel Transformer is a transformer model using pooling, a bit like a ResNet model: layers are grouped in blocks, and
+at the beginning of each block (except the first one), the hidden states are pooled among the sequence dimension. This
+way, their length is divided by 2, which speeds up the computation of the next hidden states. All pretrained models
+have three blocks, which means the final hidden state has a sequence length that is one fourth of the original sequence
+length.
+
+For tasks such as classification, this is not a problem, but for tasks like masked language modeling or token
+classification, we need a hidden state with the same sequence length as the original input. In those cases, the final
+hidden states are upsampled to the input sequence length and go through two additional layers. That's why there are two
+versions of each checkpoint. The version suffixed with "-base" contains only the three blocks, while the version
+without that suffix contains the three blocks and the upsampling head with its additional layers.
+
+The pretrained models available use the same pretraining objective as ELECTRA.
+
+The library provides a version of the model for masked language modeling, token classification, sentence
+classification, multiple choice classification and question answering.
+
 .. _longformer:
 
 Longformer

docs/source/pretrained_models.rst

@@ -5,362 +5,406 @@ Here is the full list of the currently provided pretrained models together with
 
 For a list that includes community-uploaded models, refer to `https://huggingface.co/models <https://huggingface.co/models>`__.
 
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Architecture      | Shortcut name                                              | Details of the model                                                                                                                  |
-+===================+============================================================+=======================================================================================================================================+
-| BERT              | ``bert-base-uncased``                                      | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on lower-cased English text.                                                                                                |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-large-uncased``                                     | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
-|                   |                                                            | | Trained on lower-cased English text.                                                                                                |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-cased``                                        | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on cased English text.                                                                                                      |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-large-cased``                                       | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
-|                   |                                                            | | Trained on cased English text.                                                                                                      |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-multilingual-uncased``                         | | (Original, not recommended) 12-layer, 768-hidden, 12-heads, 110M parameters.                                                        |
-|                   |                                                            | | Trained on lower-cased text in the top 102 languages with the largest Wikipedias                                                    |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__).                                              |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-multilingual-cased``                           | | (New, **recommended**) 12-layer, 768-hidden, 12-heads, 110M parameters.                                                             |
-|                   |                                                            | | Trained on cased text in the top 104 languages with the largest Wikipedias                                                          |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__).                                              |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-chinese``                                      | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on cased Chinese Simplified and Traditional text.                                                                           |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-german-cased``                                 | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on cased German text by Deepset.ai                                                                                          |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details on deepset.ai website <https://deepset.ai/german-bert>`__).                                                             |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-large-uncased-whole-word-masking``                  | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
-|                   |                                                            | | Trained on lower-cased English text using Whole-Word-Masking                                                                        |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/google-research/bert/#bert>`__).                                                                    |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-large-cased-whole-word-masking``                    | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
-|                   |                                                            | | Trained on cased English text using Whole-Word-Masking                                                                              |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/google-research/bert/#bert>`__).                                                                    |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-large-uncased-whole-word-masking-finetuned-squad``  | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
-|                   |                                                            | | The ``bert-large-uncased-whole-word-masking`` model fine-tuned on SQuAD                                                             |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see details of fine-tuning in the `example section <https://github.com/huggingface/transformers/tree/master/examples>`__).           |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-large-cased-whole-word-masking-finetuned-squad``    | | 24-layer, 1024-hidden, 16-heads, 340M parameters                                                                                    |
-|                   |                                                            | | The ``bert-large-cased-whole-word-masking`` model fine-tuned on SQuAD                                                               |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__)                           |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-cased-finetuned-mrpc``                         | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | The ``bert-base-cased`` model fine-tuned on MRPC                                                                                    |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__)                           |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-german-dbmdz-cased``                           | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on cased German text by DBMDZ                                                                                               |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__).                                                         |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-german-dbmdz-uncased``                         | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on uncased German text by DBMDZ                                                                                             |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__).                                                         |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``cl-tohoku/bert-base-japanese``                           | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece and this requires some extra dependencies,                     |
-|                   |                                                            | | `fugashi <https://github.com/polm/fugashi>`__ which is a wrapper around `MeCab <https://taku910.github.io/mecab/>`__.               |
-|                   |                                                            | | Use ``pip install transformers["ja"]`` (or ``pip install -e .["ja"]`` if you install from source) to install them.                  |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``cl-tohoku/bert-base-japanese-whole-word-masking``        | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece and this requires some extra dependencies,                     |
-|                   |                                                            | | `fugashi <https://github.com/polm/fugashi>`__ which is a wrapper around `MeCab <https://taku910.github.io/mecab/>`__.               |
-|                   |                                                            | | Use ``pip install transformers["ja"]`` (or ``pip install -e .["ja"]`` if you install from source) to install them.                  |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``cl-tohoku/bert-base-japanese-char``                      | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on Japanese text. Text is tokenized into characters.                                                                        |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``cl-tohoku/bert-base-japanese-char-whole-word-masking``   | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized into characters.                                               |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``TurkuNLP/bert-base-finnish-cased-v1``                    | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on cased Finnish text.                                                                                                      |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__).                                                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``TurkuNLP/bert-base-finnish-uncased-v1``                  | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on uncased Finnish text.                                                                                                    |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__).                                                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``wietsedv/bert-base-dutch-cased``                         | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on cased Dutch text.                                                                                                        |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details on wietsedv repository <https://github.com/wietsedv/bertje/>`__).                                                       |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| GPT               | ``openai-gpt``                                             | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | OpenAI GPT English model                                                                                                            |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| GPT-2             | ``gpt2``                                                   | | 12-layer, 768-hidden, 12-heads, 117M parameters.                                                                                    |
-|                   |                                                            | | OpenAI GPT-2 English model                                                                                                          |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``gpt2-medium``                                            | | 24-layer, 1024-hidden, 16-heads, 345M parameters.                                                                                   |
-|                   |                                                            | | OpenAI's Medium-sized GPT-2 English model                                                                                           |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``gpt2-large``                                             | | 36-layer, 1280-hidden, 20-heads, 774M parameters.                                                                                   |
-|                   |                                                            | | OpenAI's Large-sized GPT-2 English model                                                                                            |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``gpt2-xl``                                                | | 48-layer, 1600-hidden, 25-heads, 1558M parameters.                                                                                  |
-|                   |                                                            | | OpenAI's XL-sized GPT-2 English model                                                                                               |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Transformer-XL    | ``transfo-xl-wt103``                                       | | 18-layer, 1024-hidden, 16-heads, 257M parameters.                                                                                   |
-|                   |                                                            | | English model trained on wikitext-103                                                                                               |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| XLNet             | ``xlnet-base-cased``                                       | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | XLNet English model                                                                                                                 |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlnet-large-cased``                                      | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
-|                   |                                                            | | XLNet Large English model                                                                                                           |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| XLM               | ``xlm-mlm-en-2048``                                        | | 12-layer, 2048-hidden, 16-heads                                                                                                     |
-|                   |                                                            | | XLM English model                                                                                                                   |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-mlm-ende-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
-|                   |                                                            | | XLM English-German model trained on the concatenation of English and German wikipedia                                               |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-mlm-enfr-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
-|                   |                                                            | | XLM English-French model trained on the concatenation of English and French wikipedia                                               |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-mlm-enro-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
-|                   |                                                            | | XLM English-Romanian Multi-language model                                                                                           |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-mlm-xnli15-1024``                                    | | 12-layer, 1024-hidden, 8-heads                                                                                                      |
-|                   |                                                            | | XLM Model pre-trained with MLM on the `15 XNLI languages <https://github.com/facebookresearch/XNLI>`__.                             |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-mlm-tlm-xnli15-1024``                                | | 12-layer, 1024-hidden, 8-heads                                                                                                      |
-|                   |                                                            | | XLM Model pre-trained with MLM + TLM on the `15 XNLI languages <https://github.com/facebookresearch/XNLI>`__.                       |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-clm-enfr-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
-|                   |                                                            | | XLM English-French model trained with CLM (Causal Language Modeling) on the concatenation of English and French wikipedia           |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-clm-ende-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
-|                   |                                                            | | XLM English-German model trained with CLM (Causal Language Modeling) on the concatenation of English and German wikipedia           |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-mlm-17-1280``                                        | | 16-layer, 1280-hidden, 16-heads                                                                                                     |
-|                   |                                                            | | XLM model trained with MLM (Masked Language Modeling) on 17 languages.                                                              |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-mlm-100-1280``                                       | | 16-layer, 1280-hidden, 16-heads                                                                                                     |
-|                   |                                                            | | XLM model trained with MLM (Masked Language Modeling) on 100 languages.                                                             |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| RoBERTa           | ``roberta-base``                                           | | 12-layer, 768-hidden, 12-heads, 125M parameters                                                                                     |
-|                   |                                                            | | RoBERTa using the BERT-base architecture                                                                                            |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__)                                                   |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``roberta-large``                                          | | 24-layer, 1024-hidden, 16-heads, 355M parameters                                                                                    |
-|                   |                                                            | | RoBERTa using the BERT-large architecture                                                                                           |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__)                                                   |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``roberta-large-mnli``                                     | | 24-layer, 1024-hidden, 16-heads, 355M parameters                                                                                    |
-|                   |                                                            | | ``roberta-large`` fine-tuned on `MNLI <http://www.nyu.edu/projects/bowman/multinli/>`__.                                            |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__)                                                   |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``distilroberta-base``                                     | | 6-layer, 768-hidden, 12-heads, 82M parameters                                                                                       |
-|                   |                                                            | | The DistilRoBERTa model distilled from the RoBERTa model `roberta-base` checkpoint.                                                 |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``roberta-base-openai-detector``                           | | 12-layer, 768-hidden, 12-heads, 125M parameters                                                                                     |
-|                   |                                                            | | ``roberta-base`` fine-tuned by OpenAI on the outputs of the 1.5B-parameter GPT-2 model.                                             |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/openai/gpt-2-output-dataset/tree/master/detector>`__)                                               |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``roberta-large-openai-detector``                          | | 24-layer, 1024-hidden, 16-heads, 355M parameters                                                                                    |
-|                   |                                                            | | ``roberta-large`` fine-tuned by OpenAI on the outputs of the 1.5B-parameter GPT-2 model.                                            |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/openai/gpt-2-output-dataset/tree/master/detector>`__)                                               |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| DistilBERT        | ``distilbert-base-uncased``                                | | 6-layer, 768-hidden, 12-heads, 66M parameters                                                                                       |
-|                   |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint                                                   |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``distilbert-base-uncased-distilled-squad``                | | 6-layer, 768-hidden, 12-heads, 66M parameters                                                                                       |
-|                   |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint, with an additional linear layer.                 |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``distilbert-base-cased``                                  | | 6-layer, 768-hidden, 12-heads, 65M parameters                                                                                       |
-|                   |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-cased` checkpoint                                                     |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``distilbert-base-cased-distilled-squad``                  | | 6-layer, 768-hidden, 12-heads, 65M parameters                                                                                       |
-|                   |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-cased` checkpoint, with an additional question answering layer.       |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``distilgpt2``                                             | | 6-layer, 768-hidden, 12-heads, 82M parameters                                                                                       |
-|                   |                                                            | | The DistilGPT2 model distilled from the GPT2 model `gpt2` checkpoint.                                                               |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``distilbert-base-german-cased``                           | | 6-layer, 768-hidden, 12-heads, 66M parameters                                                                                       |
-|                   |                                                            | | The German DistilBERT model distilled from the German DBMDZ BERT model `bert-base-german-dbmdz-cased` checkpoint.                   |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``distilbert-base-multilingual-cased``                     | | 6-layer, 768-hidden, 12-heads, 134M parameters                                                                                      |
-|                   |                                                            | | The multilingual DistilBERT model distilled from the Multilingual BERT model `bert-base-multilingual-cased` checkpoint.             |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| CTRL              | ``ctrl``                                                   | | 48-layer, 1280-hidden, 16-heads, 1.6B parameters                                                                                    |
-|                   |                                                            | | Salesforce's Large-sized CTRL English model                                                                                         |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| CamemBERT         | ``camembert-base``                                         | | 12-layer, 768-hidden, 12-heads, 110M parameters                                                                                     |
-|                   |                                                            | | CamemBERT using the BERT-base architecture                                                                                          |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/camembert>`__)                                                 |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| ALBERT            | ``albert-base-v1``                                         | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters                                                            |
-|                   |                                                            | | ALBERT base model                                                                                                                   |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``albert-large-v1``                                        | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters                                                           |
-|                   |                                                            | | ALBERT large model                                                                                                                  |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``albert-xlarge-v1``                                       | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters                                                           |
-|                   |                                                            | | ALBERT xlarge model                                                                                                                 |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``albert-xxlarge-v1``                                      | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters                                                           |
-|                   |                                                            | | ALBERT xxlarge model                                                                                                                |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``albert-base-v2``                                         | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters                                                            |
-|                   |                                                            | | ALBERT base model with no dropout, additional training data and longer training                                                     |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``albert-large-v2``                                        | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters                                                           |
-|                   |                                                            | | ALBERT large model with no dropout, additional training data and longer training                                                    |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``albert-xlarge-v2``                                       | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters                                                           |
-|                   |                                                            | | ALBERT xlarge model with no dropout, additional training data and longer training                                                   |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``albert-xxlarge-v2``                                      | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters                                                           |
-|                   |                                                            | | ALBERT xxlarge model with no dropout, additional training data and longer training                                                  |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| T5                | ``t5-small``                                               | | ~60M parameters with 6-layers, 512-hidden-state, 2048 feed-forward hidden-state, 8-heads,                                           |
-|                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``t5-base``                                                | | ~220M parameters with 12-layers, 768-hidden-state, 3072 feed-forward hidden-state, 12-heads,                                        |
-|                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``t5-large``                                               | | ~770M parameters with 24-layers, 1024-hidden-state, 4096 feed-forward hidden-state, 16-heads,                                       |
-|                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``t5-3B``                                                  | | ~2.8B parameters with 24-layers, 1024-hidden-state, 16384 feed-forward hidden-state, 32-heads,                                      |
-|                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``t5-11B``                                                 | | ~11B parameters with 24-layers, 1024-hidden-state, 65536 feed-forward hidden-state, 128-heads,                                      |
-|                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| XLM-RoBERTa       | ``xlm-roberta-base``                                       | | ~125M parameters with 12-layers, 768-hidden-state, 3072 feed-forward hidden-state, 8-heads,                                         |
-|                   |                                                            | | Trained on on 2.5 TB of newly created clean CommonCrawl data in 100 languages                                                       |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-roberta-large``                                      | | ~355M parameters with 24-layers, 1027-hidden-state, 4096 feed-forward hidden-state, 16-heads,                                       |
-|                   |                                                            | | Trained on 2.5 TB of newly created clean CommonCrawl data in 100 languages                                                          |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| FlauBERT          | ``flaubert/flaubert_small_cased``                          | | 6-layer, 512-hidden, 8-heads, 54M parameters                                                                                        |
-|                   |                                                            | | FlauBERT small architecture                                                                                                         |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``flaubert/flaubert_base_uncased``                         | | 12-layer, 768-hidden, 12-heads, 137M parameters                                                                                     |
-|                   |                                                            | | FlauBERT base architecture with uncased vocabulary                                                                                  |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``flaubert/flaubert_base_cased``                           | | 12-layer, 768-hidden, 12-heads, 138M parameters                                                                                     |
-|                   |                                                            | | FlauBERT base architecture with cased vocabulary                                                                                    |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``flaubert/flaubert_large_cased``                          | | 24-layer, 1024-hidden, 16-heads, 373M parameters                                                                                    |
-|                   |                                                            | | FlauBERT large architecture                                                                                                         |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Bart              | ``facebook/bart-large``                                    | | 24-layer, 1024-hidden, 16-heads, 406M parameters                                                                                    |
-|                   |                                                            |                                                                                                                                       |
-|                   |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_)                                                       |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``facebook/bart-base``                                     | | 12-layer, 768-hidden, 16-heads, 139M parameters                                                                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``facebook/bart-large-mnli``                               | | Adds a 2 layer classification head with 1 million parameters                                                                        |
-|                   |                                                            | | bart-large base architecture with a classification head, finetuned on MNLI                                                          |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``facebook/bart-large-cnn``                                | | 12-layer, 1024-hidden, 16-heads, 406M parameters       (same as base)                                                               |
-|                   |                                                            | | bart-large base architecture finetuned on cnn summarization task                                                                    |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| DialoGPT          | ``DialoGPT-small``                                         | | 12-layer, 768-hidden, 12-heads, 124M parameters                                                                                     |
-|                   |                                                            | | Trained on English text: 147M conversation-like exchanges extracted from Reddit.                                                    |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``DialoGPT-medium``                                        | | 24-layer, 1024-hidden, 16-heads, 355M parameters                                                                                    |
-|                   |                                                            | | Trained on English text: 147M conversation-like exchanges extracted from Reddit.                                                    |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``DialoGPT-large``                                         | | 36-layer, 1280-hidden, 20-heads, 774M parameters                                                                                    |
-|                   |                                                            | | Trained on English text: 147M conversation-like exchanges extracted from Reddit.                                                    |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Reformer          | ``reformer-enwik8``                                        | | 12-layer, 1024-hidden, 8-heads, 149M parameters                                                                                     |
-|                   |                                                            | | Trained on English Wikipedia data - enwik8.                                                                                         |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``reformer-crime-and-punishment``                          | | 6-layer, 256-hidden, 2-heads, 3M parameters                                                                                         |
-|                   |                                                            | | Trained on English text: Crime and Punishment novel by Fyodor Dostoyevsky.                                                          |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| MarianMT          | ``Helsinki-NLP/opus-mt-{src}-{tgt}``                       | | 12-layer, 512-hidden, 8-heads, ~74M parameter Machine translation models. Parameter counts vary depending on vocab size.            |
-|                   |                                                            | | (see `model list <https://huggingface.co/Helsinki-NLP>`_)                                                                           |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Pegasus           | ``google/pegasus-{dataset}``                               | | 16-layer, 1024-hidden, 16-heads, ~568M parameter, 2.2 GB for summary. `model list <https://huggingface.co/models?search=pegasus>`__ |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Longformer        | ``allenai/longformer-base-4096``                           | | 12-layer, 768-hidden, 12-heads, ~149M parameters                                                                                    |
-|                   |                                                            | | Starting from RoBERTa-base checkpoint, trained on documents of max length 4,096                                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``allenai/longformer-large-4096``                          | | 24-layer, 1024-hidden, 16-heads, ~435M parameters                                                                                   |
-|                   |                                                            | | Starting from RoBERTa-large checkpoint, trained on documents of max length 4,096                                                    |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| MBart             | ``facebook/mbart-large-cc25``                              | | 24-layer, 1024-hidden, 16-heads, 610M parameters                                                                                    |
-|                   |                                                            | | mBART (bart-large architecture) model trained on 25 languages' monolingual corpus                                                   |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``facebook/mbart-large-en-ro``                             | | 24-layer, 1024-hidden, 16-heads, 610M parameters                                                                                    |
-|                   |                                                            | | mbart-large-cc25 model finetuned on WMT english romanian translation.                                                               |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| Architecture       | Shortcut name                                              | Details of the model                                                                                                                  |
++====================+============================================================+=======================================================================================================================================+
+| BERT               | ``bert-base-uncased``                                      | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                    |                                                            | | Trained on lower-cased English text.                                                                                                |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``bert-large-uncased``                                     | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
+|                    |                                                            | | Trained on lower-cased English text.                                                                                                |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``bert-base-cased``                                        | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                    |                                                            | | Trained on cased English text.                                                                                                      |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``bert-large-cased``                                       | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
+|                    |                                                            | | Trained on cased English text.                                                                                                      |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``bert-base-multilingual-uncased``                         | | (Original, not recommended) 12-layer, 768-hidden, 12-heads, 110M parameters.                                                        |
+|                    |                                                            | | Trained on lower-cased text in the top 102 languages with the largest Wikipedias                                                    |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__).                                              |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``bert-base-multilingual-cased``                           | | (New, **recommended**) 12-layer, 768-hidden, 12-heads, 110M parameters.                                                             |
+|                    |                                                            | | Trained on cased text in the top 104 languages with the largest Wikipedias                                                          |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__).                                              |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``bert-base-chinese``                                      | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                    |                                                            | | Trained on cased Chinese Simplified and Traditional text.                                                                           |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``bert-base-german-cased``                                 | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                    |                                                            | | Trained on cased German text by Deepset.ai                                                                                          |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details on deepset.ai website <https://deepset.ai/german-bert>`__).                                                             |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``bert-large-uncased-whole-word-masking``                  | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
+|                    |                                                            | | Trained on lower-cased English text using Whole-Word-Masking                                                                        |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/google-research/bert/#bert>`__).                                                                    |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``bert-large-cased-whole-word-masking``                    | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
+|                    |                                                            | | Trained on cased English text using Whole-Word-Masking                                                                              |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/google-research/bert/#bert>`__).                                                                    |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``bert-large-uncased-whole-word-masking-finetuned-squad``  | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
+|                    |                                                            | | The ``bert-large-uncased-whole-word-masking`` model fine-tuned on SQuAD                                                             |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see details of fine-tuning in the `example section <https://github.com/huggingface/transformers/tree/master/examples>`__).           |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``bert-large-cased-whole-word-masking-finetuned-squad``    | | 24-layer, 1024-hidden, 16-heads, 340M parameters                                                                                    |
+|                    |                                                            | | The ``bert-large-cased-whole-word-masking`` model fine-tuned on SQuAD                                                               |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__)                           |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``bert-base-cased-finetuned-mrpc``                         | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                    |                                                            | | The ``bert-base-cased`` model fine-tuned on MRPC                                                                                    |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__)                           |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``bert-base-german-dbmdz-cased``                           | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                    |                                                            | | Trained on cased German text by DBMDZ                                                                                               |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__).                                                         |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``bert-base-german-dbmdz-uncased``                         | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                    |                                                            | | Trained on uncased German text by DBMDZ                                                                                             |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__).                                                         |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``cl-tohoku/bert-base-japanese``                           | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                    |                                                            | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece and this requires some extra dependencies,                     |
+|                    |                                                            | | `fugashi <https://github.com/polm/fugashi>`__ which is a wrapper around `MeCab <https://taku910.github.io/mecab/>`__.               |
+|                    |                                                            | | Use ``pip install transformers["ja"]`` (or ``pip install -e .["ja"]`` if you install from source) to install them.                  |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``cl-tohoku/bert-base-japanese-whole-word-masking``        | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                    |                                                            | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece and this requires some extra dependencies,                     |
+|                    |                                                            | | `fugashi <https://github.com/polm/fugashi>`__ which is a wrapper around `MeCab <https://taku910.github.io/mecab/>`__.               |
+|                    |                                                            | | Use ``pip install transformers["ja"]`` (or ``pip install -e .["ja"]`` if you install from source) to install them.                  |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``cl-tohoku/bert-base-japanese-char``                      | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                    |                                                            | | Trained on Japanese text. Text is tokenized into characters.                                                                        |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``cl-tohoku/bert-base-japanese-char-whole-word-masking``   | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                    |                                                            | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized into characters.                                               |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``TurkuNLP/bert-base-finnish-cased-v1``                    | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                    |                                                            | | Trained on cased Finnish text.                                                                                                      |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__).                                                                     |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``TurkuNLP/bert-base-finnish-uncased-v1``                  | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                    |                                                            | | Trained on uncased Finnish text.                                                                                                    |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__).                                                                     |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``wietsedv/bert-base-dutch-cased``                         | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                    |                                                            | | Trained on cased Dutch text.                                                                                                        |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details on wietsedv repository <https://github.com/wietsedv/bertje/>`__).                                                       |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| GPT                | ``openai-gpt``                                             | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                    |                                                            | | OpenAI GPT English model                                                                                                            |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| GPT-2              | ``gpt2``                                                   | | 12-layer, 768-hidden, 12-heads, 117M parameters.                                                                                    |
+|                    |                                                            | | OpenAI GPT-2 English model                                                                                                          |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``gpt2-medium``                                            | | 24-layer, 1024-hidden, 16-heads, 345M parameters.                                                                                   |
+|                    |                                                            | | OpenAI's Medium-sized GPT-2 English model                                                                                           |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``gpt2-large``                                             | | 36-layer, 1280-hidden, 20-heads, 774M parameters.                                                                                   |
+|                    |                                                            | | OpenAI's Large-sized GPT-2 English model                                                                                            |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``gpt2-xl``                                                | | 48-layer, 1600-hidden, 25-heads, 1558M parameters.                                                                                  |
+|                    |                                                            | | OpenAI's XL-sized GPT-2 English model                                                                                               |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| Transformer-XL     | ``transfo-xl-wt103``                                       | | 18-layer, 1024-hidden, 16-heads, 257M parameters.                                                                                   |
+|                    |                                                            | | English model trained on wikitext-103                                                                                               |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| XLNet              | ``xlnet-base-cased``                                       | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                    |                                                            | | XLNet English model                                                                                                                 |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``xlnet-large-cased``                                      | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
+|                    |                                                            | | XLNet Large English model                                                                                                           |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| XLM                | ``xlm-mlm-en-2048``                                        | | 12-layer, 2048-hidden, 16-heads                                                                                                     |
+|                    |                                                            | | XLM English model                                                                                                                   |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``xlm-mlm-ende-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
+|                    |                                                            | | XLM English-German model trained on the concatenation of English and German wikipedia                                               |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``xlm-mlm-enfr-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
+|                    |                                                            | | XLM English-French model trained on the concatenation of English and French wikipedia                                               |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``xlm-mlm-enro-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
+|                    |                                                            | | XLM English-Romanian Multi-language model                                                                                           |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``xlm-mlm-xnli15-1024``                                    | | 12-layer, 1024-hidden, 8-heads                                                                                                      |
+|                    |                                                            | | XLM Model pre-trained with MLM on the `15 XNLI languages <https://github.com/facebookresearch/XNLI>`__.                             |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``xlm-mlm-tlm-xnli15-1024``                                | | 12-layer, 1024-hidden, 8-heads                                                                                                      |
+|                    |                                                            | | XLM Model pre-trained with MLM + TLM on the `15 XNLI languages <https://github.com/facebookresearch/XNLI>`__.                       |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``xlm-clm-enfr-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
+|                    |                                                            | | XLM English-French model trained with CLM (Causal Language Modeling) on the concatenation of English and French wikipedia           |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``xlm-clm-ende-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
+|                    |                                                            | | XLM English-German model trained with CLM (Causal Language Modeling) on the concatenation of English and German wikipedia           |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``xlm-mlm-17-1280``                                        | | 16-layer, 1280-hidden, 16-heads                                                                                                     |
+|                    |                                                            | | XLM model trained with MLM (Masked Language Modeling) on 17 languages.                                                              |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``xlm-mlm-100-1280``                                       | | 16-layer, 1280-hidden, 16-heads                                                                                                     |
+|                    |                                                            | | XLM model trained with MLM (Masked Language Modeling) on 100 languages.                                                             |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| RoBERTa            | ``roberta-base``                                           | | 12-layer, 768-hidden, 12-heads, 125M parameters                                                                                     |
+|                    |                                                            | | RoBERTa using the BERT-base architecture                                                                                            |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__)                                                   |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``roberta-large``                                          | | 24-layer, 1024-hidden, 16-heads, 355M parameters                                                                                    |
+|                    |                                                            | | RoBERTa using the BERT-large architecture                                                                                           |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__)                                                   |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``roberta-large-mnli``                                     | | 24-layer, 1024-hidden, 16-heads, 355M parameters                                                                                    |
+|                    |                                                            | | ``roberta-large`` fine-tuned on `MNLI <http://www.nyu.edu/projects/bowman/multinli/>`__.                                            |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__)                                                   |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``distilroberta-base``                                     | | 6-layer, 768-hidden, 12-heads, 82M parameters                                                                                       |
+|                    |                                                            | | The DistilRoBERTa model distilled from the RoBERTa model `roberta-base` checkpoint.                                                 |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``roberta-base-openai-detector``                           | | 12-layer, 768-hidden, 12-heads, 125M parameters                                                                                     |
+|                    |                                                            | | ``roberta-base`` fine-tuned by OpenAI on the outputs of the 1.5B-parameter GPT-2 model.                                             |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/openai/gpt-2-output-dataset/tree/master/detector>`__)                                               |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``roberta-large-openai-detector``                          | | 24-layer, 1024-hidden, 16-heads, 355M parameters                                                                                    |
+|                    |                                                            | | ``roberta-large`` fine-tuned by OpenAI on the outputs of the 1.5B-parameter GPT-2 model.                                            |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/openai/gpt-2-output-dataset/tree/master/detector>`__)                                               |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| DistilBERT         | ``distilbert-base-uncased``                                | | 6-layer, 768-hidden, 12-heads, 66M parameters                                                                                       |
+|                    |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint                                                   |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``distilbert-base-uncased-distilled-squad``                | | 6-layer, 768-hidden, 12-heads, 66M parameters                                                                                       |
+|                    |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint, with an additional linear layer.                 |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``distilbert-base-cased``                                  | | 6-layer, 768-hidden, 12-heads, 65M parameters                                                                                       |
+|                    |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-cased` checkpoint                                                     |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``distilbert-base-cased-distilled-squad``                  | | 6-layer, 768-hidden, 12-heads, 65M parameters                                                                                       |
+|                    |                                                            | | The DistilBERT model distilled from the BERT model `bert-base-cased` checkpoint, with an additional question answering layer.       |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``distilgpt2``                                             | | 6-layer, 768-hidden, 12-heads, 82M parameters                                                                                       |
+|                    |                                                            | | The DistilGPT2 model distilled from the GPT2 model `gpt2` checkpoint.                                                               |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``distilbert-base-german-cased``                           | | 6-layer, 768-hidden, 12-heads, 66M parameters                                                                                       |
+|                    |                                                            | | The German DistilBERT model distilled from the German DBMDZ BERT model `bert-base-german-dbmdz-cased` checkpoint.                   |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``distilbert-base-multilingual-cased``                     | | 6-layer, 768-hidden, 12-heads, 134M parameters                                                                                      |
+|                    |                                                            | | The multilingual DistilBERT model distilled from the Multilingual BERT model `bert-base-multilingual-cased` checkpoint.             |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| CTRL               | ``ctrl``                                                   | | 48-layer, 1280-hidden, 16-heads, 1.6B parameters                                                                                    |
+|                    |                                                            | | Salesforce's Large-sized CTRL English model                                                                                         |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| CamemBERT          | ``camembert-base``                                         | | 12-layer, 768-hidden, 12-heads, 110M parameters                                                                                     |
+|                    |                                                            | | CamemBERT using the BERT-base architecture                                                                                          |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/camembert>`__)                                                 |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| ALBERT             | ``albert-base-v1``                                         | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters                                                            |
+|                    |                                                            | | ALBERT base model                                                                                                                   |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``albert-large-v1``                                        | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters                                                           |
+|                    |                                                            | | ALBERT large model                                                                                                                  |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``albert-xlarge-v1``                                       | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters                                                           |
+|                    |                                                            | | ALBERT xlarge model                                                                                                                 |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``albert-xxlarge-v1``                                      | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters                                                           |
+|                    |                                                            | | ALBERT xxlarge model                                                                                                                |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``albert-base-v2``                                         | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters                                                            |
+|                    |                                                            | | ALBERT base model with no dropout, additional training data and longer training                                                     |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``albert-large-v2``                                        | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters                                                           |
+|                    |                                                            | | ALBERT large model with no dropout, additional training data and longer training                                                    |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``albert-xlarge-v2``                                       | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters                                                           |
+|                    |                                                            | | ALBERT xlarge model with no dropout, additional training data and longer training                                                   |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``albert-xxlarge-v2``                                      | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters                                                           |
+|                    |                                                            | | ALBERT xxlarge model with no dropout, additional training data and longer training                                                  |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| T5                 | ``t5-small``                                               | | ~60M parameters with 6-layers, 512-hidden-state, 2048 feed-forward hidden-state, 8-heads,                                           |
+|                    |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``t5-base``                                                | | ~220M parameters with 12-layers, 768-hidden-state, 3072 feed-forward hidden-state, 12-heads,                                        |
+|                    |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``t5-large``                                               | | ~770M parameters with 24-layers, 1024-hidden-state, 4096 feed-forward hidden-state, 16-heads,                                       |
+|                    |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``t5-3B``                                                  | | ~2.8B parameters with 24-layers, 1024-hidden-state, 16384 feed-forward hidden-state, 32-heads,                                      |
+|                    |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``t5-11B``                                                 | | ~11B parameters with 24-layers, 1024-hidden-state, 65536 feed-forward hidden-state, 128-heads,                                      |
+|                    |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| XLM-RoBERTa        | ``xlm-roberta-base``                                       | | ~125M parameters with 12-layers, 768-hidden-state, 3072 feed-forward hidden-state, 8-heads,                                         |
+|                    |                                                            | | Trained on on 2.5 TB of newly created clean CommonCrawl data in 100 languages                                                       |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``xlm-roberta-large``                                      | | ~355M parameters with 24-layers, 1027-hidden-state, 4096 feed-forward hidden-state, 16-heads,                                       |
+|                    |                                                            | | Trained on 2.5 TB of newly created clean CommonCrawl data in 100 languages                                                          |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| FlauBERT           | ``flaubert/flaubert_small_cased``                          | | 6-layer, 512-hidden, 8-heads, 54M parameters                                                                                        |
+|                    |                                                            | | FlauBERT small architecture                                                                                                         |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``flaubert/flaubert_base_uncased``                         | | 12-layer, 768-hidden, 12-heads, 137M parameters                                                                                     |
+|                    |                                                            | | FlauBERT base architecture with uncased vocabulary                                                                                  |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``flaubert/flaubert_base_cased``                           | | 12-layer, 768-hidden, 12-heads, 138M parameters                                                                                     |
+|                    |                                                            | | FlauBERT base architecture with cased vocabulary                                                                                    |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``flaubert/flaubert_large_cased``                          | | 24-layer, 1024-hidden, 16-heads, 373M parameters                                                                                    |
+|                    |                                                            | | FlauBERT large architecture                                                                                                         |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| Bart               | ``facebook/bart-large``                                    | | 24-layer, 1024-hidden, 16-heads, 406M parameters                                                                                    |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_)                                                       |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``facebook/bart-base``                                     | | 12-layer, 768-hidden, 16-heads, 139M parameters                                                                                     |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``facebook/bart-large-mnli``                               | | Adds a 2 layer classification head with 1 million parameters                                                                        |
+|                    |                                                            | | bart-large base architecture with a classification head, finetuned on MNLI                                                          |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``facebook/bart-large-cnn``                                | | 12-layer, 1024-hidden, 16-heads, 406M parameters       (same as base)                                                               |
+|                    |                                                            | | bart-large base architecture finetuned on cnn summarization task                                                                    |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| DialoGPT           | ``DialoGPT-small``                                         | | 12-layer, 768-hidden, 12-heads, 124M parameters                                                                                     |
+|                    |                                                            | | Trained on English text: 147M conversation-like exchanges extracted from Reddit.                                                    |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``DialoGPT-medium``                                        | | 24-layer, 1024-hidden, 16-heads, 355M parameters                                                                                    |
+|                    |                                                            | | Trained on English text: 147M conversation-like exchanges extracted from Reddit.                                                    |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``DialoGPT-large``                                         | | 36-layer, 1280-hidden, 20-heads, 774M parameters                                                                                    |
+|                    |                                                            | | Trained on English text: 147M conversation-like exchanges extracted from Reddit.                                                    |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| Reformer           | ``reformer-enwik8``                                        | | 12-layer, 1024-hidden, 8-heads, 149M parameters                                                                                     |
+|                    |                                                            | | Trained on English Wikipedia data - enwik8.                                                                                         |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``reformer-crime-and-punishment``                          | | 6-layer, 256-hidden, 2-heads, 3M parameters                                                                                         |
+|                    |                                                            | | Trained on English text: Crime and Punishment novel by Fyodor Dostoyevsky.                                                          |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| MarianMT           | ``Helsinki-NLP/opus-mt-{src}-{tgt}``                       | | 12-layer, 512-hidden, 8-heads, ~74M parameter Machine translation models. Parameter counts vary depending on vocab size.            |
+|                    |                                                            | | (see `model list <https://huggingface.co/Helsinki-NLP>`_)                                                                           |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| Pegasus            | ``google/pegasus-{dataset}``                               | | 16-layer, 1024-hidden, 16-heads, ~568M parameter, 2.2 GB for summary. `model list <https://huggingface.co/models?search=pegasus>`__ |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| Longformer         | ``allenai/longformer-base-4096``                           | | 12-layer, 768-hidden, 12-heads, ~149M parameters                                                                                    |
+|                    |                                                            | | Starting from RoBERTa-base checkpoint, trained on documents of max length 4,096                                                     |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``allenai/longformer-large-4096``                          | | 24-layer, 1024-hidden, 16-heads, ~435M parameters                                                                                   |
+|                    |                                                            | | Starting from RoBERTa-large checkpoint, trained on documents of max length 4,096                                                    |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| MBart              | ``facebook/mbart-large-cc25``                              | | 24-layer, 1024-hidden, 16-heads, 610M parameters                                                                                    |
+|                    |                                                            | | mBART (bart-large architecture) model trained on 25 languages' monolingual corpus                                                   |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``facebook/mbart-large-en-ro``                             | | 24-layer, 1024-hidden, 16-heads, 610M parameters                                                                                    |
+|                    |                                                            | | mbart-large-cc25 model finetuned on WMT english romanian translation.                                                               |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| Lxmert             | ``lxmert-base-uncased``                                    | | 9-language layers, 9-relationship layers, and 12-cross-modality layers                                                              |
+|                    |                                                            | | 768-hidden, 12-heads (for each layer) ~ 228M parameters                                                                             |
+|                    |                                                            | | Starting from lxmert-base checkpoint, trained on over 9 million image-text couplets from COCO, VisualGenome, GQA, VQA               |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| Funnel Transformer | ``funnel-transformer/small``                               | | 14 layers: 3 blocks of 4 layers then 2 layers decoder, 768-hidden, 12-heads, 130M parameters                                        |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``funnel-transformer/small-base``                          | | 12 layers: 3 blocks of 4 layers (no decoder), 768-hidden, 12-heads, 115M parameters                                                 |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``funnel-transformer/medium``                              | | 14 layers: 3 blocks 6, 3x2, 3x2 layers then 2 layers decoder, 768-hidden, 12-heads, 130M parameters                                 |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``funnel-transformer/medium-base``                         | | 12 layers: 3 blocks 6, 3x2, 3x2 layers(no decoder), 768-hidden, 12-heads, 115M parameters                                           |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``funnel-transformer/intermediate``                        | | 20 layers: 3 blocks of 6 layers then 2 layers decoder, 768-hidden, 12-heads, 177M parameters                                        |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``funnel-transformer/intermediate-base``                   | | 18 layers: 3 blocks of 6 layers (no decoder), 768-hidden, 12-heads, 161M parameters                                                 |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``funnel-transformer/large``                               | | 26 layers: 3 blocks of 8 layers then 2 layers decoder, 1024-hidden, 12-heads, 386M parameters                                       |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``funnel-transformer/large-base``                          | | 24 layers: 3 blocks of 8 layers (no decoder), 1024-hidden, 12-heads, 358M parameters                                                |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``funnel-transformer/xlarge``                              | | 32 layers: 3 blocks of 10 layers then 2 layers decoder, 1024-hidden, 12-heads, 468M parameters                                      |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
+|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                    | ``funnel-transformer/xlarge-base``                         | | 30 layers: 3 blocks of 10 layers (no decoder), 1024-hidden, 12-heads, 440M parameters                                               |
+|                    |                                                            |                                                                                                                                       |
+|                    |                                                            | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__)                                                                   |
++--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+

docs/source/quicktour.rst

@@ -214,7 +214,7 @@ Using the model
 
 Once your input has been preprocessed by the tokenizer, you can send it directly to the model. As we mentioned, it will
 contain all the relevant information the model needs. If you're using a TensorFlow model, you can pass the
-dictionary keys directly to tensor, for a PyTorch model, you need to unpack the dictionary by adding :obj:`**`.
+dictionary keys directly to tensors, for a PyTorch model, you need to unpack the dictionary by adding :obj:`**`.
 
 .. code-block::
 

examples/bert-loses-patience/pabee/modeling_pabee_albert.py

@@ -242,7 +242,7 @@ class AlbertForSequenceClassificationWithPabee(AlbertPreTrainedModel):
         labels=None,
     ):
         r"""
-            labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
+            labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
                 Labels for computing the sequence classification/regression loss.
                 Indices should be in ``[0, ..., config.num_labels - 1]``.
                 If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),

examples/bert-loses-patience/pabee/modeling_pabee_bert.py

@@ -266,7 +266,7 @@ class BertForSequenceClassificationWithPabee(BertPreTrainedModel):
         labels=None,
     ):
         r"""
-            labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
+            labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
                 Labels for computing the sequence classification/regression loss.
                 Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
                 If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),

examples/deebert/src/modeling_highway_bert.py

@@ -302,7 +302,7 @@ class DeeBertForSequenceClassification(BertPreTrainedModel):
         train_highway=False,
     ):
         r"""
-            labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
+            labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
                 Labels for computing the sequence classification/regression loss.
                 Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
                 If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),

examples/deebert/src/modeling_highway_roberta.py

@@ -59,7 +59,7 @@ class DeeRobertaForSequenceClassification(BertPreTrainedModel):
         train_highway=False,
     ):
         r"""
-            labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
+            labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
                 Labels for computing the sequence classification/regression loss.
                 Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
                 If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),

examples/distillation/lm_seqs_dataset.py

@@ -101,7 +101,7 @@ class LmSeqsDataset(Dataset):
 
     def remove_empty_sequences(self):
         """
-        Too short sequences are simply removed. This could be tunedd.
+        Too short sequences are simply removed. This could be tuned.
         """
         init_size = len(self)
         indices = self.lengths > 11

examples/language-modeling/run_language_modeling.py

@@ -125,13 +125,22 @@ class DataTrainingArguments:
     )
 
 
-def get_dataset(args: DataTrainingArguments, tokenizer: PreTrainedTokenizer, evaluate=False):
+def get_dataset(
+    args: DataTrainingArguments,
+    tokenizer: PreTrainedTokenizer,
+    evaluate: bool = False,
+    cache_dir: Optional[str] = None,
+):
     file_path = args.eval_data_file if evaluate else args.train_data_file
     if args.line_by_line:
         return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)
     else:
         return TextDataset(
-            tokenizer=tokenizer, file_path=file_path, block_size=args.block_size, overwrite_cache=args.overwrite_cache
+            tokenizer=tokenizer,
+            file_path=file_path,
+            block_size=args.block_size,
+            overwrite_cache=args.overwrite_cache,
+            cache_dir=cache_dir,
         )
 
 
@@ -229,8 +238,14 @@ def main():
 
     # Get datasets
 
-    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
-    eval_dataset = get_dataset(data_args, tokenizer=tokenizer, evaluate=True) if training_args.do_eval else None
+    train_dataset = (
+        get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None
+    )
+    eval_dataset = (
+        get_dataset(data_args, tokenizer=tokenizer, evaluate=True, cache_dir=model_args.cache_dir)
+        if training_args.do_eval
+        else None
+    )
     if config.model_type == "xlnet":
         data_collator = DataCollatorForPermutationLanguageModeling(
             tokenizer=tokenizer,

examples/lightning_base.py

@@ -22,6 +22,7 @@ from transformers import (
     PreTrainedTokenizer,
 )
 from transformers.optimization import (
+    Adafactor,
     get_cosine_schedule_with_warmup,
     get_cosine_with_hard_restarts_schedule_with_warmup,
     get_linear_schedule_with_warmup,
@@ -137,7 +138,15 @@ class BaseTransformer(pl.LightningModule):
                 "weight_decay": 0.0,
             },
         ]
-        optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)
+        if self.hparams.adafactor:
+            optimizer = Adafactor(
+                optimizer_grouped_parameters, lr=self.hparams.learning_rate, scale_parameter=False, relative_step=False
+            )
+
+        else:
+            optimizer = AdamW(
+                optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon
+            )
         self.opt = optimizer
 
         scheduler = self.get_lr_scheduler()
@@ -169,10 +178,10 @@ class BaseTransformer(pl.LightningModule):
         return self.train_loader
 
     def val_dataloader(self):
-        return self.get_dataloader("dev", self.hparams.eval_batch_size)
+        return self.get_dataloader("dev", self.hparams.eval_batch_size, shuffle=False)
 
     def test_dataloader(self):
-        return self.get_dataloader("test", self.hparams.eval_batch_size)
+        return self.get_dataloader("test", self.hparams.eval_batch_size, shuffle=False)
 
     def _feature_file(self, mode):
         return os.path.join(
@@ -251,6 +260,7 @@ class BaseTransformer(pl.LightningModule):
         parser.add_argument("--num_train_epochs", dest="max_epochs", default=3, type=int)
         parser.add_argument("--train_batch_size", default=32, type=int)
         parser.add_argument("--eval_batch_size", default=32, type=int)
+        parser.add_argument("--adafactor", action="store_true")
 
 
 class LoggingCallback(pl.Callback):
@@ -356,6 +366,8 @@ def generic_train(
     if args.gpus > 1:
         train_params["distributed_backend"] = "ddp"
 
+    train_params["accumulate_grad_batches"] = args.accumulate_grad_batches
+
     trainer = pl.Trainer.from_argparse_args(
         args,
         weights_summary=None,

examples/longform-qa/README.md

@@ -1,5 +1,5 @@
 # Long Form Question Answering
 
-This folder contains the code for the Long Form Question answering [demo](http://35.226.96.115:8080/) as well as methods to train and use a fully end-to-end Long Form Question Answering system using the [🤗transformers](https://github.com/huggingface/transformers) and [🤗nlp](https://github.com/huggingface/nlp) libraries.
+This folder contains the code for the Long Form Question answering [demo](http://35.226.96.115:8080/) as well as methods to train and use a fully end-to-end Long Form Question Answering system using the [🤗transformers](https://github.com/huggingface/transformers) and [🤗datasets](https://github.com/huggingface/datasets) libraries.
 
 You can use these methods to train your own system by following along the associate [notebook](https://github.com/huggingface/notebooks/blob/master/longform-qa/Long_Form_Question_Answering_with_ELI5_and_Wikipedia.ipynb) or [blog post](https://yjernite.github.io/lfqa.html).

examples/longform-qa/eli5_app.py

@@ -1,5 +1,5 @@
+import datasets
 import faiss
-import nlp
 import numpy as np
 import streamlit as st
 import torch
@@ -45,7 +45,7 @@ def load_models():
 def load_indexes():
     if LOAD_DENSE_INDEX:
         faiss_res = faiss.StandardGpuResources()
-        wiki40b_passages = nlp.load_dataset(path="wiki_snippets", name="wiki40b_en_100_0")["train"]
+        wiki40b_passages = datasets.load_dataset(path="wiki_snippets", name="wiki40b_en_100_0")["train"]
         wiki40b_passage_reps = np.memmap(
             "wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat",
             dtype="float32",
@@ -63,7 +63,7 @@ def load_indexes():
 
 @st.cache(allow_output_mutation=True)
 def load_train_data():
-    eli5 = nlp.load_dataset("eli5", name="LFQA_reddit")
+    eli5 = datasets.load_dataset("eli5", name="LFQA_reddit")
     eli5_train = eli5["train_eli5"]
     eli5_train_q_reps = np.memmap(
         "eli5_questions_reps.dat", dtype="float32", mode="r", shape=(eli5_train.num_rows, 128)

examples/longform-qa/eli5_utils.py

@@ -4,8 +4,8 @@ import os  # noqa: F401
 from random import choice, randint
 from time import time
 
+import datasets  # noqa: F401
 import faiss  # noqa: F401
-import nlp  # noqa: F401
 import numpy as np
 import pandas as pd
 import torch

examples/movement-pruning/emmental/modeling_bert_masked.py

@@ -426,35 +426,35 @@ MASKED_BERT_INPUTS_DOCSTRING = r"""
             :func:`transformers.PreTrainedTokenizer.__call__` for details.
 
             `What are input IDs? <../glossary.html#input-ids>`__
-        attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
+        attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
             Mask to avoid performing attention on padding token indices.
             Mask values selected in ``[0, 1]``:
             ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
 
             `What are attention masks? <../glossary.html#attention-mask>`__
-        token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
+        token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
             Segment token indices to indicate first and second portions of the inputs.
             Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
             corresponds to a `sentence B` token
 
             `What are token type IDs? <../glossary.html#token-type-ids>`_
-        position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
+        position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
             Indices of positions of each input sequence tokens in the position embeddings.
             Selected in the range ``[0, config.max_position_embeddings - 1]``.
 
             `What are position IDs? <../glossary.html#position-ids>`_
-        head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
+        head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
             Mask to nullify selected heads of the self-attention modules.
             Mask values selected in ``[0, 1]``:
             :obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
-        inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
+        inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
             Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
             This is useful if you want more control over how to convert `input_ids` indices into associated vectors
             than the model's internal embedding lookup matrix.
-        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
+        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
             Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
             if the model is configured as a decoder.
-        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
+        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
             Mask to avoid performing attention on the padding token indices of the encoder input. This mask
             is used in the cross-attention if the model is configured as a decoder.
             Mask values selected in ``[0, 1]``:
@@ -684,7 +684,7 @@ class MaskedBertForSequenceClassification(MaskedBertPreTrainedModel):
         threshold=None,
     ):
         r"""
-            labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
+            labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
                 Labels for computing the sequence classification/regression loss.
                 Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
                 If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
@@ -769,7 +769,7 @@ class MaskedBertForMultipleChoice(MaskedBertPreTrainedModel):
         threshold=None,
     ):
         r"""
-            labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
+            labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
                 Labels for computing the multiple choice classification loss.
                 Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension
                 of the input tensors. (see `input_ids` above)
@@ -859,7 +859,7 @@ class MaskedBertForTokenClassification(MaskedBertPreTrainedModel):
         threshold=None,
     ):
         r"""
-            labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
+            labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                 Labels for computing the token classification loss.
                 Indices should be in ``[0, ..., config.num_labels - 1]``.
             threshold (:obj:`float`):
@@ -946,11 +946,11 @@ class MaskedBertForQuestionAnswering(MaskedBertPreTrainedModel):
         threshold=None,
     ):
         r"""
-            start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
+            start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
                 Labels for position (index) of the start of the labelled span for computing the token classification loss.
                 Positions are clamped to the length of the sequence (`sequence_length`).
                 Position outside of the sequence are not taken into account for computing the loss.
-            end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
+            end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
                 Labels for position (index) of the end of the labelled span for computing the token classification loss.
                 Positions are clamped to the length of the sequence (`sequence_length`).
                 Position outside of the sequence are not taken into account for computing the loss.

examples/question-answering/run_squad.py

@@ -187,7 +187,7 @@ def train(args, train_dataset, model, tokenizer):
                 "end_positions": batch[4],
             }
 
-            if args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
+            if args.model_type in ["xlm", "roberta", "distilbert", "camembert", "bart"]:
                 del inputs["token_type_ids"]
 
             if args.model_type in ["xlnet", "xlm"]:
@@ -300,7 +300,7 @@ def evaluate(args, model, tokenizer, prefix=""):
                 "token_type_ids": batch[2],
             }
 
-            if args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
+            if args.model_type in ["xlm", "roberta", "distilbert", "camembert", "bart"]:
                 del inputs["token_type_ids"]
 
             feature_indices = batch[3]

examples/rag/README.md

@@ -0,0 +1,85 @@
+# Intro
+RAG (for Retrieval Augmented Generation) is a seq2seq model which encapsulates two core components: a question encoder and a generator. During a forward pass, we encode the input with the question encoder and pass it
+to the retriever to extract relevant context documents. The documents are then prepended to the input. Such contextualized input is passed to the generator. See [the paper](https://arxiv.org/pdf/2005.11401.pdf) for mored details.
+
+We implement two variants of the model, both presented in the paper - `RagSequenceForGeneration. and `RagTokenForGeneration`. In both cases we use `DPRQuestionEncoder` as the question encoder. As for the generator, two compatible architectures have been tested: `BartForConditionalGeneration`  and `T5ForConditionalGeneration`.
+
+Key files:
+- `modeling_rag.py`, `tokenization_rag.py`, `configuration_rag.py` the core model implementation
+- `retrieval_rag.py` - a distributed retriever built on top of the `torch.distributed` communication package. The retriever is an interface between the model and the faiss index of the encoded documents. During training, all workers initialize their own instance of the retriever, however, only the main worker loads the index into memory, which prevents OOMs on machines with multiple GPUs (we store the index in RAM). The index itself is based on the `nlp.Datasets`. We also implement a variant compatible with indices built using the original DPR implementation (https://github.com/facebookresearch/DPR)
+- `eval_rag.py` - an evaluation script which allows to perform the evaluation end to end (measures the exact match and F1 on the downstream task) as well as the evaluation of the retrieval component alone (measures precision@k).
+- `finetune.py` - a training script for finetuning RAG models.
+
+
+# Finetuning
+Our finetuning logic is based on scripts from [`examples/seq2seq`](https://github.com/huggingface/transformers/tree/master/examples/seq2seq).
+Follow instructions there regarding data preprocessing. A sample finetuning command:
+
+```
+python examples/rag/finetune.py \
+    --data_dir $DATA_DIR \
+    --output_dir $OUTPUT_DIR \
+    --model_name_or_path $MODEL_NAME_OR_PATH \
+    --model_type rag_sequence \
+    --fp16 \
+    --gpus 8
+```
+
+
+# Evaluation
+Apart for parameters specifying the model that's being evaluated and some extra parameters, the evaluation script expects paths to two files:
+- `evaluation_set` - a path file specifying the input dataset for evaluation, a single datapoint per line, e.g.
+```who is the owner of reading football club```
+- `gold_data_path` - a path to a file contaning ground truth answers for samples from the `evaluation_set`.
+
+We expect the following formats of the gold data file:
+
+- for e2e evaluation, we support two formats of gold files:
+    - `qa` - where a single line in the following format: input [tab] output_list, e.g.:
+    ```
+    who is the owner of reading football club	['Xiu Li Dai', 'Dai Yongge', 'Dai Xiuli', 'Yongge Dai']
+    ```
+    - `ans` - where a single line of the gold file contains the expected output string,
+    ```
+    Xiu Li Dai
+    ```
+
+- for retrieval evaluation, we expect a tab-separated list of Wikipedia page titles constituting positive contexts for a given query, e.g. given a question `who sings does he love me with reba`, a line with ground truth retrieval data could look as follows:
+```
+Does He Love You	Does He Love You	Red Sandy Spika dress of Reba McEntire	Greatest Hits Volume Two (Reba McEntire album)	Shoot for the Moon (album)
+```
+
+## Retrieval evaluation
+
+We demonstrate how to evaluate retrieval against DPR evaluation data. You can download respective files from links listed [here](https://github.com/facebookresearch/DPR/blob/master/data/download_data.py#L39-L45).
+
+1. Download and unzip the gold data file. We use the `biencoder-nq-dev` from https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz.
+2. Parse the unziped file using the `parse_dpr_relevance_data.py`
+```
+python examples/rag/parse_dpr_relevance_data.py --src_path path/to/unziped/biencoder-nq-dev.json --evaluation_set path/to/output/biencoder-nq-dev.questions --gold_data_path path/to/output/biencoder-nq-dev.pages
+```
+3. Run evaluation:
+```
+python examples/rag/eval_rag.py \
+    --model_name_or_path $MODEL_NAME_OR_PATH \ # model name or path of the model we're evaluating
+    --model_type rag_sequence \ # RAG model type (rag_token or rag_sequence)
+    --evaluation_set path/to/output/biencoder-nq-dev.questions \ # an input dataset for evaluation
+    --gold_data_path path/to/output/biencoder-nq-dev.pages \ # a dataset containing ground truth answers for samples from the evaluation_set
+    --predictions_filename retrieval_preds.tsv  \ # name of file in which predictions will be stored
+    --eval_mode retrieval  \ # indicates whether we're performing retrieval evaluation or e2e evaluation
+    --recalculate # if predictions_filename already exists, and this option is set - we regenerate the answers, otherwise we reuse the predicsion file to calculate metrics.
+```
+
+
+## End-to-end evaluation
+```
+python examples/rag/eval_rag.py \
+    --model_name_or_path /private/home/piktus/rag_huggingface/data/repro-rag-sequence-63/ \
+    --model_type rag_sequence \
+    --evaluation_set path/to/test.source \
+    --gold_data_path path/to/gold_data \
+    --predictions_filename e2e_preds.txt \
+    --eval_mode e2e  \ # indicates whether we're performing retrieval evaluation or e2e evaluation (default)
+    --n_docs 5 \ # You can experiment with retrieving different number of documents at evaluation time
+    --print_predictions
+```

examples/rag/callbacks.py

@@ -0,0 +1,30 @@
+import logging
+import os
+
+from pytorch_lightning.callbacks import ModelCheckpoint
+
+
+logger = logging.getLogger(__name__)
+
+
+def get_checkpoint_callback(output_dir, metric):
+    """Saves the best model by validation ROUGE2 score."""
+    if metric == "rouge2":
+        exp = "{val_avg_rouge2:.4f}-{step_count}"
+    elif metric == "bleu":
+        exp = "{val_avg_bleu:.4f}-{step_count}"
+    elif metric == "em":
+        exp = "{val_avg_em:.4f}-{step_count}"
+    else:
+        raise NotImplementedError(
+            f"seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this function."
+        )
+
+    checkpoint_callback = ModelCheckpoint(
+        filepath=os.path.join(output_dir, exp),
+        monitor=f"val_{metric}",
+        mode="max",
+        save_top_k=10,
+        period=0,  # maybe save a checkpoint every time val is run, not just end of epoch.
+    )
+    return checkpoint_callback

examples/rag/eval_rag.py

@@ -0,0 +1,317 @@
+""" Evaluation script for RAG models."""
+
+import argparse
+import ast
+import logging
+import os
+import sys
+
+import pandas as pd
+import torch
+from tqdm import tqdm
+
+from transformers import (
+    BartForConditionalGeneration,
+    BartTokenizer,
+    RagRetriever,
+    RagSequenceForGeneration,
+    RagTokenForGeneration,
+)
+from transformers import logging as transformers_logging
+
+
+sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
+from examples.rag.utils import exact_match_score, f1_score  # noqa: E402 # isort:skip
+
+
+logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO)
+
+transformers_logging.set_verbosity_info()
+
+
+def infer_model_type(model_name_or_path):
+    if "token" in model_name_or_path:
+        return "rag_token"
+    if "sequence" in model_name_or_path:
+        return "rag_sequence"
+    if "bart" in model_name_or_path:
+        return "bart"
+    return None
+
+
+def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
+    scores_for_ground_truths = []
+    for ground_truth in ground_truths:
+        score = metric_fn(prediction, ground_truth)
+        scores_for_ground_truths.append(score)
+    return max(scores_for_ground_truths)
+
+
+def get_scores(args, preds_path, gold_data_path):
+    hypos = [line.strip() for line in open(preds_path, "r").readlines()]
+    answers = []
+
+    if args.gold_data_mode == "qa":
+        data = pd.read_csv(gold_data_path, sep="\t", header=None)
+        for answer_list in data[1]:
+            ground_truths = ast.literal_eval(answer_list)
+            answers.append(ground_truths)
+    else:
+        references = [line.strip() for line in open(gold_data_path, "r").readlines()]
+        answers = [[reference] for reference in references]
+
+    f1 = em = total = 0
+    for prediction, ground_truths in zip(hypos, answers):
+        total += 1
+        em += metric_max_over_ground_truths(exact_match_score, prediction, ground_truths)
+        f1 += metric_max_over_ground_truths(f1_score, prediction, ground_truths)
+
+    em = 100.0 * em / total
+    f1 = 100.0 * f1 / total
+
+    logger.info("F1: {}".format(f1))
+    logger.info("EM: {}".format(em))
+
+
+def get_precision_at_k(args, preds_path, gold_data_path):
+    k = args.k
+    hypos = [line.strip() for line in open(preds_path, "r").readlines()]
+    references = [line.strip() for line in open(gold_data_path, "r").readlines()]
+
+    em = total = 0
+    for hypo, reference in zip(hypos, references):
+        hypo_provenance = set(hypo.split("\t")[:k])
+        ref_provenance = set(reference.split("\t")[1 : (k + 1)])
+        total += 1
+        em += len(hypo_provenance & ref_provenance) / k
+
+    em = 100.0 * em / total
+    logger.info("Precision@{}: {}".format(k, em))
+
+
+def evaluate_batch_retrieval(args, rag_model, tokenizer, retriever, questions):
+    def strip_title(title):
+        if title.startswith('"'):
+            title = title[1:]
+        if title.endswith('"'):
+            title = title[:-1]
+        return title
+
+    retriever_inputs = tokenizer.batch_encode_plus(
+        questions,
+        return_tensors="pt",
+        padding=True,
+        truncation=True,
+    )
+    retriever_input_embs = rag_model.model.question_encoder(retriever_inputs["input_ids"].to(args.device))[0]
+
+    _, all_docs = retriever.retrieve(retriever_input_embs.numpy(), rag_model.config.n_docs)
+
+    provenance_strings = []
+    for docs in all_docs:
+        provenance = [strip_title(title) for title in docs["title"]]
+        provenance_strings.append("\t".join(provenance))
+    return provenance_strings
+
+
+def evaluate_batch_e2e(args, rag_model, tokenizer, retriever, questions):
+    with torch.no_grad():
+        input_ids = tokenizer.batch_encode_plus(questions, return_tensors="pt", padding=True, truncation=True)[
+            "input_ids"
+        ].to(args.device)
+        outputs = rag_model.generate(
+            input_ids,
+            retriever=retriever,
+            num_beams=args.num_beams,
+            min_length=args.min_length,
+            max_length=args.max_length,
+            early_stopping=False,
+            num_return_sequences=1,
+            bad_words_ids=[[0, 0]],  # BART likes to repeat BOS tokens, dont allow it to generate more than one
+            clean_up_tokenization=True,
+            print_docs=args.print_docs,
+        )
+        answers = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+        if args.print_predictions:
+            for q, a in zip(questions, answers):
+                logger.info("Q: {} - A: {}".format(q, a))
+
+        return answers
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_type",
+        choices=["rag_sequence", "rag_token", "bart"],
+        type=str,
+        help="RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the model_name_or_path",
+    )
+    parser.add_argument(
+        "--retriever_type",
+        default=None,
+        choices=["hf_retriever", "legacy_retriever"],
+        type=str,
+        help="RAG model retriever type",
+    )
+    parser.add_argument(
+        "--index_path",
+        default=None,
+        type=str,
+        help="Path to the retrieval index",
+    )
+    parser.add_argument("--n_docs", default=5, type=int, help="Number of retrieved docs")
+    parser.add_argument(
+        "--model_name_or_path",
+        default=None,
+        type=str,
+        required=True,
+        help="Path to pretrained checkpoints or model identifier from huggingface.co/models",
+    )
+    parser.add_argument(
+        "--eval_mode",
+        choices=["e2e", "retrieval"],
+        default="e2e",
+        type=str,
+        help="Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calulates precision@k.",
+    )
+    parser.add_argument("--k", default=1, type=int, help="k for the precision@k calculation")
+    parser.add_argument(
+        "--evaluation_set",
+        default=None,
+        type=str,
+        required=True,
+        help="Path to a file containing evaluation samples",
+    )
+    parser.add_argument(
+        "--gold_data_path",
+        default=None,
+        type=str,
+        required=True,
+        help="Path to a tab-separated file with gold samples",
+    )
+    parser.add_argument(
+        "--gold_data_mode",
+        default="qa",
+        type=str,
+        choices=["qa", "ans"],
+        help="Format of the gold data file"
+        "qa - a single line in the following format: question [tab] answer_list"
+        "ans - a single line of the gold file contains the expected answer string",
+    )
+    parser.add_argument(
+        "--predictions_filename",
+        type=str,
+        default="predictions.txt",
+        help="Name of the predictions file, to be stored in the checkpoints directry",
+    )
+    parser.add_argument(
+        "--eval_all_checkpoints",
+        action="store_true",
+        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
+    )
+    parser.add_argument(
+        "--eval_batch_size",
+        default=8,
+        type=int,
+        help="Batch size per GPU/CPU for evaluation.",
+    )
+    parser.add_argument(
+        "--recalculate",
+        help="Recalculate predictions even if the prediction file exists",
+        action="store_true",
+    )
+    parser.add_argument(
+        "--num_beams",
+        default=4,
+        type=int,
+        help="Number of beams to be used when generating answers",
+    )
+    parser.add_argument("--min_length", default=1, type=int, help="Min length of the generated answers")
+    parser.add_argument("--max_length", default=50, type=int, help="Max length of the generated answers")
+
+    parser.add_argument(
+        "--print_predictions",
+        action="store_true",
+        help="If True, prints predictions while evaluating.",
+    )
+    parser.add_argument(
+        "--print_docs",
+        action="store_true",
+        help="If True, prints docs retried while generating.",
+    )
+    args = parser.parse_args()
+    args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    return args
+
+
+def main(args):
+    model_kwargs = {}
+    if args.model_type is None:
+        args.model_type = infer_model_type(args.model_name_or_path)
+        assert args.model_type is not None
+    if args.model_type.startswith("rag"):
+        model_class = RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
+        model_kwargs["n_docs"] = args.n_docs
+        if args.retriever_type is not None:
+            model_kwargs["retriever_type"] = args.retriever_type
+        if args.index_path is not None:
+            model_kwargs["index_path"] = args.index_path
+    else:
+        model_class = BartForConditionalGeneration
+
+    checkpoints = (
+        [f.path for f in os.scandir(args.model_name_or_path) if f.is_dir()]
+        if args.eval_all_checkpoints
+        else [args.model_name_or_path]
+    )
+
+    logger.info("Evaluate the following checkpoints: %s", checkpoints)
+
+    score_fn = get_scores if args.eval_mode == "e2e" else get_precision_at_k
+    evaluate_batch_fn = evaluate_batch_e2e if args.eval_mode == "e2e" else evaluate_batch_retrieval
+
+    for checkpoint in checkpoints:
+        predictions_path = os.path.join(checkpoint, args.predictions_filename)
+        if os.path.exists(predictions_path) and (not args.recalculate):
+            logger.info("Calculating metrics based on an existing predictions file: {}".format(predictions_path))
+            score_fn(args, predictions_path, args.gold_data_path)
+            continue
+
+        logger.info("***** Running evaluation for {} *****".format(checkpoint))
+        logger.info("  Batch size = %d", args.eval_batch_size)
+        logger.info("  Predictions will be stored under {}".format(predictions_path))
+
+        model = model_class.from_pretrained(checkpoint, **model_kwargs)
+        model.to(args.device)
+        retriever = RagRetriever(model.config)  # TODO: add tokenizers
+        tokenizer = (
+            retriever.generator_tokenizer
+            if args.model_type != "bart" and args.eval_mode == "e2e"
+            else retriever.question_encoder_tokenizer
+            if args.model_type != "bart" and args.eval_mode == "retrieval"
+            else BartTokenizer.from_pretrained("facebook/bart-large")
+        )
+
+        with open(args.evaluation_set, "r") as eval_file, open(predictions_path, "w") as preds_file:
+            questions = []
+            for line in tqdm(eval_file):
+                questions.append(line.strip())
+                if len(questions) == args.eval_batch_size:
+                    answers = evaluate_batch_fn(args, model, tokenizer, retriever, questions)
+                    preds_file.write("\n".join(answers) + "\n")
+                    preds_file.flush()
+                    questions = []
+            if len(questions) > 0:
+                answers = evaluate_batch_fn(args, model, tokenizer, retriever, questions)
+                preds_file.write("\n".join(answers))
+                preds_file.flush()
+
+            score_fn(args, predictions_path, args.gold_data_path)
+
+
+if __name__ == "__main__":
+    args = get_args()
+    main(args)

examples/rag/finetune.py

@@ -0,0 +1,476 @@
+"""Finetuning script for RAG models. Adapted from examples.seq2seq.finetune.py"""
+
+import argparse
+import glob
+import logging
+import os
+import sys
+import time
+import warnings
+from collections import defaultdict
+from pathlib import Path
+from typing import Any, Dict, List, Tuple
+
+import numpy as np
+import pytorch_lightning as pl
+import torch
+import torch.distributed as dist
+from torch.utils.data import DataLoader
+
+from transformers import (
+    AutoConfig,
+    AutoTokenizer,
+    BartForConditionalGeneration,
+    RagConfig,
+    RagPyTorchDistributedRetriever,
+    RagSequenceForGeneration,
+    RagTokenForGeneration,
+    T5ForConditionalGeneration,
+    get_linear_schedule_with_warmup,
+)
+from transformers import logging as transformers_logging
+
+
+sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip
+
+from examples.lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa: E402 # isort:skip
+from examples.rag.callbacks import get_checkpoint_callback  # noqa: E402 # isort:skip
+from examples.rag.utils import (  # noqa: E402 # isort:skip
+    Seq2SeqDataset,
+    calculate_exact_match,
+    is_rag_model,
+    set_extra_model_params,
+)
+from examples.seq2seq.callbacks import Seq2SeqLoggingCallback, get_early_stopping_callback  # noqa: E402 # isort:skip
+from examples.seq2seq.utils import (  # noqa: E402 # isort:skip
+    flatten_list,
+    get_git_info,
+    lmap,
+    pickle_save,
+    save_git_info,
+    save_json,
+)
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+transformers_logging.set_verbosity_info()
+
+
+class AttrDict(dict):
+    def __init__(self, *args, **kwargs):
+        super(AttrDict, self).__init__(*args, **kwargs)
+        self.__dict__ = self
+
+
+class GenerativeQAModule(BaseTransformer):
+    mode = "generative_qa"
+    loss_names = ["loss"]
+    metric_names = ["em"]
+    val_metric = "em"
+
+    def __init__(self, hparams, **kwargs):
+        # when loading from a pytorch lightning checkpoint, hparams are passed as dict
+        if isinstance(hparams, dict):
+            hparams = AttrDict(hparams)
+        if hparams.model_type == "rag_sequence":
+            self.model_class = RagSequenceForGeneration
+        elif hparams.model_type == "rag_token":
+            self.model_class = RagTokenForGeneration
+        elif hparams.model_type == "bart":
+            self.model_class = BartForConditionalGeneration
+        else:
+            self.model_class = T5ForConditionalGeneration
+        self.is_rag_model = is_rag_model(hparams.model_type)
+
+        config_class = RagConfig if self.is_rag_model else AutoConfig
+        config = config_class.from_pretrained(hparams.model_name_or_path)
+
+        # set extra_model_params for generator configs and load_model
+        extra_model_params = ("encoder_layerdrop", "decoder_layerdrop", "attention_dropout", "dropout")
+        if self.is_rag_model:
+            generator_config = AutoConfig.from_pretrained(
+                config.pretrained_generator_name_or_path, prefix=config.prefix
+            )
+            hparams, generator_config = set_extra_model_params(extra_model_params, hparams, generator_config)
+            model = self.model_class.from_pretrained(
+                hparams.model_name_or_path, config=config, generator_config=generator_config
+            )
+        else:
+            if args.prefix is not None:
+                setattr(config, "prefix", args.prefix)
+            hparams, config = set_extra_model_params(extra_model_params, hparams, config)
+            model = self.model_class.from_pretrained(hparams.model_name_or_path, config=config)
+            generator_config = config
+
+        tokenizer = (
+            AutoTokenizer.from_pretrained(config.pretrained_generator_tokenizer_name_or_path)
+            if self.is_rag_model
+            else AutoTokenizer.from_pretrained(hparams.model_name_or_path)
+        )
+
+        super().__init__(hparams, config=config, tokenizer=tokenizer, model=model)
+
+        self.retriever = (
+            RagPyTorchDistributedRetriever(self.model.config) if self.is_rag_model else None
+        )  # TODO add tokenizers
+
+        save_git_info(self.hparams.output_dir)
+        self.output_dir = Path(self.hparams.output_dir)
+        self.metrics_save_path = Path(self.output_dir) / "metrics.json"
+        self.hparams_save_path = Path(self.output_dir) / "hparams.pkl"
+        pickle_save(self.hparams, self.hparams_save_path)
+        self.step_count = 0
+        self.metrics = defaultdict(list)
+
+        self.dataset_kwargs: dict = dict(
+            data_dir=self.hparams.data_dir,
+            max_source_length=self.hparams.max_source_length,
+            prefix=generator_config.prefix or "",
+        )
+        n_observations_per_split = {
+            "train": self.hparams.n_train,
+            "val": self.hparams.n_val,
+            "test": self.hparams.n_test,
+        }
+        self.n_obs = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
+
+        self.target_lens = {
+            "train": self.hparams.max_target_length,
+            "val": self.hparams.val_max_target_length,
+            "test": self.hparams.test_max_target_length,
+        }
+        assert self.target_lens["train"] <= self.target_lens["val"], f"target_lens: {self.target_lens}"
+        assert self.target_lens["train"] <= self.target_lens["test"], f"target_lens: {self.target_lens}"
+
+        self.hparams.git_sha = get_git_info()["repo_sha"]
+        self.num_workers = hparams.num_workers
+        self.distributed_port = self.hparams.distributed_port
+
+    def init_ddp_connection(self, global_rank: int, world_size: int, is_slurm_managing_tasks: bool = True):
+        logger.info("Custom init_ddp_connection.")
+        os.environ["MASTER_PORT"] = str(self.distributed_port)
+        super().init_ddp_connection(global_rank, world_size, is_slurm_managing_tasks)
+        if self.is_rag_model:
+            self.retriever.init_retrieval(self.distributed_port)
+
+    def forward(self, input_ids, **kwargs):
+        return self.model(input_ids, **kwargs)
+
+    def ids_to_clean_text(self, generated_ids: List[int]):
+        gen_text = self.tokenizer.batch_decode(
+            generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
+        )
+        return lmap(str.strip, gen_text)
+
+    def _step(self, batch: dict) -> Tuple:
+        source_ids, source_mask, target_ids = batch["input_ids"], batch["attention_mask"], batch["decoder_input_ids"]
+
+        if isinstance(self.model, T5ForConditionalGeneration):
+            decoder_input_ids = self.model._shift_right(target_ids)
+            lm_labels = target_ids
+        elif isinstance(self.model, BartForConditionalGeneration):
+            decoder_input_ids = target_ids[:, :-1].contiguous()
+            lm_labels = target_ids[:, 1:].clone()
+        else:
+            assert self.is_rag_model
+            generator = self.model.model.generator
+            if isinstance(generator, T5ForConditionalGeneration):
+                decoder_start_token_id = generator.config.decoder_start_token_id
+                decoder_input_ids = (
+                    torch.cat(
+                        [torch.Tensor([[decoder_start_token_id]] * target_ids.shape[0]).to(target_ids), target_ids],
+                        dim=1,
+                    )
+                    if target_ids.shape[0] < self.target_lens["train"]
+                    else generator._shift_right(target_ids)
+                )
+            elif isinstance(generator, BartForConditionalGeneration):
+                decoder_input_ids = target_ids
+            lm_labels = None
+
+        assert decoder_input_ids is not None
+
+        if lm_labels is not None:
+            outputs = self(
+                source_ids,
+                attention_mask=source_mask,
+                decoder_input_ids=decoder_input_ids,
+                use_cache=False,
+                labels=lm_labels,
+                return_dict=True,
+            )
+        else:  # RAG models
+            outputs = self(
+                source_ids,
+                retriever=self.retriever,
+                attention_mask=source_mask,
+                decoder_input_ids=decoder_input_ids,
+                use_cache=False,
+                return_loss=True,
+                reduce=True,
+                label_smoothing=self.hparams.label_smoothing,
+            )
+
+        loss = outputs["loss"]
+        return (loss,)
+
+    @property
+    def pad(self) -> int:
+        return self.tokenizer.pad_token_id
+
+    def training_step(self, batch, batch_idx) -> Dict:
+        loss_tensors = self._step(batch)
+
+        logs = {name: loss for name, loss in zip(self.loss_names, loss_tensors)}
+        # tokens per batch
+        logs["tpb"] = batch["input_ids"].ne(self.pad).sum() + batch["decoder_input_ids"].ne(self.pad).sum()
+
+        return {"loss": loss_tensors[0], "log": logs}
+
+    def validation_step(self, batch, batch_idx) -> Dict:
+        return self._generative_step(batch)
+
+    def validation_epoch_end(self, outputs, prefix="val") -> Dict:
+        self.step_count += 1
+        losses = {k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
+        loss = losses["loss"]
+        gen_metrics = {
+            k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ["gen_time", "gen_len"]
+        }
+        metrics_tensor: torch.FloatTensor = torch.tensor(gen_metrics[self.val_metric]).type_as(loss)
+        gen_metrics.update({k: v.item() for k, v in losses.items()})
+
+        # fix for https://github.com/PyTorchLightning/pytorch-lightning/issues/2424
+        if dist.is_initialized():
+            dist.all_reduce(metrics_tensor, op=dist.ReduceOp.SUM)
+            metrics_tensor = metrics_tensor / dist.get_world_size()
+            gen_metrics.update({self.val_metric: metrics_tensor.item()})
+
+        losses.update(gen_metrics)
+        metrics = {f"{prefix}_avg_{k}": x for k, x in losses.items()}
+        metrics["step_count"] = self.step_count
+        self.save_metrics(metrics, prefix)  # writes to self.metrics_save_path
+        preds = flatten_list([x["preds"] for x in outputs])
+        return {"log": metrics, "preds": preds, f"{prefix}_loss": loss, f"{prefix}_{self.val_metric}": metrics_tensor}
+
+    def save_metrics(self, latest_metrics, type_path) -> None:
+        self.metrics[type_path].append(latest_metrics)
+        save_json(self.metrics, self.metrics_save_path)
+
+    def calc_generative_metrics(self, preds, target) -> Dict:
+        return calculate_exact_match(preds, target)
+
+    def _generative_step(self, batch: dict) -> dict:
+        start_time = time.time()
+        generated_ids = self.model.generate(
+            batch["input_ids"],
+            retriever=self.retriever,
+            dedup=False,  # rag specific parameter
+            attention_mask=batch["attention_mask"],
+            use_cache=True,
+            min_length=1,
+            max_length=self.target_lens["val"],
+        )
+
+        gen_time = (time.time() - start_time) / batch["input_ids"].shape[0]
+        preds: List[str] = self.ids_to_clean_text(generated_ids)
+        target: List[str] = self.ids_to_clean_text(batch["decoder_input_ids"])
+        loss_tensors = self._step(batch)
+        base_metrics = {name: loss for name, loss in zip(self.loss_names, loss_tensors)}
+        gen_metrics: Dict = self.calc_generative_metrics(preds, target)
+
+        summ_len = np.mean(lmap(len, generated_ids))
+        base_metrics.update(gen_time=gen_time, gen_len=summ_len, preds=preds, target=target, **gen_metrics)
+        return base_metrics
+
+    def test_step(self, batch, batch_idx):
+        return self._generative_step(batch)
+
+    def test_epoch_end(self, outputs):
+        return self.validation_epoch_end(outputs, prefix="test")
+
+    def get_dataset(self, type_path) -> Seq2SeqDataset:
+        n_obs = self.n_obs[type_path]
+        max_target_length = self.target_lens[type_path]
+        dataset = Seq2SeqDataset(
+            self.tokenizer,
+            type_path=type_path,
+            n_obs=n_obs,
+            max_target_length=max_target_length,
+            **self.dataset_kwargs,
+        )
+        return dataset
+
+    def get_dataloader(self, type_path: str, batch_size: int, shuffle: bool = False) -> DataLoader:
+        dataset = self.get_dataset(type_path)
+        sampler = None
+        if self.hparams.sortish_sampler and type_path == "train":
+            assert self.hparams.gpus <= 1  # TODO: assert earlier
+            sampler = dataset.make_sortish_sampler(batch_size)
+            shuffle = False
+
+        dataloader = DataLoader(
+            dataset,
+            batch_size=batch_size,
+            collate_fn=dataset.collate_fn,
+            shuffle=shuffle,
+            num_workers=self.num_workers,
+            sampler=sampler,
+        )
+        return dataloader
+
+    def train_dataloader(self) -> DataLoader:
+        dataloader = self.get_dataloader("train", batch_size=self.hparams.train_batch_size, shuffle=True)
+        t_total = (
+            (len(dataloader.dataset) // (self.hparams.train_batch_size * max(1, self.hparams.gpus)))
+            // self.hparams.accumulate_grad_batches
+            * float(self.hparams.max_epochs)
+        )
+        scheduler = get_linear_schedule_with_warmup(
+            self.opt, num_warmup_steps=self.hparams.warmup_steps, num_training_steps=t_total
+        )
+        if max(scheduler.get_last_lr()) > 0:
+            warnings.warn("All learning rates are 0")
+        self.lr_scheduler = scheduler
+        return dataloader
+
+    def val_dataloader(self) -> DataLoader:
+        return self.get_dataloader("val", batch_size=self.hparams.eval_batch_size)
+
+    def test_dataloader(self) -> DataLoader:
+        return self.get_dataloader("test", batch_size=self.hparams.eval_batch_size)
+
+    @pl.utilities.rank_zero_only
+    def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
+        save_path = self.output_dir.joinpath("checkpoint{}".format(self.step_count))
+        self.model.config.save_step = self.step_count
+        self.model.save_pretrained(save_path)
+        self.tokenizer.save_pretrained(save_path)
+
+    @staticmethod
+    def add_model_specific_args(parser, root_dir):
+        BaseTransformer.add_model_specific_args(parser, root_dir)
+        add_generic_args(parser, root_dir)
+        parser.add_argument(
+            "--max_source_length",
+            default=128,
+            type=int,
+            help="The maximum total input sequence length after tokenization. Sequences longer "
+            "than this will be truncated, sequences shorter will be padded.",
+        )
+        parser.add_argument(
+            "--max_target_length",
+            default=25,
+            type=int,
+            help="The maximum total input sequence length after tokenization. Sequences longer "
+            "than this will be truncated, sequences shorter will be padded.",
+        )
+        parser.add_argument(
+            "--val_max_target_length",
+            default=25,
+            type=int,
+            help="The maximum total input sequence length after tokenization. Sequences longer "
+            "than this will be truncated, sequences shorter will be padded.",
+        )
+        parser.add_argument(
+            "--test_max_target_length",
+            default=25,
+            type=int,
+            help="The maximum total input sequence length after tokenization. Sequences longer "
+            "than this will be truncated, sequences shorter will be padded.",
+        )
+        parser.add_argument("--sortish_sampler", action="store_true", default=False)
+        parser.add_argument("--logger_name", type=str, choices=["default", "wandb", "wandb_shared"], default="default")
+        parser.add_argument("--n_train", type=int, default=-1, required=False, help="# examples. -1 means use all.")
+        parser.add_argument("--n_val", type=int, default=-1, required=False, help="# examples. -1 means use all.")
+        parser.add_argument("--n_test", type=int, default=-1, required=False, help="# examples. -1 means use all.")
+        parser.add_argument("--label_smoothing", type=float, default=0.0, required=False)
+        parser.add_argument(
+            "--prefix",
+            type=str,
+            default=None,
+            help="Prefix added at the beginning of each text, typically used with T5-based models.",
+        )
+        parser.add_argument(
+            "--early_stopping_patience",
+            type=int,
+            default=-1,
+            required=False,
+            help="-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So val_check_interval will effect it.",
+        )
+        parser.add_argument(
+            "--distributed-port", type=int, default=-1, required=False, help="Port number for distributed training."
+        )
+        parser.add_argument(
+            "--model_type",
+            choices=["rag_sequence", "rag_token", "bart", "t5"],
+            type=str,
+            help="RAG model type: sequence or token, if none specified, the type is inferred from the model_name_or_path",
+        )
+        return parser
+
+
+def main(args, model=None) -> GenerativeQAModule:
+    Path(args.output_dir).mkdir(exist_ok=True)
+    if model is None:
+        model: GenerativeQAModule = GenerativeQAModule(args)
+
+    dataset = Path(args.data_dir).name
+    if (
+        args.logger_name == "default"
+        or args.fast_dev_run
+        or str(args.output_dir).startswith("/tmp")
+        or str(args.output_dir).startswith("/var")
+    ):
+        logger = True  # don't pollute wandb logs unnecessarily
+    elif args.logger_name == "wandb":
+        from pytorch_lightning.loggers import WandbLogger
+
+        project = os.environ.get("WANDB_PROJECT", dataset)
+        logger = WandbLogger(name=model.output_dir.name, project=project)
+
+    elif args.logger_name == "wandb_shared":
+        from pytorch_lightning.loggers import WandbLogger
+
+        logger = WandbLogger(name=model.output_dir.name, project=f"hf_{dataset}")
+
+    es_callback = (
+        get_early_stopping_callback(model.val_metric, args.early_stopping_patience)
+        if args.early_stopping_patience >= 0
+        else False
+    )
+    trainer: pl.Trainer = generic_train(
+        model,
+        args,
+        logging_callback=Seq2SeqLoggingCallback(),
+        checkpoint_callback=get_checkpoint_callback(args.output_dir, model.val_metric),
+        early_stopping_callback=es_callback,
+        logger=logger,
+    )
+    pickle_save(model.hparams, model.output_dir / "hparams.pkl")
+
+    if not args.do_predict:
+        return model
+
+    model.hparams.test_checkpoint = ""
+    checkpoints = list(sorted(glob.glob(os.path.join(args.output_dir, "*.ckpt"), recursive=True)))
+    if checkpoints:
+        model.hparams.test_checkpoint = checkpoints[-1]
+        trainer.resume_from_checkpoint = checkpoints[-1]  # best checkpoint
+    trainer.logger.log_hyperparams(model.hparams)
+
+    # test() without a model tests using the best checkpoint automatically
+    trainer.test()
+
+    return model
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser = pl.Trainer.add_argparse_args(parser)
+    parser = GenerativeQAModule.add_model_specific_args(parser, os.getcwd())
+
+    args = parser.parse_args()
+
+    main(args)

examples/rag/finetune.sh

@@ -0,0 +1,34 @@
+# Add parent directory to python path to access lightning_base.py
+export PYTHONPATH="../":"${PYTHONPATH}"
+
+# A sample finetuning run, you need to specify data_dir, output_dir and model_name_or_path
+# run ./examples/rag/finetune.sh --help to see all the possible options
+
+python examples/rag/finetune.py \
+    --data_dir $DATA_DIR \
+    --output_dir $OUTPUT_DIR \
+    --model_name_or_path $MODLE_NAME_OR_PATH \
+    --model_type rag_sequence \
+    --fp16 \
+    --gpus 8 \
+    --do_train \
+    --do_predict \
+    --n_val -1 \
+    --val_check_interval 0.25 \
+    --train_batch_size 8 \
+    --eval_batch_size 1 \
+    --max_source_length 128 \
+    --max_target_length 25 \
+    --val_max_target_length 25 \
+    --test_max_target_length 25 \
+    --label_smoothing 0.1 \
+    --dropout 0.1 \
+    --attention_dropout 0.1 \
+    --weight_decay 0.001 \
+    --adam_epsilon 1e-08 \
+    --max_grad_norm 0.1 \
+    --lr_scheduler polynomial \
+    --learning_rate 3e-05 \
+    --num_train_epochs 100 \
+    --warmup_steps 500 \
+    --gradient_accumulation_steps 1

examples/rag/parse_dpr_relevance_data.py

@@ -0,0 +1,47 @@
+"""
+This script reads DPR retriever training data and parses each datapoint. We save a line per datapoint.
+Each line consists of the query followed by a tab-separated list of Wikipedia page titles constituting
+positive contexts for a given query.
+"""
+
+import argparse
+import json
+
+from tqdm import tqdm
+
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    # Required parameters
+    parser.add_argument(
+        "--src_path",
+        type=str,
+        default="biencoder-nq-dev.json",
+        help="Path to raw DPR training data",
+    )
+    parser.add_argument(
+        "--evaluation_set",
+        type=str,
+        help="where to store parsed evaluation_set file",
+    )
+    parser.add_argument(
+        "--gold_data_path",
+        type=str,
+        help="where to store parsed gold_data_path file",
+    )
+    args = parser.parse_args()
+
+    with open(args.src_path, "r") as src_file, open(args.evaluation_set, "w") as eval_file, open(
+        args.gold_data_path, "w"
+    ) as gold_file:
+        dpr_records = json.load(src_file)
+        for dpr_record in tqdm(dpr_records):
+            question = dpr_record["question"]
+            contexts = [context["title"] for context in dpr_record["positive_ctxs"]]
+            eval_file.write(question + "\n")
+            gold_file.write("\t".join(contexts) + "\n")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/utils.py

@@ -0,0 +1,174 @@
+import linecache
+import re
+import string
+from collections import Counter
+from logging import getLogger
+from pathlib import Path
+from typing import Dict, List
+
+import torch
+from torch.utils.data import Dataset
+
+from examples.seq2seq.utils import SortishSampler, trim_batch
+from transformers import BartTokenizer, T5Tokenizer
+
+
+def encode_line(tokenizer, line, max_length, padding_side, pad_to_max_length=True, return_tensors="pt"):
+    extra_kw = {"add_prefix_space": True} if isinstance(tokenizer, BartTokenizer) else {}
+    tokenizer.padding_side = padding_side
+    return tokenizer(
+        [line],
+        max_length=max_length,
+        padding="max_length" if pad_to_max_length else None,
+        truncation=True,
+        return_tensors=return_tensors,
+        add_special_tokens=True,
+        **extra_kw,
+    )
+
+
+class Seq2SeqDataset(Dataset):
+    def __init__(
+        self,
+        tokenizer,
+        data_dir,
+        max_source_length,
+        max_target_length,
+        type_path="train",
+        n_obs=None,
+        src_lang=None,
+        tgt_lang=None,
+        prefix="",
+    ):
+        super().__init__()
+        self.src_file = Path(data_dir).joinpath(type_path + ".source")
+        self.tgt_file = Path(data_dir).joinpath(type_path + ".target")
+        self.src_lens = self.get_char_lens(self.src_file)
+        self.max_source_length = max_source_length
+        self.max_target_length = max_target_length
+        assert min(self.src_lens) > 0, f"found empty line in {self.src_file}"
+        self.tokenizer = tokenizer
+        self.prefix = prefix
+        if n_obs is not None:
+            self.src_lens = self.src_lens[:n_obs]
+        self.pad_token_id = self.tokenizer.pad_token_id
+        self.src_lang = src_lang
+        self.tgt_lang = tgt_lang
+
+    def __len__(self):
+        return len(self.src_lens)
+
+    def __getitem__(self, index) -> Dict[str, torch.Tensor]:
+        index = index + 1  # linecache starts at 1
+        source_line = self.prefix + linecache.getline(str(self.src_file), index).rstrip("\n")
+        tgt_line = linecache.getline(str(self.tgt_file), index).rstrip("\n")
+        assert source_line, f"empty source line for index {index}"
+        assert tgt_line, f"empty tgt line for index {index}"
+
+        # Need to add eos token manually for T5
+        if isinstance(self.tokenizer, T5Tokenizer):
+            source_line += self.tokenizer.eos_token
+            tgt_line += self.tokenizer.eos_token
+
+        # Pad source to the left and target to the right
+        source_inputs = encode_line(self.tokenizer, source_line, self.max_source_length, "right")  # "left")
+        target_inputs = encode_line(self.tokenizer, tgt_line, self.max_target_length, "right")
+
+        source_ids = source_inputs["input_ids"].squeeze()
+        target_ids = target_inputs["input_ids"].squeeze()
+        src_mask = source_inputs["attention_mask"].squeeze()
+        return {
+            "input_ids": source_ids,
+            "attention_mask": src_mask,
+            "decoder_input_ids": target_ids,
+        }
+
+    @staticmethod
+    def get_char_lens(data_file):
+        return [len(x) for x in Path(data_file).open().readlines()]
+
+    def collate_fn(self, batch) -> Dict[str, torch.Tensor]:
+        input_ids = torch.stack([x["input_ids"] for x in batch])
+        masks = torch.stack([x["attention_mask"] for x in batch])
+        target_ids = torch.stack([x["decoder_input_ids"] for x in batch])
+        pad_token_id = self.pad_token_id
+        y = trim_batch(target_ids, pad_token_id)
+        source_ids, source_mask = trim_batch(input_ids, pad_token_id, attention_mask=masks)
+        batch = {
+            "input_ids": source_ids,
+            "attention_mask": source_mask,
+            "decoder_input_ids": y,
+        }
+        return batch
+
+    def make_sortish_sampler(self, batch_size):
+        return SortishSampler(self.src_lens, batch_size)
+
+
+logger = getLogger(__name__)
+
+
+def normalize_answer(s):
+    """Lower text and remove punctuation, articles and extra whitespace."""
+
+    def remove_articles(text):
+        return re.sub(r"\b(a|an|the)\b", " ", text)
+
+    def white_space_fix(text):
+        return " ".join(text.split())
+
+    def remove_punc(text):
+        exclude = set(string.punctuation)
+        return "".join(ch for ch in text if ch not in exclude)
+
+    def lower(text):
+        return text.lower()
+
+    return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+
+def f1_score(prediction, ground_truth):
+    prediction_tokens = normalize_answer(prediction).split()
+    ground_truth_tokens = normalize_answer(ground_truth).split()
+    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
+    num_same = sum(common.values())
+    if num_same == 0:
+        return 0
+    precision = 1.0 * num_same / len(prediction_tokens)
+    recall = 1.0 * num_same / len(ground_truth_tokens)
+    f1 = (2 * precision * recall) / (precision + recall)
+    return f1
+
+
+def exact_match_score(prediction, ground_truth):
+    return normalize_answer(prediction) == normalize_answer(ground_truth)
+
+
+def calculate_exact_match(output_lns: List[str], reference_lns: List[str]) -> Dict:
+    assert len(output_lns) == len(reference_lns)
+    em = 0
+    for hypo, pred in zip(output_lns, reference_lns):
+        em += exact_match_score(hypo, pred)
+    if len(output_lns) > 0:
+        em /= len(output_lns)
+    return {"em": em}
+
+
+def is_rag_model(model_prefix):
+    return model_prefix.startswith("rag")
+
+
+def set_extra_model_params(extra_params, hparams, config):
+    equivalent_param = {p: p for p in extra_params}
+    # T5 models don't have `dropout` param, they have `dropout_rate` instead
+    equivalent_param["dropout"] = "dropout_rate"
+    for p in extra_params:
+        if getattr(hparams, p, None):
+            if not hasattr(config, p) and not hasattr(config, equivalent_param[p]):
+                logger.info("config doesn't have a `{}` attribute".format(p))
+                delattr(hparams, p)
+                continue
+            set_p = p if hasattr(config, p) else equivalent_param[p]
+            setattr(config, set_p, getattr(hparams, p))
+            delattr(hparams, p)
+    return hparams, config

examples/requirements.txt

@@ -12,7 +12,7 @@ faiss
 streamlit
 elasticsearch
 pandas
-nlp
+datasets
 fire
 pytest
 conllu

examples/seq2seq/README.md

@@ -6,8 +6,9 @@ Please tag @sshleifer with any issues/unexpected behaviors, or send a PR!
 For `bertabs` instructions, see [`bertabs/README.md`](bertabs/README.md).
 
 
-### Data
-XSUM Data:
+## Datasets
+
+#### XSUM:
 ```bash
 cd examples/seq2seq
 wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz
@@ -17,23 +18,33 @@ export XSUM_DIR=${PWD}/xsum
 this should make a directory called `xsum/` with files like `test.source`.
 To use your own data, copy that files format. Each article to be summarized is on its own line.
 
-CNN/DailyMail data
+#### CNN/DailyMail
 ```bash
 cd examples/seq2seq
-wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/cnn_dm.tgz
-tar -xzvf cnn_dm.tgz
+wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/cnn_dm_v2.tgz
+tar -xzvf cnn_dm_v2.tgz  # empty lines removed
+mv cnn_cln cnn_dm
 export CNN_DIR=${PWD}/cnn_dm
-this should make a directory called `cnn_dm/` with files like `test.source`.
 ```
+this should make a directory called `cnn_dm/` with 6 files.
 
-WMT16 English-Romanian Translation Data:
+#### WMT16 English-Romanian Translation Data:
 download with this command:
 ```bash
 wget https://s3.amazonaws.com/datasets.huggingface.co/translation/wmt_en_ro.tar.gz
 tar -xzvf wmt_en_ro.tar.gz
 export ENRO_DIR=${PWD}/wmt_en_ro
-this should make a directory called `wmt_en_ro/` with files like `test.source`.
 ```
+this should make a directory called `wmt_en_ro/` with 6 files.
+
+#### WMT English-German:
+```bash
+wget https://s3.amazonaws.com/datasets.huggingface.co/translation/wmt_en_de.tgz
+tar -xzvf wmt_en_de.tgz
+export DATA_DIR=${PWD}/wmt_en_de
+```
+
+#### Private Data
 
 If you are using your own data, it must be formatted as one directory with 6 files: 
 ```
@@ -71,11 +82,12 @@ Summarization Tips:
 (It rarely makes sense to start from `bart-large` unless you are a researching finetuning methods).
 
 **Update 2018-07-18**
-Datasets: `Seq2SeqDataset` should be used for all tokenizers without a `prepare_seq2seq_batch` method. For those who do (like Marian, MBart), `TranslationDataset` should be used.**
-A new dataset is needed to support multilingual tasks.
+Datasets: `LegacySeq2SeqDataset` will be used for all tokenizers without a `prepare_seq2seq_batch` method. Otherwise, `Seq2SeqDataset` will be used.
+Future work/help wanted: A new dataset to support multilingual tasks.
 
 
-### Command Line Options
+### Finetuning Scripts
+All finetuning bash scripts call finetune.py (or distillation.py) with reasonable command line arguments. They usually require extra command line arguments to work.
 
 To see all the possible command line options, run:
 
@@ -106,10 +118,12 @@ The following command should work on a 16GB GPU:
     --train_batch_size=1 \
     --eval_batch_size=1 \
     --output_dir=xsum_results \
-    --num_train_epochs 1 \
+    --num_train_epochs 6 \
     --model_name_or_path facebook/bart-large
 ```
 
+There is a starter finetuning script for pegasus at `finetune_pegasus_xsum.sh`.
+
 ### Translation Finetuning
 
 First, follow the wmt_en_ro download instructions.

examples/seq2seq/callbacks.py

@@ -75,22 +75,24 @@ class Seq2SeqLoggingCallback(pl.Callback):
         return self._write_logs(trainer, pl_module, "test")
 
 
-def get_checkpoint_callback(output_dir, metric):
+def get_checkpoint_callback(output_dir, metric, save_top_k=1, lower_is_better=False):
     """Saves the best model by validation ROUGE2 score."""
     if metric == "rouge2":
         exp = "{val_avg_rouge2:.4f}-{step_count}"
     elif metric == "bleu":
         exp = "{val_avg_bleu:.4f}-{step_count}"
+    elif metric == "loss":
+        exp = "{val_avg_loss:.4f}-{step_count}"
     else:
         raise NotImplementedError(
-            f"seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this function."
+            f"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to this function."
         )
 
     checkpoint_callback = ModelCheckpoint(
         filepath=os.path.join(output_dir, exp),
         monitor=f"val_{metric}",
-        mode="max",
-        save_top_k=1,
+        mode="min" if "loss" in metric else "max",
+        save_top_k=save_top_k,
         period=0,  # maybe save a checkpoint every time val is run, not just end of epoch.
     )
     return checkpoint_callback
@@ -98,8 +100,8 @@ def get_checkpoint_callback(output_dir, metric):
 
 def get_early_stopping_callback(metric, patience):
     return EarlyStopping(
-        monitor=f"val_{metric}",
-        mode="max",
+        monitor=f"val_{metric}",  # does this need avg?
+        mode="min" if "loss" in metric else "max",
         patience=patience,
         verbose=True,
     )

examples/seq2seq/convert_pl_checkpoint_to_hf.py

@@ -0,0 +1,72 @@
+import os
+from pathlib import Path
+from typing import Dict, List
+
+import fire
+import torch
+
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from transformers.utils.logging import get_logger
+
+
+logger = get_logger(__name__)
+
+
+def remove_prefix(text: str, prefix: str):
+    if text.startswith(prefix):
+        return text[len(prefix) :]
+    return text  # or whatever
+
+
+def sanitize(sd):
+    return {remove_prefix(k, "model."): v for k, v in sd.items()}
+
+
+def average_state_dicts(state_dicts: List[Dict[str, torch.Tensor]]):
+    new_sd = {}
+    for k in state_dicts[0].keys():
+        tensors = [sd[k] for sd in state_dicts]
+        new_t = sum(tensors) / len(tensors)
+        assert isinstance(new_t, torch.Tensor)
+        new_sd[k] = new_t
+    return new_sd
+
+
+def convert_pl_to_hf(pl_ckpt_path: str, hf_src_model_dir: str, save_path: str) -> None:
+    """Cleanup a pytorch-lightning .ckpt file or experiment dir and save a huggingface model with that state dict.
+    Silently allows extra pl keys (like teacher.) Puts all ckpt models into CPU RAM at once!
+
+    Args:
+        pl_ckpt_path (:obj:`str`): Path to a .ckpt file saved by pytorch_lightning or dir containing ckpt files.
+            If a directory is passed, all .ckpt files inside it will be averaged!
+        hf_src_model_dir (:obj:`str`): Path to a directory containing a correctly shaped checkpoint
+        save_path (:obj:`str`): Directory to save the new model
+
+    """
+    hf_model = AutoModelForSeq2SeqLM.from_pretrained(hf_src_model_dir)
+    if os.path.isfile(pl_ckpt_path):
+        ckpt_files = [pl_ckpt_path]
+    else:
+        assert os.path.isdir(pl_ckpt_path)
+        ckpt_files = list(Path(pl_ckpt_path).glob("*.ckpt"))
+        assert ckpt_files, f"could not find any ckpt files inside the {pl_ckpt_path} directory"
+
+    if len(ckpt_files) > 1:
+        logger.info(f"averaging the weights of {ckpt_files}")
+
+    state_dicts = [sanitize(torch.load(x, map_location="cpu")["state_dict"]) for x in ckpt_files]
+    state_dict = average_state_dicts(state_dicts)
+
+    missing, unexpected = hf_model.load_state_dict(state_dict, strict=False)
+    assert not missing, f"missing keys: {missing}"
+    hf_model.save_pretrained(save_path)
+    try:
+        tok = AutoTokenizer.from_pretrained(hf_src_model_dir)
+        tok.save_pretrained(save_path)
+    except Exception:
+        pass
+        # dont copy tokenizer if cant
+
+
+if __name__ == "__main__":
+    fire.Fire(convert_pl_to_hf)

examples/seq2seq/distil_marian_enro_teacher.sh

@@ -0,0 +1,20 @@
+#!/usr/bin/env bash
+export PYTHONPATH="../":"${PYTHONPATH}"
+export WANDB_PROJECT=dmar
+# export MAX_LEN=128
+python distillation.py \
+  --learning_rate=3e-4 \
+  --do_train \
+  --fp16 \
+  --val_check_interval 0.25 \
+  --teacher Helsinki-NLP/opus-mt-en-ro \
+  --max_source_length $MAX_LEN --max_target_length $MAX_LEN --val_max_target_length $MAX_LEN --test_max_target_length $MAX_LEN \
+  --student_decoder_layers 3 --student_encoder_layers 6 \
+  --freeze_encoder --freeze_embeds \
+  --model_name_or_path IGNORED \
+  --alpha_hid=3. \
+  --train_batch_size=$BS --eval_batch_size=$BS \
+  --tokenizer_name Helsinki-NLP/opus-mt-en-ro \
+  --warmup_steps 500 --logger_name wandb \
+  --fp16_opt_level O1 --task translation --normalize_hidden --num_sanity_val_steps=0 \
+  "$@"

examples/seq2seq/distil_marian_no_teacher.sh

@@ -0,0 +1,17 @@
+#!/usr/bin/env bash
+export PYTHONPATH="../":"${PYTHONPATH}"
+export WANDB_PROJECT=dmar
+python distillation.py \
+  --learning_rate=3e-4 \
+  --do_train \
+  --do_predict \
+  --fp16 --no_teacher \
+  --val_check_interval 0.25 \
+  --data_dir $ENRO_DIR \
+  --max_source_length $MAX_LEN --max_target_length $MAX_LEN --val_max_target_length $MAX_LEN --test_max_target_length $MAX_LEN \
+  --freeze_encoder --freeze_embeds \
+  --train_batch_size=$BS --eval_batch_size=$BS \
+  --tokenizer_name $m --model_name_or_path $m \
+  --warmup_steps 500 --sortish_sampler --logger_name wandb \
+  --gpus 1 --fp16_opt_level=O1 --task translation --num_sanity_val_steps=0 \
+  "$@"

examples/seq2seq/distillation.py

@@ -1,6 +1,7 @@
 import argparse
 import gc
 import os
+import warnings
 from pathlib import Path
 from typing import List
 
@@ -10,7 +11,8 @@ from torch import nn
 from torch.nn import functional as F
 
 from lightning_base import generic_train
-from transformers import BartConfig, BartForConditionalGeneration, MBartTokenizer, T5Config, T5ForConditionalGeneration
+from transformers import AutoModelForSeq2SeqLM, MBartTokenizer, T5Config, T5ForConditionalGeneration
+from transformers.modeling_bart import shift_tokens_right
 
 
 try:
@@ -22,6 +24,7 @@ try:
         assert_all_frozen,
         calculate_bleu,
         freeze_params,
+        label_smoothed_nll_loss,
         pickle_load,
         use_task_specific_params,
     )
@@ -34,12 +37,15 @@ except ImportError:
         assert_all_frozen,
         calculate_bleu,
         freeze_params,
+        label_smoothed_nll_loss,
         pickle_load,
         use_task_specific_params,
     )
 
 
 class BartSummarizationDistiller(SummarizationModule):
+    """Supports Bart, Pegasus and other models that inherit from Bart."""
+
     loss_names = ["loss", "ce_loss", "mlm_loss", "enc_mse_loss", "hid_loss_enc", "hid_loss_dec"]
 
     def __init__(self, hparams):
@@ -74,22 +80,31 @@ class BartSummarizationDistiller(SummarizationModule):
     def pre_init(self, hparams):
         self.output_dir = Path(hparams.output_dir)
         self.output_dir.mkdir(exist_ok=True)
-        teacher = BartForConditionalGeneration.from_pretrained(hparams.teacher).eval()
+        teacher = AutoModelForSeq2SeqLM.from_pretrained(hparams.teacher).eval()
         student_updates = {
             "decoder_layers": hparams.student_decoder_layers,
             "encoder_layers": hparams.student_encoder_layers,
         }
         if hparams.length_penalty != -1:
             student_updates["length_penalty"] = hparams.length_penalty
-        d_layers_to_copy = get_layers_to_copy(student_updates["decoder_layers"], teacher.config.decoder_layers)
         e_layers_to_copy: List = get_layers_to_copy(student_updates["encoder_layers"], teacher.config.encoder_layers)
-        hparams.d_layer_to_copy = d_layers_to_copy
         hparams.e_layer_to_copy = e_layers_to_copy
+
+        d_layers_to_copy: List = get_layers_to_copy(student_updates["decoder_layers"], teacher.config.decoder_layers)
+
+        if hparams.supervise_forward:
+            hparams.d_matches = get_layers_to_supervise(
+                student_updates["decoder_layers"], teacher.config.decoder_layers
+            )
+        else:
+            hparams.d_matches = d_layers_to_copy
+        hparams.d_layer_to_copy = d_layers_to_copy
+
         kw = teacher.config.to_diff_dict()
         kw.update(student_updates)
         # Copy weights
-        student_cfg = BartConfig(**kw)
-        student = BartForConditionalGeneration(student_cfg)
+        student_cfg = teacher.config_class(**kw)
+        student = type(teacher)(student_cfg)
         student, _ = init_student(student, teacher)
         save_dir = self.output_dir.joinpath("student")
         self.copy_to_student(d_layers_to_copy, e_layers_to_copy, hparams, student, teacher)
@@ -160,22 +175,32 @@ class BartSummarizationDistiller(SummarizationModule):
     def _step(self, batch):
         # assert is_frozen(self.teacher)
         pad_token_id = self.tokenizer.pad_token_id
-        input_ids, src_mask, y = batch["input_ids"], batch["attention_mask"], batch["decoder_input_ids"]
-        decoder_input_ids = y[:, :-1].contiguous()
-        labels = y[:, 1:].clone()
-        labels[y[:, 1:] == pad_token_id] = -100
+        input_ids, src_mask, tgt_ids = batch["input_ids"], batch["attention_mask"], batch["labels"]
+        decoder_input_ids = shift_tokens_right(tgt_ids, pad_token_id)
         # noinspection PyCallingNonCallable
-        sloss, slogits, dec_hidden, enc_outputs, enc_hidden_state = self(
+        lm_logits, dec_hidden, enc_outputs, enc_hidden_state = self(
             input_ids,
             attention_mask=src_mask,
             decoder_input_ids=decoder_input_ids,
-            labels=labels,
             output_hidden_states=True,
             output_attentions=False,
-        )
+            use_cache=False,
+        )  # TODO(@sshleifer): return_dict=True cleanup
+
+        # Same cross entropy vs. label smoothing logic as finetune.py
+        assert lm_logits.shape[-1] == self.model.config.vocab_size
+        if self.hparams.label_smoothing == 0:
+            # Same behavior as modeling_bart.py, besides ignoring pad_token_id
+            loss_fct = torch.nn.CrossEntropyLoss(ignore_index=pad_token_id)
+            student_lm_loss = loss_fct(lm_logits.view(-1, lm_logits.shape[-1]), tgt_ids.view(-1))
+        else:
+            lprobs = torch.nn.functional.log_softmax(lm_logits, dim=-1)
+            student_lm_loss, _ = label_smoothed_nll_loss(
+                lprobs, tgt_ids, self.hparams.label_smoothing, ignore_index=pad_token_id
+            )
 
         def zero_tensor():
-            return torch.tensor(0.0).type_as(sloss)
+            return torch.tensor(0.0).type_as(student_lm_loss)
 
         loss_encoder, hid_loss_enc, hid_loss_dec = zero_tensor(), zero_tensor(), zero_tensor()
         if self.different_encoder:
@@ -199,41 +224,40 @@ class BartSummarizationDistiller(SummarizationModule):
                 attention_mask=src_mask,
                 encoder_outputs=teacher_enc_outputs,
                 decoder_input_ids=decoder_input_ids,
-                lm_labels=labels,
+                lm_labels=tgt_ids,
                 output_hidden_states=True,
             )
         dec_mask = decoder_input_ids.ne(pad_token_id)
-        loss_ce, s_logits_slct, t_logits_slct = self.calc_ce_loss(dec_mask, slogits, tlogits)
+        loss_ce, s_logits_slct, t_logits_slct = self.calc_ce_loss(dec_mask, lm_logits, tlogits)
         if self.alpha_hid > 0:
-            hid_loss_dec = self.calc_hidden_loss(dec_mask, dec_hidden, tdec_hidden, self.hparams.d_layer_to_copy)
+            hid_loss_dec = self.calc_hidden_loss(dec_mask, dec_hidden, tdec_hidden, self.hparams.d_matches)
 
         blended_loss = (
             self.alpha_ce * loss_ce
-            + self.alpha_mlm * sloss
+            + self.alpha_mlm * student_lm_loss
             + self.hparams.alpha_encoder_loss * loss_encoder
             + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
         )
-        return blended_loss, loss_ce, sloss, loss_encoder, hid_loss_enc, hid_loss_dec
+        return blended_loss, loss_ce, student_lm_loss, loss_encoder, hid_loss_enc, hid_loss_dec
 
     def calc_hidden_loss(self, attention_mask, hidden_states, hidden_states_T, matches):
-        assert not isinstance(
-            hidden_states, torch.Tensor
-        ), f"expected list or tuple for hidden_states, got tensor of shape {hidden_states.shape}"
-        assert not isinstance(
-            hidden_states_T, torch.Tensor
-        ), f"expected list or tuple for hidden_states_T, got tensor of shape {hidden_states_T.shape}"
+        msg = "expected list or tuple for hidden_states, got tensor of shape: "
+        assert not isinstance(hidden_states, torch.Tensor), f"{msg}{hidden_states.shape}"
+        assert not isinstance(hidden_states_T, torch.Tensor), f"{msg}{hidden_states_T.shape}"
         mask = attention_mask.to(hidden_states[0])
         valid_count = mask.sum() * hidden_states[0].size(-1)
-        hidden_losses = [
-            (F.mse_loss(hidden_states[i], hidden_states_T[j], reduction="none") * mask.unsqueeze(-1)).sum()
-            / valid_count
-            for i, j in enumerate(matches)
-        ]
-        return sum(hidden_losses)
+        student_states = torch.stack([hidden_states[i] for i in range(len(matches))])
+        teacher_states = torch.stack([hidden_states_T[j] for j in matches])
+        if self.hparams.normalize_hidden:
+            student_states = F.layer_norm(student_states, student_states.shape[1:])
+            teacher_states = F.layer_norm(teacher_states, teacher_states.shape[1:])
+        mse = F.mse_loss(student_states, teacher_states, reduction="none")
+        masked_mse = (mse * mask.unsqueeze(0).unsqueeze(-1)).sum() / valid_count
+        return masked_mse
 
 
 def add_distill_args(parser):
-    parser.add_argument("--teacher", default="facebook/bart-large-cnn", type=str)
+    parser.add_argument("--teacher", type=str)
     parser.add_argument("--alpha_ce", default=0.8, type=float)
     parser.add_argument("--alpha_mlm", default=0.2, type=float)
     parser.add_argument("--alpha_encoder_loss", default=0.0, type=float)
@@ -242,17 +266,19 @@ def add_distill_args(parser):
     parser.add_argument("--student_encoder_layers", default=12, type=int, required=False)
     parser.add_argument("--no_teacher", action="store_true", default=False)
     parser.add_argument("--length_penalty", type=float, default=-1)
+    parser.add_argument("--supervise_forward", action="store_true", default=False)
+    parser.add_argument("--normalize_hidden", action="store_true", default=False)
 
 
 class BartTranslationDistiller(BartSummarizationDistiller):
+    """Supports Mbart, Marian, other models that inherit from Bart."""
+
     mode = "translation"
-    loss_names = ["loss"]
     metric_names = ["bleu"]
-    val_metric = "bleu"
+    default_val_metric = "bleu"
 
     def __init__(self, hparams, **kwargs):
         super().__init__(hparams, **kwargs)
-        assert isinstance(self.tokenizer, MBartTokenizer)
         assert hparams.src_lang is not None
         assert hparams.tgt_lang is not None
         self.dataset_kwargs["src_lang"] = hparams.src_lang
@@ -369,14 +395,14 @@ class T5SummarizationDistiller(BartSummarizationDistiller):
                 attention_mask=source_mask,
                 encoder_outputs=teacher_enc_outputs,
                 decoder_input_ids=decoder_input_ids,
-                lm_labels=labels,
+                labels=labels,
                 output_hidden_states=True,
                 use_cache=False,
             )
 
         loss_ce, s_logits_slct, t_logits_slct = self.calc_ce_loss(dec_mask, slogits, tlogits)
         if self.alpha_hid > 0:
-            hid_loss_dec = self.calc_hidden_loss(dec_mask, dec_hidden, tdec_hidden, self.hparams.d_layer_to_copy)
+            hid_loss_dec = self.calc_hidden_loss(dec_mask, dec_hidden, tdec_hidden, self.hparams.d_matches)
 
         blended_loss = (
             self.alpha_ce * loss_ce
@@ -403,6 +429,7 @@ def create_module(args):
 
 
 def evaluate_checkpoint(ckpt_path: Path, dest_dir=None):
+    # TODO(SS): DELETE? Better to convert_pl_ckpt_to_hf and run_eval.py
     exp_dir = ckpt_path.parent
     if dest_dir is None:
         dest_dir = exp_dir
@@ -425,33 +452,53 @@ def evaluate_checkpoint(ckpt_path: Path, dest_dir=None):
     trainer.test(model)
 
 
-def get_layers_to_copy(n_to_get, tot):
-    all_layers = list(range(tot))
-    if tot == 12:  # Alternating for special cases
-        layers_to_copy = {  # maps  num layers in student -> which teacher layers to copy
-            1: [0],
-            2: [0, 6],
-            3: [0, 6, 11],
-            4: [0, 4, 8, 11],
-            6: [0, 2, 4, 7, 9, 11],
-            9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
-            12: all_layers,
-        }
-        return layers_to_copy[n_to_get]
-    elif tot == 16:
-        layers_to_copy = {  # maps  num layers in student -> which teacher layers to copy
-            1: [0],
-            2: [0, 8],
-            3: [0, 8, 15],
-            4: [0, 5, 10, 15],
-            6: [0, 3, 6, 9, 12, 15],
-            8: [0, 2, 4, 6, 8, 10, 12, 15],
-            9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
-            16: all_layers,
-        }
-        return layers_to_copy[n_to_get]
-    else:
-        return all_layers[:n_to_get]  # TODO: better version on theseus-bart branch
+LAYERS_TO_COPY = {
+    # maps  num layers in student -> which teacher layers to copy.
+    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
+    12: {
+        1: [0],
+        2: [0, 6],
+        3: [0, 6, 11],
+        4: [0, 4, 8, 11],
+        6: [0, 2, 4, 7, 9, 11],
+        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
+        12: list(range(12)),
+    },
+    16: {  # maps  num layers in student -> which teacher layers to copy
+        1: [0],
+        2: [0, 8],
+        3: [0, 8, 15],
+        4: [0, 5, 10, 15],
+        6: [0, 3, 6, 9, 12, 15],
+        8: [0, 2, 4, 6, 8, 10, 12, 15],
+        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
+        16: list(range(16)),
+    },
+    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
+}
+LAYERS_TO_SUPERVISE = {
+    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
+    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
+    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
+    2: {1: [1], 2: [0, 1]},
+}
+
+
+def get_layers_to_supervise(n_student, n_teacher):
+    return LAYERS_TO_SUPERVISE[n_teacher][n_student]
+
+
+def get_layers_to_copy(n_student, n_teacher):
+    try:
+        val = LAYERS_TO_COPY[n_teacher][n_student]
+        assert len(LAYERS_TO_SUPERVISE[n_teacher][n_student]) == len(val) == n_student
+        return val
+    except KeyError:
+        if n_student != n_teacher:
+            warnings.warn(
+                f"no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first {n_student}"
+            )
+        return list(range(n_student))
 
 
 def distill_main(args):

examples/seq2seq/download_wmt.py

@@ -5,25 +5,25 @@ from tqdm import tqdm
 
 
 def download_wmt_dataset(src_lang="ro", tgt_lang="en", dataset="wmt16", save_dir=None) -> None:
-    """Download a dataset using the nlp package and save it to the format expected by finetune.py
+    """Download a dataset using the datasets package and save it to the format expected by finetune.py
     Format of save_dir: train.source, train.target, val.source, val.target, test.source, test.target.
 
     Args:
         src_lang: <str> source language
         tgt_lang: <str> target language
-        dataset: <str> wmt16, wmt17, etc. wmt16 is a good start as it's small. To get the full list run `import nlp; print([d.id for d in nlp.list_datasets() if "wmt" in d.id])`
+        dataset: <str> wmt16, wmt17, etc. wmt16 is a good start as it's small. To get the full list run `import datasets; print([d.id for d in datasets.list_datasets() if "wmt" in d.id])`
         save_dir: <str>, where to save the datasets, defaults to f'{dataset}-{src_lang}-{tgt_lang}'
 
     Usage:
         >>> download_wmt_dataset('ro', 'en', dataset='wmt16') # saves to wmt16-ro-en
     """
     try:
-        import nlp
+        import datasets
     except (ModuleNotFoundError, ImportError):
-        raise ImportError("run pip install nlp")
+        raise ImportError("run pip install datasets")
     pair = f"{src_lang}-{tgt_lang}"
     print(f"Converting {dataset}-{pair}")
-    ds = nlp.load_dataset(dataset, pair)
+    ds = datasets.load_dataset(dataset, pair)
     if save_dir is None:
         save_dir = f"{dataset}-{pair}"
     save_dir = Path(save_dir)

examples/seq2seq/finetune.py

@@ -13,15 +13,16 @@ import torch
 from torch.utils.data import DataLoader
 
 from lightning_base import BaseTransformer, add_generic_args, generic_train
-from transformers import MarianTokenizer, MBartTokenizer, T5ForConditionalGeneration
+from transformers import MBartTokenizer, T5ForConditionalGeneration
+from transformers.modeling_bart import shift_tokens_right
 
 
 try:
     from .callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
     from .utils import (
         ROUGE_KEYS,
+        LegacySeq2SeqDataset,
         Seq2SeqDataset,
-        TranslationDataset,
         assert_all_frozen,
         calculate_bleu,
         calculate_rouge,
@@ -39,8 +40,8 @@ except ImportError:
     from callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
     from utils import (
         ROUGE_KEYS,
+        LegacySeq2SeqDataset,
         Seq2SeqDataset,
-        TranslationDataset,
         assert_all_frozen,
         calculate_bleu,
         calculate_rouge,
@@ -62,9 +63,11 @@ class SummarizationModule(BaseTransformer):
     mode = "summarization"
     loss_names = ["loss"]
     metric_names = ROUGE_KEYS
-    val_metric = "rouge2"
+    default_val_metric = "rouge2"
 
     def __init__(self, hparams, **kwargs):
+        if hparams.sortish_sampler and hparams.gpus > 1:
+            hparams.replace_sampler_ddp = False
         super().__init__(hparams, num_labels=None, mode=self.mode, **kwargs)
         use_task_specific_params(self.model, "summarization")
         save_git_info(self.hparams.output_dir)
@@ -102,14 +105,20 @@ class SummarizationModule(BaseTransformer):
 
         self.hparams.git_sha = get_git_info()["repo_sha"]
         self.num_workers = hparams.num_workers
-        self.decoder_start_token_id = None
+        self.decoder_start_token_id = None  # default to config
         if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer, MBartTokenizer):
             self.decoder_start_token_id = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
             self.model.config.decoder_start_token_id = self.decoder_start_token_id
-        if isinstance(self.tokenizer, MBartTokenizer) or isinstance(self.tokenizer, MarianTokenizer):
-            self.dataset_class = TranslationDataset
+        self.dataset_class = (
+            Seq2SeqDataset if hasattr(self.tokenizer, "prepare_seq2seq_batch") else LegacySeq2SeqDataset
+        )
+        self.eval_beams = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
+        assert self.eval_beams >= 1, f"got self.eval_beams={self.eval_beams}. Need an integer > 1"
+        if self.hparams.eval_max_gen_length is not None:
+            self.eval_max_length = self.hparams.eval_max_gen_length
         else:
-            self.dataset_class = Seq2SeqDataset
+            self.eval_max_length = self.model.config.max_length
+        self.val_metric = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
 
     def freeze_embeds(self):
         """Freeze token embeddings and positional embeddings for bart, just token embeddings for t5."""
@@ -134,27 +143,25 @@ class SummarizationModule(BaseTransformer):
 
     def _step(self, batch: dict) -> Tuple:
         pad_token_id = self.tokenizer.pad_token_id
-        source_ids, source_mask, target_ids = batch["input_ids"], batch["attention_mask"], batch["decoder_input_ids"]
-
+        src_ids, src_mask = batch["input_ids"], batch["attention_mask"]
+        tgt_ids = batch["labels"]
         if isinstance(self.model, T5ForConditionalGeneration):
-            decoder_input_ids = self.model._shift_right(target_ids)
-            lm_labels = target_ids
+            decoder_input_ids = self.model._shift_right(tgt_ids)
         else:
-            decoder_input_ids = target_ids[:, :-1].contiguous()  # Why this line?
-            lm_labels = target_ids[:, 1:].clone()  # why clone?
-
-        outputs = self(source_ids, attention_mask=source_mask, decoder_input_ids=decoder_input_ids, use_cache=False)
+            decoder_input_ids = shift_tokens_right(tgt_ids, pad_token_id)
 
+        outputs = self(src_ids, attention_mask=src_mask, decoder_input_ids=decoder_input_ids, use_cache=False)
+        lm_logits = outputs[0]
         if self.hparams.label_smoothing == 0:
-            # Same behavior as modeling_bart.py
-            loss_fct = torch.nn.CrossEntropyLoss(ignore_index=pad_token_id)
-            lm_logits = outputs[0]
+            # Same behavior as modeling_bart.py, besides ignoring pad_token_id
+            ce_loss_fct = torch.nn.CrossEntropyLoss(ignore_index=pad_token_id)
+
             assert lm_logits.shape[-1] == self.model.config.vocab_size
-            loss = loss_fct(lm_logits.view(-1, lm_logits.shape[-1]), lm_labels.view(-1))
+            loss = ce_loss_fct(lm_logits.view(-1, lm_logits.shape[-1]), tgt_ids.view(-1))
         else:
-            lprobs = torch.nn.functional.log_softmax(outputs[0], dim=-1)
+            lprobs = torch.nn.functional.log_softmax(lm_logits, dim=-1)
             loss, nll_loss = label_smoothed_nll_loss(
-                lprobs, lm_labels, self.hparams.label_smoothing, ignore_index=pad_token_id
+                lprobs, tgt_ids, self.hparams.label_smoothing, ignore_index=pad_token_id
             )
         return (loss,)
 
@@ -167,7 +174,7 @@ class SummarizationModule(BaseTransformer):
 
         logs = {name: loss for name, loss in zip(self.loss_names, loss_tensors)}
         # tokens per batch
-        logs["tpb"] = batch["input_ids"].ne(self.pad).sum() + batch["decoder_input_ids"].ne(self.pad).sum()
+        logs["tpb"] = batch["input_ids"].ne(self.pad).sum() + batch["labels"].ne(self.pad).sum()
         return {"loss": loss_tensors[0], "log": logs}
 
     def validation_step(self, batch, batch_idx) -> Dict:
@@ -177,15 +184,25 @@ class SummarizationModule(BaseTransformer):
         self.step_count += 1
         losses = {k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
         loss = losses["loss"]
-        rouges = {k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ["gen_time", "gen_len"]}
-        rouge_tensor: torch.FloatTensor = torch.tensor(rouges[self.val_metric]).type_as(loss)
-        rouges.update({k: v.item() for k, v in losses.items()})
-        losses.update(rouges)
-        metrics = {f"{prefix}_avg_{k}": x for k, x in losses.items()}
-        metrics["step_count"] = self.step_count
-        self.save_metrics(metrics, prefix)  # writes to self.metrics_save_path
+        generative_metrics = {
+            k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ["gen_time", "gen_len"]
+        }
+        metric_val = (
+            generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
+        )
+        metric_tensor: torch.FloatTensor = torch.tensor(metric_val).type_as(loss)
+        generative_metrics.update({k: v.item() for k, v in losses.items()})
+        losses.update(generative_metrics)
+        all_metrics = {f"{prefix}_avg_{k}": x for k, x in losses.items()}
+        all_metrics["step_count"] = self.step_count
+        self.save_metrics(all_metrics, prefix)  # writes to self.metrics_save_path
         preds = flatten_list([x["preds"] for x in outputs])
-        return {"log": metrics, "preds": preds, f"{prefix}_loss": loss, f"{prefix}_{self.val_metric}": rouge_tensor}
+        return {
+            "log": all_metrics,
+            "preds": preds,
+            f"{prefix}_loss": loss,
+            f"{prefix}_{self.val_metric}": metric_tensor,
+        }
 
     def save_metrics(self, latest_metrics, type_path) -> None:
         self.metrics[type_path].append(latest_metrics)
@@ -196,15 +213,19 @@ class SummarizationModule(BaseTransformer):
 
     def _generative_step(self, batch: dict) -> dict:
         t0 = time.time()
+
+        # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
         generated_ids = self.model.generate(
             batch["input_ids"],
             attention_mask=batch["attention_mask"],
             use_cache=True,
             decoder_start_token_id=self.decoder_start_token_id,
+            num_beams=self.eval_beams,
+            max_length=self.eval_max_length,
         )
         gen_time = (time.time() - t0) / batch["input_ids"].shape[0]
         preds: List[str] = self.ids_to_clean_text(generated_ids)
-        target: List[str] = self.ids_to_clean_text(batch["decoder_input_ids"])
+        target: List[str] = self.ids_to_clean_text(batch["labels"])
         loss_tensors = self._step(batch)
         base_metrics = {name: loss for name, loss in zip(self.loss_names, loss_tensors)}
         rouge: Dict = self.calc_generative_metrics(preds, target)
@@ -234,8 +255,7 @@ class SummarizationModule(BaseTransformer):
         dataset = self.get_dataset(type_path)
         sampler = None
         if self.hparams.sortish_sampler and type_path == "train":
-            assert self.hparams.gpus <= 1  # TODO: assert earlier
-            sampler = dataset.make_sortish_sampler(batch_size)
+            sampler = dataset.make_sortish_sampler(batch_size, distributed=self.hparams.gpus > 1)
             shuffle = False
 
         dataloader = DataLoader(
@@ -303,6 +323,12 @@ class SummarizationModule(BaseTransformer):
         parser.add_argument("--label_smoothing", type=float, default=0.0, required=False)
         parser.add_argument("--src_lang", type=str, default="", required=False)
         parser.add_argument("--tgt_lang", type=str, default="", required=False)
+        parser.add_argument("--eval_beams", type=int, default=None, required=False)
+        parser.add_argument(
+            "--val_metric", type=str, default=None, required=False, choices=["bleu", "rouge2", "loss", None]
+        )
+        parser.add_argument("--eval_max_gen_length", type=int, default=None, help="never generate more than n tokens")
+        parser.add_argument("--save_top_k", type=int, default=1, required=False, help="How many checkpoints to save")
         parser.add_argument(
             "--early_stopping_patience",
             type=int,
@@ -317,7 +343,7 @@ class TranslationModule(SummarizationModule):
     mode = "translation"
     loss_names = ["loss"]
     metric_names = ["bleu"]
-    val_metric = "bleu"
+    default_val_metric = "bleu"
 
     def __init__(self, hparams, **kwargs):
         super().__init__(hparams, **kwargs)
@@ -333,11 +359,10 @@ def main(args, model=None) -> SummarizationModule:
     if len(os.listdir(args.output_dir)) > 3 and args.do_train:
         raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
     if model is None:
-        if args.task == "summarization":
+        if "summarization" in args.task:
             model: SummarizationModule = SummarizationModule(args)
         else:
             model: SummarizationModule = TranslationModule(args)
-
     dataset = Path(args.data_dir).name
     if (
         args.logger_name == "default"
@@ -361,14 +386,17 @@ def main(args, model=None) -> SummarizationModule:
         es_callback = get_early_stopping_callback(model.val_metric, args.early_stopping_patience)
     else:
         es_callback = False
+
+    lower_is_better = args.val_metric == "loss"
     trainer: pl.Trainer = generic_train(
         model,
         args,
         logging_callback=Seq2SeqLoggingCallback(),
-        checkpoint_callback=get_checkpoint_callback(args.output_dir, model.val_metric),
+        checkpoint_callback=get_checkpoint_callback(
+            args.output_dir, model.val_metric, args.save_top_k, lower_is_better
+        ),
         early_stopping_callback=es_callback,
         logger=logger,
-        # TODO: early stopping callback seems messed up
     )
     pickle_save(model.hparams, model.output_dir / "hparams.pkl")
     if not args.do_predict:

examples/seq2seq/finetune_pegasus_xsum.sh

@@ -10,5 +10,5 @@ python finetune.py \
     --n_val 1000 \
     --val_check_interval 0.25 \
     --max_source_length 512 --max_target_length 56 \
-    --freeze_embeds --max_target_length 56 --label_smoothing 0.1 \
+    --freeze_embeds --label_smoothing 0.1 --adafactor --task summarization_xsum \
     "$@"

examples/seq2seq/run_eval.py

@@ -1,6 +1,10 @@
 import argparse
 import json
+import time
+import warnings
+from logging import getLogger
 from pathlib import Path
+from typing import Dict, List
 
 import torch
 from tqdm import tqdm
@@ -8,10 +12,12 @@ from tqdm import tqdm
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
 
+logger = getLogger(__name__)
+
 try:
-    from .utils import calculate_bleu, calculate_rouge, trim_batch, use_task_specific_params
+    from .utils import calculate_bleu, calculate_rouge, parse_numeric_cl_kwargs, use_task_specific_params
 except ImportError:
-    from utils import calculate_bleu, calculate_rouge, trim_batch, use_task_specific_params
+    from utils import calculate_bleu, calculate_rouge, parse_numeric_cl_kwargs, use_task_specific_params
 
 DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 
@@ -23,44 +29,47 @@ def chunks(lst, n):
 
 
 def generate_summaries_or_translations(
-    examples: list,
+    examples: List[str],
     out_file: str,
     model_name: str,
     batch_size: int = 8,
     device: str = DEFAULT_DEVICE,
     fp16=False,
     task="summarization",
-    decoder_start_token_id=None,
-    **gen_kwargs,
-) -> None:
+    prefix=None,
+    **generate_kwargs,
+) -> Dict:
+    """Save model.generate results to <out_file>, and return how long it took."""
     fout = Path(out_file).open("w", encoding="utf-8")
     model_name = str(model_name)
     model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
     if fp16:
         model = model.half()
-    if decoder_start_token_id is None:
-        decoder_start_token_id = gen_kwargs.pop("decoder_start_token_id", None)
 
     tokenizer = AutoTokenizer.from_pretrained(model_name)
+    logger.info(f"Inferred tokenizer type: {tokenizer.__class__}")  # if this is wrong, check config.model_type.
 
-    # update config with summarization specific params
+    start_time = time.time()
+    # update config with task specific params
     use_task_specific_params(model, task)
-
-    for batch in tqdm(list(chunks(examples, batch_size))):
-        if "t5" in model_name:
-            batch = [model.config.prefix + text for text in batch]
-        batch = tokenizer(batch, return_tensors="pt", truncation=True, padding="max_length").to(device)
-        input_ids, attention_mask = trim_batch(**batch, pad_token_id=tokenizer.pad_token_id)
+    if prefix is None:
+        prefix = prefix or getattr(model.config, "prefix", "") or ""
+    for examples_chunk in tqdm(list(chunks(examples, batch_size))):
+        examples_chunk = [prefix + text for text in examples_chunk]
+        batch = tokenizer(examples_chunk, return_tensors="pt", truncation=True, padding="longest").to(device)
         summaries = model.generate(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            decoder_start_token_id=decoder_start_token_id,
-            **gen_kwargs,
+            input_ids=batch.input_ids,
+            attention_mask=batch.attention_mask,
+            **generate_kwargs,
         )
         dec = tokenizer.batch_decode(summaries, skip_special_tokens=True, clean_up_tokenization_spaces=False)
         for hypothesis in dec:
             fout.write(hypothesis + "\n")
             fout.flush()
+    fout.close()
+    runtime = int(time.time() - start_time)  # seconds
+    n_obs = len(examples)
+    return dict(n_obs=n_obs, runtime=runtime, seconds_per_sample=round(runtime / n_obs, 4))
 
 
 def run_generate():
@@ -68,29 +77,30 @@ def run_generate():
     parser.add_argument("model_name", type=str, help="like facebook/bart-large-cnn,t5-base, etc.")
     parser.add_argument("input_path", type=str, help="like cnn_dm/test.source")
     parser.add_argument("save_path", type=str, help="where to save summaries")
-
-    parser.add_argument("--reference_path", type=str, required=False, help="like cnn_dm/test_reference_summaries.txt")
-    parser.add_argument("--score_path", type=str, required=False, help="where to save the rouge score in json format")
+    parser.add_argument("--reference_path", type=str, required=False, help="like cnn_dm/test.target")
+    parser.add_argument("--score_path", type=str, required=False, default="metrics.json", help="where to save metrics")
     parser.add_argument("--device", type=str, required=False, default=DEFAULT_DEVICE, help="cuda, cuda:1, cpu etc.")
-    parser.add_argument("--task", type=str, default="summarization", help="typically translation or summarization")
-    parser.add_argument("--bs", type=int, default=8, required=False, help="batch size")
     parser.add_argument(
-        "--decoder_start_token_id",
-        type=int,
-        default=None,
-        required=False,
-        help="decoder_start_token_id (otherwise will look at config)",
+        "--prefix", type=str, required=False, default=None, help="will be added to the begininng of src examples"
     )
+    parser.add_argument("--task", type=str, default="summarization", help="used for task_specific_params + metrics")
+    parser.add_argument("--bs", type=int, default=8, required=False, help="batch size")
     parser.add_argument(
         "--n_obs", type=int, default=-1, required=False, help="How many observations. Defaults to all."
     )
     parser.add_argument("--fp16", action="store_true")
-    args = parser.parse_args()
+    # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
+    args, rest = parser.parse_known_args()
+    parsed = parse_numeric_cl_kwargs(rest)
+    if parsed:
+        print(f"parsed the following generate kwargs: {parsed}")
     examples = [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path).readlines()]
     if args.n_obs > 0:
         examples = examples[: args.n_obs]
     Path(args.save_path).parent.mkdir(exist_ok=True)
-    generate_summaries_or_translations(
+    if args.reference_path is None and Path(args.score_path).exists():
+        warnings.warn(f"score_path {args.score_path} will be overwritten unless you type ctrl-c.")
+    runtime_metrics = generate_summaries_or_translations(
         examples,
         args.save_path,
         args.model_name,
@@ -98,7 +108,8 @@ def run_generate():
         device=args.device,
         fp16=args.fp16,
         task=args.task,
-        decoder_start_token_id=args.decoder_start_token_id,
+        prefix=args.prefix,
+        **parsed,
     )
     if args.reference_path is None:
         return
@@ -107,11 +118,14 @@ def run_generate():
     output_lns = [x.rstrip() for x in open(args.save_path).readlines()]
     reference_lns = [x.rstrip() for x in open(args.reference_path).readlines()][: len(output_lns)]
     scores: dict = score_fn(output_lns, reference_lns)
+    scores.update(runtime_metrics)
     print(scores)
     if args.score_path is not None:
-        json.dump(scores, open(args.score_path, "w+"))
+        json.dump(scores, open(args.score_path, "w"))
     return scores
 
 
 if __name__ == "__main__":
+    # Usage for MT:
+    # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
     run_generate()

examples/seq2seq/test_bash_script.py

@@ -10,9 +10,10 @@ import pytorch_lightning as pl
 import timeout_decorator
 import torch
 
-from transformers import BartForConditionalGeneration
+from transformers import BartForConditionalGeneration, MarianMTModel
 from transformers.testing_utils import slow
 
+from .distillation import BartSummarizationDistiller, distill_main
 from .finetune import SummarizationModule, main
 from .test_seq2seq_examples import CUDA_AVAILABLE, MBART_TINY
 from .utils import load_json
@@ -20,6 +21,7 @@ from .utils import load_json
 
 MODEL_NAME = MBART_TINY
 # TODO(SS): MODEL_NAME = "sshleifer/student_mbart_en_ro_1_1"
+MARIAN_MODEL = "sshleifer/student_marian_en_ro_6_1"
 
 
 @slow
@@ -27,6 +29,7 @@ MODEL_NAME = MBART_TINY
 def test_model_download():
     """This warms up the cache so that we can time the next test without including download time, which varies between machines."""
     BartForConditionalGeneration.from_pretrained(MODEL_NAME)
+    MarianMTModel.from_pretrained(MARIAN_MODEL)
 
 
 @timeout_decorator.timeout(120)
@@ -35,34 +38,30 @@ def test_model_download():
 def test_train_mbart_cc25_enro_script():
     data_dir = "examples/seq2seq/test_data/wmt_en_ro"
     env_vars_to_replace = {
-        "$MAX_LEN": 200,
+        "--fp16_opt_level=O1": "",
+        "$MAX_LEN": 128,
         "$BS": 4,
         "$GAS": 1,
         "$ENRO_DIR": data_dir,
         "facebook/mbart-large-cc25": MODEL_NAME,
-        # 1 encoder and 1 decoder layer from finetuned mbart en-ro. Should be able to start >0 and improve quickly.
-        # Download is 600MB in previous test.
+        # Download is 120MB in previous test.
         "val_check_interval=0.25": "val_check_interval=1.0",
     }
 
     # Clean up bash script
     bash_script = Path("examples/seq2seq/train_mbart_cc25_enro.sh").open().read().split("finetune.py")[1].strip()
-    bash_script = bash_script.replace("\\\n", "").strip().replace("$@", "")
+    bash_script = bash_script.replace("\\\n", "").strip().replace('"$@"', "")
     for k, v in env_vars_to_replace.items():
         bash_script = bash_script.replace(k, str(v))
-    output_dir = tempfile.mkdtemp(prefix="output")
+    output_dir = tempfile.mkdtemp(prefix="output_mbart")
 
-    if CUDA_AVAILABLE:
-        gpus = 1  # torch.cuda.device_count()
-    else:
-        gpus = 0
-    bash_script = bash_script.replace("--fp16", "")
+    bash_script = bash_script.replace("--fp16 ", "")
     testargs = (
         ["finetune.py"]
         + bash_script.split()
         + [
             f"--output_dir={output_dir}",
-            f"--gpus={gpus}",
+            "--gpus=1",
             "--learning_rate=3e-1",
             "--warmup_steps=0",
             "--val_check_interval=1.0",
@@ -82,7 +81,86 @@ def test_train_mbart_cc25_enro_script():
     metrics = load_json(model.metrics_save_path)
     first_step_stats = metrics["val"][0]
     last_step_stats = metrics["val"][-1]
-    assert len(metrics["val"]) == (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check
+    assert len(metrics["val"]) == (args.max_epochs / args.val_check_interval) + 1  # +1 accounts for val_sanity_check
+
+    assert last_step_stats["val_avg_gen_time"] >= 0.01
+
+    assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
+    assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
+    assert isinstance(last_step_stats[f"val_avg_{model.val_metric}"], float)
+
+    # check lightning ckpt can be loaded and has a reasonable statedict
+    contents = os.listdir(output_dir)
+    ckpt_path = [x for x in contents if x.endswith(".ckpt")][0]
+    full_path = os.path.join(args.output_dir, ckpt_path)
+    ckpt = torch.load(full_path, map_location="cpu")
+    expected_key = "model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
+    assert expected_key in ckpt["state_dict"]
+    assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.float32
+
+    # TODO(SS): turn on args.do_predict when PL bug fixed.
+    if args.do_predict:
+        contents = {os.path.basename(p) for p in contents}
+        assert "test_generations.txt" in contents
+        assert "test_results.txt" in contents
+        # assert len(metrics["val"]) ==  desired_n_evals
+        assert len(metrics["test"]) == 1
+
+
+@timeout_decorator.timeout(600)
+@slow
+@pytest.mark.skipif(not CUDA_AVAILABLE, reason="too slow to run on CPU")
+def test_opus_mt_distill_script():
+    data_dir = "examples/seq2seq/test_data/wmt_en_ro"
+    env_vars_to_replace = {
+        "--fp16_opt_level=O1": "",
+        "$MAX_LEN": 128,
+        "$BS": 16,
+        "$GAS": 1,
+        "$ENRO_DIR": data_dir,
+        "$m": "sshleifer/student_marian_en_ro_6_1",
+        "val_check_interval=0.25": "val_check_interval=1.0",
+    }
+
+    # Clean up bash script
+    bash_script = (
+        Path("examples/seq2seq/distil_marian_no_teacher.sh").open().read().split("distillation.py")[1].strip()
+    )
+    bash_script = bash_script.replace("\\\n", "").strip().replace('"$@"', "")
+    bash_script = bash_script.replace("--fp16 ", " ")
+
+    for k, v in env_vars_to_replace.items():
+        bash_script = bash_script.replace(k, str(v))
+    output_dir = tempfile.mkdtemp(prefix="marian_output")
+    bash_script = bash_script.replace("--fp16", "")
+    epochs = 6
+    testargs = (
+        ["distillation.py"]
+        + bash_script.split()
+        + [
+            f"--output_dir={output_dir}",
+            "--gpus=1",
+            "--learning_rate=1e-3",
+            f"--num_train_epochs={epochs}",
+            "--warmup_steps=10",
+            "--val_check_interval=1.0",
+        ]
+    )
+    with patch.object(sys, "argv", testargs):
+        parser = argparse.ArgumentParser()
+        parser = pl.Trainer.add_argparse_args(parser)
+        parser = BartSummarizationDistiller.add_model_specific_args(parser, os.getcwd())
+        args = parser.parse_args()
+        args.do_predict = False
+        # assert args.gpus == gpus THIS BREAKS for multigpu
+
+        model = distill_main(args)
+
+    # Check metrics
+    metrics = load_json(model.metrics_save_path)
+    first_step_stats = metrics["val"][0]
+    last_step_stats = metrics["val"][-1]
+    assert len(metrics["val"]) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check
 
     assert last_step_stats["val_avg_gen_time"] >= 0.01
 

examples/seq2seq/test_seq2seq_examples.py

@@ -10,18 +10,20 @@ from unittest.mock import patch
 import pytest
 import pytorch_lightning as pl
 import torch
-from pytest import param
 from torch.utils.data import DataLoader
 
 import lightning_base
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-from transformers.testing_utils import CaptureStderr, CaptureStdout, require_multigpu
+from transformers import AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer
+from transformers.hf_api import HfApi
+from transformers.modeling_bart import shift_tokens_right
+from transformers.testing_utils import CaptureStderr, CaptureStdout, require_multigpu, require_torch_and_cuda, slow
 
+from .convert_pl_checkpoint_to_hf import convert_pl_to_hf
 from .distillation import distill_main, evaluate_checkpoint
 from .finetune import SummarizationModule, main
 from .pack_dataset import pack_data_dir
 from .run_eval import generate_summaries_or_translations, run_generate
-from .utils import Seq2SeqDataset, TranslationDataset, label_smoothed_nll_loss, lmap, load_json
+from .utils import LegacySeq2SeqDataset, Seq2SeqDataset, label_smoothed_nll_loss, lmap, load_json
 
 
 logging.basicConfig(level=logging.DEBUG)
@@ -29,7 +31,14 @@ logging.basicConfig(level=logging.DEBUG)
 logger = logging.getLogger()
 CUDA_AVAILABLE = torch.cuda.is_available()
 CHEAP_ARGS = {
+    "supervise_forward": True,
+    "normalize_hidden": True,
     "label_smoothing": 0.2,
+    "eval_max_gen_length": None,
+    "eval_beams": 1,
+    "val_metric": "loss",
+    "save_top_k": 1,
+    "adafactor": True,
     "early_stopping_patience": 2,
     "logger_name": "default",
     "length_penalty": 0.5,
@@ -115,15 +124,34 @@ class TestSummarizationDistiller(unittest.TestCase):
         logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
         return cls
 
+    @slow
+    @require_torch_and_cuda
+    def test_hub_configs(self):
+        """I put require_torch_and_cuda cause I only want this to run with self-scheduled."""
+
+        model_list = HfApi().model_list()
+        org = "sshleifer"
+        model_ids = [x.modelId for x in model_list if x.modelId.startswith(org)]
+        allowed_to_be_broken = ["sshleifer/blenderbot-3B", "sshleifer/blenderbot-90M"]
+        failures = []
+        for m in model_ids:
+            if m in allowed_to_be_broken:
+                continue
+            try:
+                AutoConfig.from_pretrained(m)
+            except Exception:
+                failures.append(m)
+        assert not failures, f"The following models could not be loaded through AutoConfig: {failures}"
+
     @require_multigpu
     def test_multigpu(self):
         updates = dict(
             no_teacher=True,
             freeze_encoder=True,
             gpus=2,
-            sortish_sampler=False,
+            sortish_sampler=True,
         )
-        self._test_distiller_cli(updates)
+        self._test_distiller_cli(updates, check_contents=False)
 
     def test_distill_no_teacher(self):
         updates = dict(student_encoder_layers=2, student_decoder_layers=1, no_teacher=True)
@@ -150,6 +178,9 @@ class TestSummarizationDistiller(unittest.TestCase):
         self.assertTrue(Path(out_path).exists())
 
         evaluate_checkpoint(ckpts[0], dest_dir=Path(tempfile.mkdtemp()))
+        out_path_new = tempfile.mkdtemp()
+        convert_pl_to_hf(ckpts[0], transformer_ckpts[0].parent, out_path_new)
+        assert os.path.exists(os.path.join(out_path_new, "pytorch_model.bin"))
 
     def test_loss_fn(self):
         model = AutoModelForSeq2SeqLM.from_pretrained(BART_TINY, return_dict=True)
@@ -186,6 +217,7 @@ class TestSummarizationDistiller(unittest.TestCase):
             tgt_lang="ro_RO",
         )
         model = self._test_distiller_cli(updates, check_contents=False)
+        assert model.model.config.model_type == "mbart"
 
         ckpts = list(Path(model.output_dir).glob("*.ckpt"))
         self.assertEqual(1, len(ckpts))
@@ -233,9 +265,9 @@ class TestSummarizationDistiller(unittest.TestCase):
         if not check_contents:
             return model
         contents = os.listdir(output_dir)
-        ckpt_name = "val_avg_rouge2=0.0000-step_count=2.ckpt"  # "val_avg_rouge2=0.0000-epoch=1.ckpt"  # "epoch=1-val_avg_rouge2=0.0000.ckpt"
         contents = {os.path.basename(p) for p in contents}
-        self.assertIn(ckpt_name, contents)
+        ckpt_files = [p for p in contents if p.endswith("ckpt")]
+        assert len(ckpt_files) > 0
 
         self.assertIn("test_generations.txt", contents)
         self.assertIn("test_results.txt", contents)
@@ -252,13 +284,28 @@ class TestSummarizationDistiller(unittest.TestCase):
 
 
 @pytest.mark.parametrize(["model"], [pytest.param(T5_TINY), pytest.param(BART_TINY), pytest.param(MBART_TINY)])
-def test_run_eval_bart(model):
+def test_run_eval(model):
     input_file_name = Path(tempfile.mkdtemp()) / "utest_input.source"
     output_file_name = input_file_name.parent / "utest_output.txt"
     assert not output_file_name.exists()
     articles = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
     _dump_articles(input_file_name, articles)
-    testargs = ["run_eval.py", model, str(input_file_name), str(output_file_name)]  # TODO: test score_path
+    score_path = str(Path(tempfile.mkdtemp()) / "scores.json")
+    task = "translation_en_to_de" if model == T5_TINY else "summarization"
+    testargs = [
+        "run_eval.py",
+        model,
+        str(input_file_name),
+        str(output_file_name),
+        "--score_path",
+        score_path,
+        "--task",
+        task,
+        "--num_beams",
+        "2",
+        "--length_penalty",
+        "2.0",
+    ]
     with patch.object(sys, "argv", testargs):
         run_generate()
         assert Path(output_file_name).exists()
@@ -439,18 +486,27 @@ def test_pack_dataset():
     assert orig_paths == new_paths
 
 
-@pytest.mark.parametrize(["tok_name"], [pytest.param(MBART_TINY), pytest.param(MARIAN_TINY)])
-def test_mbart_dataset_truncation(tok_name):
+@pytest.mark.parametrize(
+    ["tok_name"],
+    [
+        pytest.param(MBART_TINY),
+        pytest.param(MARIAN_TINY),
+        pytest.param(T5_TINY),
+        pytest.param(BART_TINY),
+        pytest.param("google/pegasus-xsum"),
+    ],
+)
+def test_seq2seq_dataset_truncation(tok_name):
     tokenizer = AutoTokenizer.from_pretrained(tok_name)
     tmp_dir = make_test_data_dir()
     max_len_source = max(len(tokenizer.encode(a)) for a in ARTICLES)
     max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
     max_src_len = 4
     max_tgt_len = 8
-    assert max_len_target > max_src_len  # Truncated
-    assert max_len_source > max_src_len
-    src_lang, tgt_lang = "ro_RO", "de_DE"  # NOT WHAT IT WAS TRAINED ON
-    train_dataset = TranslationDataset(
+    assert max_len_target > max_src_len  # Will be truncated
+    assert max_len_source > max_src_len  # Will be truncated
+    src_lang, tgt_lang = "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
+    train_dataset = Seq2SeqDataset(
         tokenizer,
         data_dir=tmp_dir,
         type_path="train",
@@ -466,10 +522,11 @@ def test_mbart_dataset_truncation(tok_name):
         # show that articles were trimmed.
         assert batch["input_ids"].shape[1] == max_src_len
         # show that targets are the same len
-        assert batch["decoder_input_ids"].shape[1] == max_tgt_len
-        if tok_name == MARIAN_TINY:
+        assert batch["labels"].shape[1] == max_tgt_len
+        if tok_name != MBART_TINY:
             continue
         # check language codes in correct place
+        batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], tokenizer.pad_token_id)
         assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
         assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
         assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
@@ -478,14 +535,14 @@ def test_mbart_dataset_truncation(tok_name):
         break  # No need to test every batch
 
 
-@pytest.mark.parametrize(["tok"], [pytest.param(T5_TINY), pytest.param(BART_TINY), param(MARIAN_TINY)])
-def test_summarization_dataset_truncation(tok):
+@pytest.mark.parametrize(["tok"], [pytest.param(BART_TINY), pytest.param("bert-base-cased")])
+def test_legacy_dataset_truncation(tok):
     tokenizer = AutoTokenizer.from_pretrained(tok)
     tmp_dir = make_test_data_dir()
     max_len_source = max(len(tokenizer.encode(a)) for a in ARTICLES)
     max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
     trunc_target = 4
-    train_dataset = Seq2SeqDataset(
+    train_dataset = LegacySeq2SeqDataset(
         tokenizer,
         data_dir=tmp_dir,
         type_path="train",
@@ -499,6 +556,6 @@ def test_summarization_dataset_truncation(tok):
         assert batch["input_ids"].shape[1] == max_len_source
         assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
         # show that targets were truncated
-        assert batch["decoder_input_ids"].shape[1] == trunc_target  # Truncated
+        assert batch["labels"].shape[1] == trunc_target  # Truncated
         assert max_len_target > trunc_target  # Truncated
         break  # No need to test every batch

examples/seq2seq/utils.py

@@ -1,16 +1,17 @@
 import itertools
 import json
 import linecache
+import math
 import os
 import pickle
-import warnings
 from logging import getLogger
 from pathlib import Path
-from typing import Callable, Dict, Iterable, List
+from typing import Callable, Dict, Iterable, List, Union
 
 import git
 import numpy as np
 import torch
+import torch.distributed as dist
 from rouge_score import rouge_scorer, scoring
 from sacrebleu import corpus_bleu
 from torch import nn
@@ -41,6 +42,7 @@ def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=-100):
 
 
 def encode_line(tokenizer, line, max_length, pad_to_max_length=True, return_tensors="pt"):
+    """Only used by LegacyDataset"""
     extra_kw = {"add_prefix_space": True} if isinstance(tokenizer, BartTokenizer) else {}
     return tokenizer(
         [line],
@@ -75,7 +77,7 @@ def trim_batch(
         return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
 
 
-class Seq2SeqDataset(Dataset):
+class AbstractSeq2SeqDataset(Dataset):
     def __init__(
         self,
         tokenizer,
@@ -102,11 +104,31 @@ class Seq2SeqDataset(Dataset):
         self.pad_token_id = self.tokenizer.pad_token_id
         self.src_lang = src_lang
         self.tgt_lang = tgt_lang
+        self.add_prefix_space = isinstance(self.tokenizer, BartTokenizer)
 
     def __len__(self):
         return len(self.src_lens)
 
+    @staticmethod
+    def get_char_lens(data_file):
+        return [len(x) for x in Path(data_file).open().readlines()]
+
+    def make_sortish_sampler(self, batch_size, distributed=False):
+        if distributed:
+            return DistributedSortishSampler(self, batch_size)
+        else:
+            return SortishSampler(self.src_lens, batch_size)
+
+    def __getitem__(self, item):
+        raise NotImplementedError("You must implement this")
+
+    def collate_fn(self, batch):
+        raise NotImplementedError("You must implement this")
+
+
+class LegacySeq2SeqDataset(AbstractSeq2SeqDataset):
     def __getitem__(self, index) -> Dict[str, torch.Tensor]:
+        """Call tokenizer on src and tgt_lines"""
         index = index + 1  # linecache starts at 1
         source_line = self.prefix + linecache.getline(str(self.src_file), index).rstrip("\n")
         tgt_line = linecache.getline(str(self.tgt_file), index).rstrip("\n")
@@ -121,42 +143,27 @@ class Seq2SeqDataset(Dataset):
         return {
             "input_ids": source_ids,
             "attention_mask": src_mask,
-            "decoder_input_ids": target_ids,
+            "labels": target_ids,
         }
 
-    @staticmethod
-    def get_char_lens(data_file):
-        return [len(x) for x in Path(data_file).open().readlines()]
-
     def collate_fn(self, batch) -> Dict[str, torch.Tensor]:
         input_ids = torch.stack([x["input_ids"] for x in batch])
         masks = torch.stack([x["attention_mask"] for x in batch])
-        target_ids = torch.stack([x["decoder_input_ids"] for x in batch])
+        target_ids = torch.stack([x["labels"] for x in batch])
         pad_token_id = self.pad_token_id
         y = trim_batch(target_ids, pad_token_id)
         source_ids, source_mask = trim_batch(input_ids, pad_token_id, attention_mask=masks)
         batch = {
             "input_ids": source_ids,
             "attention_mask": source_mask,
-            "decoder_input_ids": y,
+            "labels": y,
         }
         return batch
 
-    def make_sortish_sampler(self, batch_size):
-        return SortishSampler(self.src_lens, batch_size)
 
-
-class TranslationDataset(Seq2SeqDataset):
+class Seq2SeqDataset(AbstractSeq2SeqDataset):
     """A dataset that calls prepare_seq2seq_batch."""
 
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        if self.max_source_length != self.max_target_length:
-            warnings.warn(
-                f"Mbart is using sequence lengths {self.max_source_length}, {self.max_target_length}. "
-                f"Imbalanced sequence lengths may be undesired for translation tasks"
-            )
-
     def __getitem__(self, index) -> Dict[str, str]:
         index = index + 1  # linecache starts at 1
         source_line = self.prefix + linecache.getline(str(self.src_file), index).rstrip("\n")
@@ -169,6 +176,7 @@ class TranslationDataset(Seq2SeqDataset):
         }
 
     def collate_fn(self, batch) -> Dict[str, torch.Tensor]:
+        """Call prepare_seq2seq_batch."""
         batch_encoding = self.tokenizer.prepare_seq2seq_batch(
             [x["src_texts"] for x in batch],
             src_lang=self.src_lang,
@@ -176,6 +184,8 @@ class TranslationDataset(Seq2SeqDataset):
             tgt_lang=self.tgt_lang,
             max_length=self.max_source_length,
             max_target_length=self.max_target_length,
+            return_tensors="pt",
+            add_prefix_space=self.add_prefix_space,
         )
         return batch_encoding.data
 
@@ -186,24 +196,77 @@ class SortishSampler(Sampler):
     def __init__(self, data, batch_size):
         self.data, self.bs = data, batch_size
 
-    def key(self, i):
-        return self.data[i]
-
     def __len__(self) -> int:
         return len(self.data)
 
     def __iter__(self):
-        idxs = np.random.permutation(len(self.data))
-        sz = self.bs * 50
-        ck_idx = [idxs[i : i + sz] for i in range(0, len(idxs), sz)]
-        sort_idx = np.concatenate([sorted(s, key=self.key, reverse=True) for s in ck_idx])
-        sz = self.bs
-        ck_idx = [sort_idx[i : i + sz] for i in range(0, len(sort_idx), sz)]
-        max_ck = np.argmax([self.key(ck[0]) for ck in ck_idx])  # find the chunk with the largest key,
-        ck_idx[0], ck_idx[max_ck] = ck_idx[max_ck], ck_idx[0]  # then make sure it goes first.
-        sort_idx = np.concatenate(np.random.permutation(ck_idx[1:])) if len(ck_idx) > 1 else np.array([], dtype=np.int)
-        sort_idx = np.concatenate((ck_idx[0], sort_idx))
-        return iter(sort_idx)
+        return iter(sortish_sampler_indices(self.data, self.bs))
+
+
+def sortish_sampler_indices(data: List, bs: int) -> np.array:
+    "Go through the text data by order of src length with a bit of randomness. From fastai repo."
+
+    def key_fn(i):
+        return data[i]
+
+    idxs = np.random.permutation(len(data))
+    sz = bs * 50
+    ck_idx = [idxs[i : i + sz] for i in range(0, len(idxs), sz)]
+    sort_idx = np.concatenate([sorted(s, key=key_fn, reverse=True) for s in ck_idx])
+    sz = bs
+    ck_idx = [sort_idx[i : i + sz] for i in range(0, len(sort_idx), sz)]
+    max_ck = np.argmax([key_fn(ck[0]) for ck in ck_idx])  # find the chunk with the largest key,
+    ck_idx[0], ck_idx[max_ck] = ck_idx[max_ck], ck_idx[0]  # then make sure it goes first.
+    sort_idx = np.concatenate(np.random.permutation(ck_idx[1:])) if len(ck_idx) > 1 else np.array([], dtype=np.int)
+    sort_idx = np.concatenate((ck_idx[0], sort_idx))
+    return sort_idx
+
+
+class DistributedSortishSampler(Sampler):
+    """Copied from torch DistributedSampler"""
+
+    def __init__(self, dataset, batch_size, num_replicas=None, rank=None):
+        if num_replicas is None:
+            if not dist.is_available():
+                raise RuntimeError("Requires distributed package to be available")
+            num_replicas = dist.get_world_size()
+        if rank is None:
+            if not dist.is_available():
+                raise RuntimeError("Requires distributed package to be available")
+            rank = dist.get_rank()
+        self.dataset = dataset
+        self.num_replicas = num_replicas
+        self.rank = rank
+        self.epoch = 0
+        self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
+        self.total_size = self.num_samples * self.num_replicas
+        self.batch_size = batch_size
+
+    def __iter__(self) -> Iterable:
+        g = torch.Generator()
+        g.manual_seed(self.epoch)
+        available_indices = self.get_indices_for_rank()  # indices[self.rank: self.total_size: self.num_replicas]
+
+        sortish_data = [self.dataset.src_lens[i] for i in available_indices]
+        sortish_indices = sortish_sampler_indices(sortish_data, self.batch_size)
+        indices = [available_indices[i] for i in sortish_indices]
+        assert len(indices) == self.num_samples
+        return iter(indices)
+
+    def get_indices_for_rank(self) -> np.array:
+        indices = list(range(len(self.dataset)))
+        # add extra samples to make it evenly divisible
+        indices += indices[: (self.total_size - len(indices))]
+        assert len(indices) == self.total_size
+        # subsample
+        available_indices = indices[self.rank : self.total_size : self.num_replicas]
+        return available_indices
+
+    def __len__(self):
+        return self.num_samples
+
+    def set_epoch(self, epoch):
+        self.epoch = epoch
 
 
 logger = getLogger(__name__)
@@ -276,7 +339,11 @@ def calculate_rouge(output_lns: List[str], reference_lns: List[str], use_stemmer
     return {k: round(v.mid.fmeasure * 100, 4) for k, v in result.items()}
 
 
+# Utilities for freezing parameters and checking whether they are frozen
+
+
 def freeze_params(model: nn.Module):
+    """Set requires_grad=False for each of model.parameters()"""
     for par in model.parameters():
         par.requires_grad = False
 
@@ -300,3 +367,23 @@ def assert_not_all_frozen(model):
     model_grads: List[bool] = list(grad_status(model))
     npars = len(model_grads)
     assert any(model_grads), f"none of {npars} weights require grad"
+
+
+# CLI Parsing utils
+
+
+def parse_numeric_cl_kwargs(unparsed_args: List[str]) -> Dict[str, Union[int, float]]:
+    """Parse an argv list of unspecified command line args to a dict. Assumes all values are numeric."""
+    result = {}
+    assert len(unparsed_args) % 2 == 0, f"got odd number of unparsed args: {unparsed_args}"
+    num_pairs = len(unparsed_args) // 2
+    for pair_num in range(num_pairs):
+        i = 2 * pair_num
+        assert unparsed_args[i].startswith("--")
+        try:
+            value = int(unparsed_args[i + 1])
+        except ValueError:
+            value = float(unparsed_args[i + 1])  # this can raise another informative ValueError
+
+        result[unparsed_args[i][2:]] = value
+    return result

examples/test_examples.py

@@ -53,7 +53,7 @@ def get_setup_file():
     return args.f
 
 
-def is_cuda_and_apex_avaliable():
+def is_cuda_and_apex_available():
     is_using_cuda = torch.cuda.is_available() and torch_device == "cuda"
     return is_using_cuda and is_apex_available()
 
@@ -85,7 +85,7 @@ class ExamplesTests(TestCasePlus):
         testargs += "--output_dir " + output_dir
         testargs = testargs.split()
 
-        if is_cuda_and_apex_avaliable():
+        if is_cuda_and_apex_available():
             testargs.append("--fp16")
 
         with patch.object(sys, "argv", testargs):
@@ -114,7 +114,9 @@ class ExamplesTests(TestCasePlus):
             --max_seq_length=128
             """.split()
         if torch.cuda.is_available():
-            testargs += ["--fp16", "--gpus=1"]
+            testargs += ["--gpus=1"]
+        if is_cuda_and_apex_available():
+            testargs.append("--fp16")
 
         with patch.object(sys, "argv", testargs):
             result = run_pl_glue.main()
@@ -193,7 +195,7 @@ class ExamplesTests(TestCasePlus):
 
         testargs = ["run_generation.py", "--prompt=Hello", "--length=10", "--seed=42"]
 
-        if is_cuda_and_apex_avaliable():
+        if is_cuda_and_apex_available():
             testargs.append("--fp16")
 
         model_type, model_name = (

examples/text-classification/run_glue.py

@@ -13,7 +13,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-""" Finetuning the library models for sequence classification on GLUE (Bert, XLM, XLNet, RoBERTa, Albert, XLM-RoBERTa)."""
+""" Finetuning the library models for sequence classification on GLUE."""
 
 
 import dataclasses

examples/text-generation/run_generation.py

@@ -61,7 +61,7 @@ MODEL_CLASSES = {
 # Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
 # in https://github.com/rusiaaman/XLNet-gen#methodology
 # and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
-PADDING_TEXT = """In 1991, the remains of Russian Tsar Nicholas II and his family
+PREFIX = """In 1991, the remains of Russian Tsar Nicholas II and his family
 (except for Alexei and Maria) are discovered.
 The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
 remainder of the story. 1883 Western Siberia,
@@ -122,12 +122,14 @@ def prepare_xlm_input(args, model, tokenizer, prompt_text):
 
 
 def prepare_xlnet_input(args, _, tokenizer, prompt_text):
-    prompt_text = (args.padding_text if args.padding_text else PADDING_TEXT) + prompt_text
+    prefix = args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
+    prompt_text = prefix + prompt_text
     return prompt_text
 
 
 def prepare_transfoxl_input(args, _, tokenizer, prompt_text):
-    prompt_text = (args.padding_text if args.padding_text else PADDING_TEXT) + prompt_text
+    prefix = args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
+    prompt_text = prefix + prompt_text
     return prompt_text
 
 
@@ -182,7 +184,8 @@ def main():
     parser.add_argument("--k", type=int, default=0)
     parser.add_argument("--p", type=float, default=0.9)
 
-    parser.add_argument("--padding_text", type=str, default="", help="Padding text for Transfo-XL and XLNet.")
+    parser.add_argument("--prefix", type=str, default="", help="Text added prior to input.")
+    parser.add_argument("--padding_text", type=str, default="", help="Deprecated, the use of `--prefix` is preferred.")
     parser.add_argument("--xlm_language", type=str, default="", help="Optional language when used with the XLM model.")
 
     parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
@@ -241,7 +244,8 @@ def main():
             preprocessed_prompt_text, add_special_tokens=False, return_tensors="pt", **tokenizer_kwargs
         )
     else:
-        encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False, return_tensors="pt")
+        prefix = args.prefix if args.prefix else args.padding_text
+        encoded_prompt = tokenizer.encode(prefix + prompt_text, add_special_tokens=False, return_tensors="pt")
     encoded_prompt = encoded_prompt.to(args.device)
 
     if encoded_prompt.size()[-1] == 0:

examples/token-classification/run_tf_ner.py

@@ -18,6 +18,7 @@
 import logging
 import os
 from dataclasses import dataclass, field
+from importlib import import_module
 from typing import Dict, List, Optional, Tuple
 
 import numpy as np
@@ -32,7 +33,7 @@ from transformers import (
     TFTrainer,
     TFTrainingArguments,
 )
-from utils_ner import Split, TFNerDataset, get_labels
+from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask
 
 
 logger = logging.getLogger(__name__)
@@ -50,6 +51,9 @@ class ModelArguments:
     config_name: Optional[str] = field(
         default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
     )
+    task_type: Optional[str] = field(
+        default="NER", metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}
+    )
     tokenizer_name: Optional[str] = field(
         default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
     )
@@ -102,6 +106,17 @@ def main():
             f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
         )
 
+    module = import_module("tasks")
+
+    try:
+        token_classification_task_clazz = getattr(module, model_args.task_type)
+        token_classification_task: TokenClassificationTask = token_classification_task_clazz()
+    except AttributeError:
+        raise ValueError(
+            f"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
+            f"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"
+        )
+
     # Setup logging
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
@@ -117,7 +132,7 @@ def main():
     logger.info("Training/evaluation parameters %s", training_args)
 
     # Prepare Token Classification task
-    labels = get_labels(data_args.labels)
+    labels = token_classification_task.get_labels(data_args.labels)
     label_map: Dict[int, str] = {i: label for i, label in enumerate(labels)}
     num_labels = len(labels)
 
@@ -150,7 +165,8 @@ def main():
 
     # Get datasets
     train_dataset = (
-        TFNerDataset(
+        TFTokenClassificationDataset(
+            token_classification_task=token_classification_task,
             data_dir=data_args.data_dir,
             tokenizer=tokenizer,
             labels=labels,
@@ -163,7 +179,8 @@ def main():
         else None
     )
     eval_dataset = (
-        TFNerDataset(
+        TFTokenClassificationDataset(
+            token_classification_task=token_classification_task,
             data_dir=data_args.data_dir,
             tokenizer=tokenizer,
             labels=labels,
@@ -233,7 +250,8 @@ def main():
 
     # Predict
     if training_args.do_predict:
-        test_dataset = TFNerDataset(
+        test_dataset = TFTokenClassificationDataset(
+            token_classification_task=token_classification_task,
             data_dir=data_args.data_dir,
             tokenizer=tokenizer,
             labels=labels,

examples/token-classification/utils_ner.py

@@ -276,7 +276,7 @@ if is_torch_available():
 if is_tf_available():
     import tensorflow as tf
 
-    class TFNerDataset:
+    class TFTokenClassificationDataset:
         """
         This will be superseded by a framework-agnostic approach
         soon.

hubconf.py

@@ -28,10 +28,10 @@ def config(*args, **kwargs):
                 config = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased')  # Download configuration from S3 and cache.
                 config = torch.hub.load('huggingface/transformers', 'config', './test/bert_saved_model/')  # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
                 config = torch.hub.load('huggingface/transformers', 'config', './test/bert_saved_model/my_configuration.json')
-                config = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased', output_attention=True, foo=False)
-                assert config.output_attention == True
-                config, unused_kwargs = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased', output_attention=True, foo=False, return_unused_kwargs=True)
-                assert config.output_attention == True
+                config = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased', output_attentions=True, foo=False)
+                assert config.output_attentions == True
+                config, unused_kwargs = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased', output_attentions=True, foo=False, return_unused_kwargs=True)
+                assert config.output_attentions == True
                 assert unused_kwargs == {'foo': False}
 
             """
@@ -61,8 +61,8 @@ def model(*args, **kwargs):
 
             model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
             model = torch.hub.load('huggingface/transformers', 'model', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
-            model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased', output_attention=True)  # Update configuration during loading
-            assert model.config.output_attention == True
+            model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased', output_attentions=True)  # Update configuration during loading
+            assert model.config.output_attentions == True
             # Loading from a TF checkpoint file instead of a PyTorch model (slower)
             config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
             model = torch.hub.load('huggingface/transformers', 'model', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
@@ -80,8 +80,8 @@ def modelWithLMHead(*args, **kwargs):
 
         model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
         model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
-        model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', 'bert-base-uncased', output_attention=True)  # Update configuration during loading
-        assert model.config.output_attention == True
+        model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', 'bert-base-uncased', output_attentions=True)  # Update configuration during loading
+        assert model.config.output_attentions == True
         # Loading from a TF checkpoint file instead of a PyTorch model (slower)
         config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
         model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
@@ -98,8 +98,8 @@ def modelForSequenceClassification(*args, **kwargs):
 
             model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
             model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
-            model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'bert-base-uncased', output_attention=True)  # Update configuration during loading
-            assert model.config.output_attention == True
+            model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'bert-base-uncased', output_attentions=True)  # Update configuration during loading
+            assert model.config.output_attentions == True
             # Loading from a TF checkpoint file instead of a PyTorch model (slower)
             config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
             model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
@@ -117,8 +117,8 @@ def modelForQuestionAnswering(*args, **kwargs):
 
         model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
         model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
-        model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'bert-base-uncased', output_attention=True)  # Update configuration during loading
-        assert model.config.output_attention == True
+        model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'bert-base-uncased', output_attentions=True)  # Update configuration during loading
+        assert model.config.output_attentions == True
         # Loading from a TF checkpoint file instead of a PyTorch model (slower)
         config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
         model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)

model_cards/HooshvareLab/bert-fa-base-uncased/README.md

@@ -0,0 +1,147 @@
+---
+language: fa
+tags:
+- bert-fa
+- bert-persian
+- persian-lm
+license: apache-2.0
+---
+
+# ParsBERT (v2.0)
+A Transformer-based Model for Persian Language Understanding
+
+
+We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes!
+Please follow the [ParsBERT](https://github.com/hooshvare/parsbert) repo for the latest information about previous and current models.
+
+## Introduction
+
+ParsBERT is a monolingual language model based on Google’s BERT architecture. This model is pre-trained on large Persian corpora with various writing styles from numerous subjects (e.g., scientific, novels, news) with more than `3.9M` documents, `73M` sentences, and `1.3B` words.
+ 
+Paper presenting ParsBERT: [arXiv:2005.12515](https://arxiv.org/abs/2005.12515)
+
+## Intended uses & limitations
+
+You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
+be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?search=bert-fa) to look for
+fine-tuned versions on a task that interests you.
+
+
+### How to use
+
+#### TensorFlow 2.0
+
+```python
+from transformers import AutoConfig, AutoTokenizer, TFAutoModel
+
+config = AutoConfig.from_pretrained("HooshvareLab/bert-fa-base-uncased")
+tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-fa-base-uncased")
+model = TFAutoModel.from_pretrained("HooshvareLab/bert-fa-base-uncased")
+
+text = "ما در هوشواره معتقدیم با انتقال صحیح دانش و آگاهی، همه افراد میتوانند از ابزارهای هوشمند استفاده کنند. شعار ما هوش مصنوعی برای همه است."
+tokenizer.tokenize(text)
+
+>>> ['ما', 'در', 'هوش', '##واره', 'معتقدیم', 'با', 'انتقال', 'صحیح', 'دانش', 'و', 'اگاهی', '،', 'همه', 'افراد', 'میتوانند', 'از', 'ابزارهای', 'هوشمند', 'استفاده', 'کنند', '.', 'شعار', 'ما', 'هوش', 'مصنوعی', 'برای', 'همه', 'است', '.']
+```
+
+#### Pytorch
+
+```python
+from transformers import AutoConfig, AutoTokenizer, AutoModel
+
+config = AutoConfig.from_pretrained("HooshvareLab/bert-fa-base-uncased")
+tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-fa-base-uncased")
+model = AutoModel.from_pretrained("HooshvareLab/bert-fa-base-uncased")
+```
+
+## Training
+
+ParsBERT trained on a massive amount of public corpora ([Persian Wikidumps](https://dumps.wikimedia.org/fawiki/), [MirasText](https://github.com/miras-tech/MirasText)) and six other manually crawled text data from a various type of websites ([BigBang Page](https://bigbangpage.com/) `scientific`, [Chetor](https://www.chetor.com/) `lifestyle`, [Eligasht](https://www.eligasht.com/Blog/) `itinerary`,  [Digikala](https://www.digikala.com/mag/) `digital magazine`, [Ted Talks](https://www.ted.com/talks) `general conversational`, Books `novels, storybooks, short stories from old to the contemporary era`).
+
+As a part of ParsBERT methodology, an extensive pre-processing combining POS tagging and WordPiece segmentation was carried out to bring the corpora into a proper format.
+
+## Goals
+Objective goals during training are as below (after 300k steps).
+
+``` bash
+***** Eval results *****
+global_step = 300000
+loss = 1.4392426
+masked_lm_accuracy = 0.6865794
+masked_lm_loss = 1.4469004
+next_sentence_accuracy = 1.0
+next_sentence_loss = 6.534152e-05
+```
+
+
+## Derivative models
+
+### Base Config
+
+#### ParsBERT v2.0 Model
+- [HooshvareLab/bert-fa-base-uncased](https://huggingface.co/HooshvareLab/bert-fa-base-uncased) 
+
+#### ParsBERT v2.0 Sentiment Analysis
+- [HooshvareLab/bert-fa-base-uncased-sentiment-digikala](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-digikala) 
+- [HooshvareLab/bert-fa-base-uncased-sentiment-snappfood](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-snappfood) 
+- [HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary) 
+- [HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-multi](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-multi) 
+
+#### ParsBERT v2.0 Text Classification
+- [HooshvareLab/bert-fa-base-uncased-clf-digimag](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-clf-digimag) 
+- [HooshvareLab/bert-fa-base-uncased-clf-persiannews](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-clf-persiannews) 
+
+#### ParsBERT v2.0 NER 
+- [HooshvareLab/bert-fa-base-uncased-ner-peyma](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-ner-peyma) 
+- [HooshvareLab/bert-fa-base-uncased-ner-arman](https://huggingface.co/HooshvareLab/bert-fa-base-uncased-ner-arman) 
+
+
+## Eval results
+
+ParsBERT is evaluated on three NLP downstream tasks: Sentiment Analysis (SA), Text Classification, and Named Entity Recognition (NER). For this matter and due to insufficient resources, two large datasets for SA and two for text classification were manually composed, which are available for public use and benchmarking. ParsBERT outperformed all other language models, including multilingual BERT and other hybrid deep learning models for all tasks, improving the state-of-the-art performance in Persian language modeling.
+
+
+### Sentiment Analysis (SA) Task
+
+|          Dataset         | ParsBERT v2 | ParsBERT v1 | mBERT | DeepSentiPers |
+|:------------------------:|:-----------:|:-----------:|:-----:|:-------------:|
+|  Digikala User Comments  |    81.72    |    81.74*   | 80.74 |       -       |
+|  SnappFood User Comments |    87.98    |    88.12*   | 87.87 |       -       |
+|  SentiPers (Multi Class) |    71.31*   |    71.11    |   -   |     69.33     |
+| SentiPers (Binary Class) |    92.42*   |    92.13    |   -   |     91.98     |
+
+
+### Text Classification (TC) Task
+
+|      Dataset      | ParsBERT v2 | ParsBERT v1 | mBERT |
+|:-----------------:|:-----------:|:-----------:|:-----:|
+| Digikala Magazine |    93.65*   |    93.59    | 90.72 |
+|    Persian News   |    97.44*   |    97.19    | 95.79 |
+
+
+### Named Entity Recognition (NER) Task
+
+| Dataset | ParsBERT v2 | ParsBERT v1 | mBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF |
+|:-------:|:-----------:|:-----------:|:-----:|:----------:|:------------:|:--------:|:--------------:|:----------:|
+|  PEYMA  |    93.40*   |    93.10    | 86.64 |      -     |     90.59    |     -    |      84.00     |      -     |
+|  ARMAN  |    99.84*   |    98.79    | 95.89 |    89.9    |     84.03    |   86.55  |        -       |    77.45   |
+
+
+
+
+### BibTeX entry and citation info
+
+Please cite in publications as the following:
+
+```bibtex
+@article{ParsBERT,
+    title={ParsBERT: Transformer-based Model for Persian Language Understanding},
+    author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
+    journal={ArXiv},
+    year={2020},
+    volume={abs/2005.12515}
+}
+```
+
+## Questions?
+Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/issues) repo.

model_cards/Naveen-k/KanBERTo/README.md

@@ -0,0 +1,28 @@
+---
+language: kn
+---
+
+# Welcome to KanBERTo (ಕನ್ಬರ್ಟೋ)
+
+## Model Description
+ 
+> This is a small language model for [Kannada](https://en.wikipedia.org/wiki/Kannada) language with 1M data samples taken from
+  [OSCAR page](https://traces1.inria.fr/oscar/files/compressed-orig/kn.txt.gz)
+
+## Training params 
+
+- **Dataset** - 1M data samples are used to train this model from OSCAR page(https://traces1.inria.fr/oscar/) eventhough data set is of 1.7 GB due to resource constraint to train 
+I have picked only 1M data from the total 1.7GB data set. If you are interested in collaboration and have computational resources to train on you are most welcome to do so.
+
+- **Preprocessing** - ByteLevelBPETokenizer is used to tokenize the sentences at character level and vocabulary size is set to 52k as per standard values given by 🤗 
+- **Hyperparameters** - __ByteLevelBPETokenizer__ : vocabulary size = 52_000 and  min_frequency = 2
+                        __Trainer__ :               num_train_epochs=12 - trained for 12 epochs
+                                                    per_gpu_train_batch_size=64 - batch size for the datasamples is 64
+                                                    save_steps=10_000 - save model for every 10k steps
+                                                    save_total_limit=2 - save limit is set for 2
+
+**Intended uses & limitations**
+  this is for anyone who wants to make use of kannada language models for various tasks like language generation, translation and many more use cases.
+
+**Whatever else is helpful!**
+  If you are intersted in collaboration feel free to reach  me [Naveen](mailto:naveen.maltesh@gmail.com)

model_cards/Primer/bart-squad2/README.md

@@ -0,0 +1,63 @@
+---
+language: "en"
+---
+
+# BART-Squad2
+
+## Model description
+
+BART for extractive (span-based) question answering, trained on Squad 2.0.
+
+F1 score of 87.4.
+
+## Intended uses & limitations
+
+Unfortunately, the Huggingface auto-inference API won't run this model, so if you're attempting to try it through the input box above and it complains, don't be discouraged!
+
+#### How to use
+
+Here's a quick way to get question answering running locally:
+
+```python
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+
+tokenizer = AutoTokenizer.from_pretrained("Primer/bart-squad2")
+model = AutoModelForQuestionAnswering.from_pretrained("Primer/bart-squad2")
+model.to('cuda'); model.eval()
+
+def answer(question, text):
+    seq = '<s>' +  question + ' </s> </s> ' + text + ' </s>'
+    tokens = tokenizer.encode_plus(seq, return_tensors='pt', padding='max_length', max_length=1024)
+    input_ids = tokens['input_ids'].to('cuda')
+    attention_mask = tokens['attention_mask'].to('cuda')
+    start, end, _ = model(input_ids, attention_mask=attention_mask)
+    start_idx = int(start.argmax().int())
+    end_idx =  int(end.argmax().int())
+    print(tokenizer.decode(input_ids[0, start_idx:end_idx]).strip())
+    # ^^ it will be an empty string if the model decided "unanswerable"
+
+>>> question = "Where does Tom live?"
+>>> context = "Tom is an engineer in San Francisco."
+>>> answer(question, context)
+San Francisco
+```
+
+(Just drop the `.to('cuda')` stuff if running on CPU).
+
+#### Limitations and bias
+
+Unknown, no further evaluation has been performed. In a technical sense one big limitation is that it's 1.6G 😬
+
+## Training procedure
+
+`run_squad.py` with:
+
+|param|value|
+|---|---|
+|batch size|8|
+|max_seq_length|1024|
+|learning rate|1e-5|
+|epochs|2|
+
+Modified to freeze shared parameters and encoder embeddings.
+

model_cards/Rostlab/prot_bert/README.md

@@ -0,0 +1,141 @@
+---
+language: protein
+tags:
+- protein language model
+datasets:
+- Uniref100
+---
+
+# ProtBert model
+
+Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in
+[this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in
+[this repository](https://github.com/agemagician/ProtTrans). This model is trained on uppercase amino acids: it only works with capital letter amino acids.
+
+
+## Model description
+
+ProtBert is based on Bert model which pretrained on a large corpus of protein sequences in a self-supervised fashion.
+This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of
+publicly available data) with an automatic process to generate inputs and labels from those protein sequences.
+
+One important difference between our Bert model and the original Bert version is the way of dealing with sequences as separate documents.
+This means the Next sentence prediction is not used, as each sequence is treated as a complete document.
+The masking follows the original Bert training with randomly masks 15% of the amino acids in the input. 
+
+At the end, the feature extracted from this model revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein
+shape.
+This implied learning some of the grammar of the language of life realized in protein sequences.
+
+## Intended uses & limitations
+
+The model could be used for protein feature extraction or to be fine-tuned on downstream tasks.
+We have noticed in some tasks you could gain more accuracy by fine-tuning the model rather than using it as a feature extractor.
+
+### How to use
+
+You can use this model directly with a pipeline for masked language modeling:
+
+```python
+>>> from transformers import BertForMaskedLM, BertTokenizer, pipeline
+>>> tokenizer = BertTokenizer.from_pretrained("Rostlab/prot_bert", do_lower_case=False )
+>>> model = BertForMaskedLM.from_pretrained("Rostlab/prot_bert")
+>>> unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer)
+>>> unmasker('D L I P T S S K L V V [MASK] D T S L Q V K K A F F A L V T')
+
+[{'score': 0.11088453233242035,
+  'sequence': '[CLS] D L I P T S S K L V V L D T S L Q V K K A F F A L V T [SEP]',
+  'token': 5,
+  'token_str': 'L'},
+ {'score': 0.08402521163225174,
+  'sequence': '[CLS] D L I P T S S K L V V S D T S L Q V K K A F F A L V T [SEP]',
+  'token': 10,
+  'token_str': 'S'},
+ {'score': 0.07328339666128159,
+  'sequence': '[CLS] D L I P T S S K L V V V D T S L Q V K K A F F A L V T [SEP]',
+  'token': 8,
+  'token_str': 'V'},
+ {'score': 0.06921856850385666,
+  'sequence': '[CLS] D L I P T S S K L V V K D T S L Q V K K A F F A L V T [SEP]',
+  'token': 12,
+  'token_str': 'K'},
+ {'score': 0.06382402777671814,
+  'sequence': '[CLS] D L I P T S S K L V V I D T S L Q V K K A F F A L V T [SEP]',
+  'token': 11,
+  'token_str': 'I'}]
+```
+
+Here is how to use this model to get the features of a given protein sequence in PyTorch:
+
+```python
+from transformers import BertModel, BertTokenizer
+import re
+tokenizer = BertTokenizer.from_pretrained("Rostlab/prot_bert", do_lower_case=False )
+model = BertModel.from_pretrained("Rostlab/prot_bert")
+sequence_Example = "A E T C Z A O"
+sequence_Example = re.sub(r"[UZOB]", "X", sequence_Example)
+encoded_input = tokenizer(sequence_Example, return_tensors='pt')
+output = model(**encoded_input)
+```
+
+## Training data
+
+The ProtBert model was pretrained on [Uniref100](https://www.uniprot.org/downloads), a dataset consisting of 217 million protein sequences.
+
+## Training procedure
+
+### Preprocessing
+
+The protein sequences are uppercased and tokenized using a single space and a vocabulary size of 21. The rare amino acids "U,Z,O,B" were mapped to "X".
+The inputs of the model are then of the form:
+
+```
+[CLS] Protein Sequence A [SEP] Protein Sequence B [SEP]
+```
+
+Furthermore, each protein sequence was treated as a separate document.
+The preprocessing step was performed twice, once for a combined length (2 sequences) of less than 512 amino acids, and another time using a combined length (2 sequences) of less than 2048 amino acids.
+
+The details of the masking procedure for each sequence followed the original Bert model as following:
+- 15% of the amino acids are masked.
+- In 80% of the cases, the masked amino acids are replaced by `[MASK]`.
+- In 10% of the cases, the masked amino acids are replaced by a random amino acid (different) from the one they replace.
+- In the 10% remaining cases, the masked amino acids are left as is.
+
+### Pretraining
+
+The model was trained on a single TPU Pod V3-512 for 400k steps in total.
+300K steps using sequence length 512 (batch size 15k), and 100K steps using sequence length 2048 (batch size 2.5k).
+The optimizer used is Lamb with a learning rate of 0.002, a weight decay of 0.01, learning rate warmup for 40k steps and linear decay of the learning rate after.
+
+## Evaluation results
+
+When fine-tuned on downstream tasks, this model achieves the following results:
+
+Test results :
+
+| Task/Dataset | secondary structure (3-states) | secondary structure (8-states)  |  Localization | Membrane  |
+|:-----:|:-----:|:-----:|:-----:|:-----:|
+|   CASP12  | 75 | 63 |    |    |
+|   TS115   | 83 | 72 |    |    | 
+|   CB513   | 81 | 66 |    |    |
+|  DeepLoc  |    |    | 79 | 91 |
+
+### BibTeX entry and citation info
+
+```bibtex
+@article {Elnaggar2020.07.12.199554,
+	author = {Elnaggar, Ahmed and Heinzinger, Michael and Dallago, Christian and Rehawi, Ghalia and Wang, Yu and Jones, Llion and Gibbs, Tom and Feher, Tamas and Angerer, Christoph and Steinegger, Martin and BHOWMIK, DEBSINDHU and Rost, Burkhard},
+	title = {ProtTrans: Towards Cracking the Language of Life{\textquoteright}s Code Through Self-Supervised Deep Learning and High Performance Computing},
+	elocation-id = {2020.07.12.199554},
+	year = {2020},
+	doi = {10.1101/2020.07.12.199554},
+	publisher = {Cold Spring Harbor Laboratory},
+	abstract = {Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models (Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112 times the entire English Wikipedia). The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs) and one TPU Pod (V3-512 or V3-1024). We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure (3-states: Q3=76-84, 8 states: Q8=65-73), sub-cellular localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89). Dimensionality reduction revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC to larger data sets slightly reduced the gap between models trained on evolutionary information and LMs. Availability ProtTrans: \<a href="https://github.com/agemagician/ProtTrans"\>https://github.com/agemagician/ProtTrans\</a\>Competing Interest StatementThe authors have declared no competing interest.},
+	URL = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554},
+	eprint = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554.full.pdf},
+	journal = {bioRxiv}
+}
+```
+
+> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)

model_cards/Rostlab/prot_bert_bfd/README.md

@@ -0,0 +1,141 @@
+---
+language: protein
+tags:
+- protein language model
+datasets:
+- BFD
+---
+
+# ProtBert-BFD model
+
+Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in
+[this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in
+[this repository](https://github.com/agemagician/ProtTrans). This model is trained on uppercase amino acids: it only works with capital letter amino acids.
+
+
+## Model description
+
+ProtBert-BFD is based on Bert model which pretrained on a large corpus of protein sequences in a self-supervised fashion.
+This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of
+publicly available data) with an automatic process to generate inputs and labels from those protein sequences.
+
+One important difference between our Bert model and the original Bert version is the way of dealing with sequences as separate documents
+This means the Next sentence prediction is not used, as each sequence is treated as a complete document.
+The masking follows the original Bert training with randomly masks 15% of the amino acids in the input. 
+
+At the end, the feature extracted from this model revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein
+shape.
+This implied learning some of the grammar of the language of life realized in protein sequences.
+
+## Intended uses & limitations
+
+The model could be used for protein feature extraction or to be fine-tuned on downstream tasks.
+We have noticed in some tasks you could gain more accuracy by fine-tuning the model rather than using it as a feature extractor.
+
+### How to use
+
+You can use this model directly with a pipeline for masked language modeling:
+
+```python
+>>> from transformers import BertForMaskedLM, BertTokenizer, pipeline
+>>> tokenizer = BertTokenizer.from_pretrained('Rostlab/prot_bert_bfd', do_lower_case=False )
+>>> model = BertForMaskedLM.from_pretrained("Rostlab/prot_bert_bfd")
+>>> unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer)
+>>> unmasker('D L I P T S S K L V V [MASK] D T S L Q V K K A F F A L V T')
+
+[{'score': 0.1165614128112793,
+  'sequence': '[CLS] D L I P T S S K L V V L D T S L Q V K K A F F A L V T [SEP]',
+  'token': 5,
+  'token_str': 'L'},
+ {'score': 0.08976086974143982,
+  'sequence': '[CLS] D L I P T S S K L V V V D T S L Q V K K A F F A L V T [SEP]',
+  'token': 8,
+  'token_str': 'V'},
+ {'score': 0.08864385634660721,
+  'sequence': '[CLS] D L I P T S S K L V V S D T S L Q V K K A F F A L V T [SEP]',
+  'token': 10,
+  'token_str': 'S'},
+ {'score': 0.06227643042802811,
+  'sequence': '[CLS] D L I P T S S K L V V A D T S L Q V K K A F F A L V T [SEP]',
+  'token': 6,
+  'token_str': 'A'},
+ {'score': 0.06194969266653061,
+  'sequence': '[CLS] D L I P T S S K L V V T D T S L Q V K K A F F A L V T [SEP]',
+  'token': 15,
+  'token_str': 'T'}]
+```
+
+Here is how to use this model to get the features of a given protein sequence in PyTorch:
+
+```python
+from transformers import BertModel, BertTokenizer
+import re
+tokenizer = BertTokenizer.from_pretrained('Rostlab/prot_bert_bfd', do_lower_case=False )
+model = BertModel.from_pretrained("Rostlab/prot_bert_bfd")
+sequence_Example = "A E T C Z A O"
+sequence_Example = re.sub(r"[UZOB]", "X", sequence_Example)
+encoded_input = tokenizer(sequence_Example, return_tensors='pt')
+output = model(**encoded_input)
+```
+
+## Training data
+
+The ProtBert-BFD model was pretrained on [BFD](https://bfd.mmseqs.com/), a dataset consisting of 2.1 billion protein sequences.
+
+## Training procedure
+
+### Preprocessing
+
+The protein sequences are uppercased and tokenized using a single space and a vocabulary size of 21.
+The inputs of the model are then of the form:
+
+```
+[CLS] Protein Sequence A [SEP] Protein Sequence B [SEP]
+```
+
+Furthermore, each protein sequence was treated as a separate document.
+The preprocessing step was performed twice, once for a combined length (2 sequences) of less than 512 amino acids, and another time using a combined length (2 sequences) of less than 2048 amino acids.
+
+The details of the masking procedure for each sequence followed the original Bert model as following:
+- 15% of the amino acids are masked.
+- In 80% of the cases, the masked amino acids are replaced by `[MASK]`.
+- In 10% of the cases, the masked amino acids are replaced by a random amino acid (different) from the one they replace.
+- In the 10% remaining cases, the masked amino acids are left as is.
+
+### Pretraining
+
+The model was trained on a single TPU Pod V3-1024 for one million steps in total.
+800k steps using sequence length 512 (batch size 32k), and 200K steps using sequence length 2048 (batch size 6k).
+The optimizer used is Lamb with a learning rate of 0.002, a weight decay of 0.01, learning rate warmup for 140k steps and linear decay of the learning rate after.
+
+## Evaluation results
+
+When fine-tuned on downstream tasks, this model achieves the following results:
+
+Test results :
+
+| Task/Dataset | secondary structure (3-states) | secondary structure (8-states)  |  Localization | Membrane  |
+|:-----:|:-----:|:-----:|:-----:|:-----:|
+|   CASP12  | 76 | 65 |    |    |
+|   TS115   | 84 | 73 |    |    | 
+|   CB513   | 83 | 70 |    |    |
+|  DeepLoc  |    |    | 78 | 91 |
+
+### BibTeX entry and citation info
+
+```bibtex
+@article {Elnaggar2020.07.12.199554,
+	author = {Elnaggar, Ahmed and Heinzinger, Michael and Dallago, Christian and Rehawi, Ghalia and Wang, Yu and Jones, Llion and Gibbs, Tom and Feher, Tamas and Angerer, Christoph and Steinegger, Martin and BHOWMIK, DEBSINDHU and Rost, Burkhard},
+	title = {ProtTrans: Towards Cracking the Language of Life{\textquoteright}s Code Through Self-Supervised Deep Learning and High Performance Computing},
+	elocation-id = {2020.07.12.199554},
+	year = {2020},
+	doi = {10.1101/2020.07.12.199554},
+	publisher = {Cold Spring Harbor Laboratory},
+	abstract = {Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models (Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112 times the entire English Wikipedia). The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs) and one TPU Pod (V3-512 or V3-1024). We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure (3-states: Q3=76-84, 8 states: Q8=65-73), sub-cellular localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89). Dimensionality reduction revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC to larger data sets slightly reduced the gap between models trained on evolutionary information and LMs. Availability ProtTrans: \<a href="https://github.com/agemagician/ProtTrans"\>https://github.com/agemagician/ProtTrans\</a\>Competing Interest StatementThe authors have declared no competing interest.},
+	URL = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554},
+	eprint = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554.full.pdf},
+	journal = {bioRxiv}
+}
+```
+
+> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)

model_cards/SZTAKI-HLT/hubert-base-cc/README.md

@@ -0,0 +1,46 @@
+---
+language: hu
+license: apache-2.0
+datasets:
+- common_crawl
+- wikipedia
+---
+
+# huBERT base model (cased)
+
+## Model description
+
+Cased BERT model for Hungarian, trained on the (filtered, deduplicated) Hungarian subset of the Common Crawl and a snapshot of the Hungarian Wikipedia.
+
+## Intended uses & limitations
+
+The model can be used as any other (cased) BERT model. It has been tested on the chunking and
+named entity recognition tasks and set a new state-of-the-art on the former.
+
+## Training
+
+Details of the training data and procedure can be found in the PhD thesis linked below. (With the caveat that it only contains preliminary results
+based on the Wikipedia subcorpus. Evaluation of the full model will appear in a future paper.)
+
+## Eval results
+
+When fine-tuned (via `BertForTokenClassification`) on chunking and NER, the model outperforms multilingual BERT, achieves state-of-the-art results on the
+former task and comes within 0.5% F1 to the SotA on the latter. The exact scores are
+
+| NER | Minimal NP | Maximal NP |
+|-----|------------|------------|
+| 97.62% | **97.14%** | **96.97%** |
+
+### BibTeX entry and citation info
+
+The training corpus, parameters and the evaluation methods are discussed in the
+[following PhD thesis](https://hlt.bme.hu/en/publ/nemeskey_2020):
+
+```bibtex
+@PhDThesis{ Nemeskey:2020,
+  author = {Nemeskey, Dávid Márk},
+  title  = {Natural Language Processing Methods for Language Modeling},
+  year   = {2020},
+  school = {E\"otv\"os Lor\'and University}
+}
+```

model_cards/akhooli/mbart-large-cc25-ar-en/README.md

@@ -0,0 +1,15 @@
+---
+tags:
+- translation
+
+language:
+- ar
+- en
+
+license: mit
+---
+### mbart-large-ar-en
+This is mbart-large-cc25, finetuned on a subset of the OPUS corpus for ar_en.   
+Usage: see [example notebook](https://colab.research.google.com/drive/1I6RFOWMaTpPBX7saJYjnSTddW0TD6H1t?usp=sharing)  
+Note: model has limited training set, not fully trained (do not use for production).   
+Other models by me: [Abed Khooli](https://huggingface.co/akhooli)  

model_cards/akhooli/mbart-large-cc25-en-ar/README.md

@@ -0,0 +1,14 @@
+---
+tags:
+- translation
+
+language:
+- en
+- ar
+
+license: mit
+---
+### mbart-large-en-ar
+This is mbart-large-cc25, finetuned on a subset of the UN corpus for en_ar.  
+Usage: see [example notebook](https://colab.research.google.com/drive/1I6RFOWMaTpPBX7saJYjnSTddW0TD6H1t?usp=sharing) 
+Note: model has limited training set, not fully trained (do not use for production). 

model_cards/akhooli/xlm-r-large-arabic-sent/README.md

@@ -0,0 +1,13 @@
+---
+
+language:
+- ar
+- en
+
+license: mit
+---
+### xlm-r-large-arabic-sent 
+Multilingual sentiment classification (Label_0: mixed, Label_1: negative, Label_2: positive) of Arabic reviews by fine-tuning XLM-Roberta-Large. 
+Zero shot classification of other languages (also works in mixed languages - ex. Arabic & English). Mixed category is not accurate and may confuse other 
+classes (was based on a rate of 3 out of 5 in reviews).  
+Usage: see last section in this [Colab notebook](https://lnkd.in/d3bCFyZ)

model_cards/amberoad/bert-multilingual-passage-reranking-msmarco/README.md

@@ -0,0 +1,103 @@
+---
+language: multilingual
+thumbnail: "https://amberoad.de/images/logo_text.png"
+tags:
+- msmarco
+- multilingual
+- passage reranking
+license:  Apache-2.0
+datasets:
+- msmarco
+metrics:
+- MRR
+widget:
+- query: "What is a corporation?"
+  passage: "A company is incorporated in a specific nation, often within the bounds of a smaller subset of that nation, such as a state or province. The corporation is then governed by the laws of incorporation in that state. A corporation may issue stock, either private or public, or may be classified as a non-stock corporation. If stock is issued, the corporation will usually be governed by its shareholders, either directly or indirectly."
+---
+
+# Passage Reranking Multilingual BERT 🔃 🌍
+
+
+
+## Model description
+**Input:** Supports over 100 Languages. See [List of supported languages](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages) for all available.
+
+**Purpose:** This module takes a search query [1] and a passage [2] and calculates if the passage matches the query. 
+It can be used as an improvement for Elasticsearch Results and boosts the relevancy by up to 100%. 
+
+**Architecture:** On top of BERT there is a Densly Connected NN which takes the 768 Dimensional [CLS] Token as input and provides the output ([Arxiv](https://arxiv.org/abs/1901.04085)).
+
+**Output:** Just a single value between between -10 and 10. Better matching query,passage pairs tend to have a higher a score.
+
+
+
+## Intended uses & limitations
+Both query[1] and passage[2] have to fit in 512 Tokens.
+As you normally want to rerank the first dozens of search results keep in mind the inference time of approximately 300 ms/query.
+
+#### How to use
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("amberoad/bert-multilingual-passage-reranking-msmarco")
+
+model = AutoModelForSequenceClassification.from_pretrained("amberoad/bert-multilingual-passage-reranking-msmarco")
+```
+
+This Model can be used as a drop-in replacement in the [Nboost Library](https://github.com/koursaros-ai/nboost)
+Through this you can directly improve your Elasticsearch Results without any coding. 
+
+
+## Training data
+
+This model is trained using the [**Microsoft MS Marco Dataset**](https://microsoft.github.io/msmarco/ "Microsoft MS Marco"). This training dataset contains approximately 400M tuples of a query, relevant and non-relevant passages. All datasets used for training and evaluating are listed in this [table](https://github.com/microsoft/MSMARCO-Passage-Ranking#data-information-and-formating). The used dataset for training is called *Train Triples Large*, while the evaluation was made on *Top 1000 Dev*. There are 6,900 queries in total in the development dataset, where each query is mapped to top 1,000 passage retrieved using BM25 from MS MARCO corpus. 
+
+## Training procedure
+
+The training is performed the same way as stated in this [README](https://github.com/nyu-dl/dl4marco-bert "NYU Github"). See their excellent Paper on [Arxiv](https://arxiv.org/abs/1901.04085). 
+
+We changed the BERT Model from an English only to the default BERT Multilingual uncased Model from [Google](https://huggingface.co/bert-base-multilingual-uncased).
+
+Training was done 400 000 Steps. This equaled 12 hours an a TPU V3-8.
+
+
+## Eval results
+
+We see nearly similar performance than the English only Model in the English [Bing Queries Dataset](http://www.msmarco.org/). Although the training data is English only internal Tests on private data showed a far higher accurancy in German than all other available models.
+
+
+
+Fine-tuned Models                                                                   | Dependency                                                                   | Eval Set                                                           | Search Boost<a href='#benchmarks'> | Speed on GPU
+----------------------------------------------------------------------------------- | ---------------------------------------------------------------------------- | ------------------------------------------------------------------ | ----------------------------------------------------- | ----------------------------------
+**`amberoad/Multilingual-uncased-MSMARCO`**  (This Model)                                       | <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-blue"/>          |  <a href ='http://www.msmarco.org/'>bing queries</a>               | **+61%** <sub><sup>(0.29 vs 0.18)</sup></sub>         | ~300 ms/query <a href='#footnotes'>
+`nboost/pt-tinybert-msmarco`                                          | <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-red"/>          |  <a href ='http://www.msmarco.org/'>bing queries</a>               | **+45%** <sub><sup>(0.26 vs 0.18)</sup></sub>         | ~50ms/query <a href='#footnotes'>
+`nboost/pt-bert-base-uncased-msmarco`                                               | <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-red"/>          | <a href ='http://www.msmarco.org/'>bing queries</a>                | **+62%** <sub><sup>(0.29 vs 0.18)</sup></sub>         | ~300 ms/query<a href='#footnotes'>
+`nboost/pt-bert-large-msmarco`                                                      | <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-red"/>          | <a href ='http://www.msmarco.org/'>bing queries</a>                | **+77%** <sub><sup>(0.32 vs 0.18)</sup></sub>         | -
+`nboost/pt-biobert-base-msmarco`                                                    | <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-red"/>          | <a href ='https://github.com/naver/biobert-pretrained'>biomed</a>  | **+66%** <sub><sup>(0.17 vs 0.10)</sup></sub>         | ~300 ms/query<a href='#footnotes'>
+
+This table is taken from [nboost](https://github.com/koursaros-ai/nboost) and extended by the first line. 
+
+
+
+## Contact Infos
+
+![](https://amberoad.de/images/logo_text.png)
+
+Amberoad is a company focussing on Search and Business Intelligence. 
+We provide you: 
+* Advanced Internal Company Search Engines thorugh NLP
+* External Search Egnines: Find Competitors, Customers, Suppliers 
+
+**Get in Contact now to benefit from our Expertise:**
+
+The training and evaluation was performed by [**Philipp Reissel**](https://reissel.eu/) and [**Igli Manaj**](https://github.com/iglimanaj) 
+
+ [![Amberoad](https://i.stack.imgur.com/gVE0j.png) Linkedin](https://de.linkedin.com/company/amberoad) | <svg xmlns="http://www.w3.org/2000/svg" x="0px" y="0px"
+width="32" height="32"
+viewBox="0 0 172 172"
+style=" fill:#000000;"><g fill="none" fill-rule="nonzero" stroke="none" stroke-width="1" stroke-linecap="butt" stroke-linejoin="miter" stroke-miterlimit="10" stroke-dasharray="" stroke-dashoffset="0" font-family="none" font-weight="none" font-size="none" text-anchor="none" style="mix-blend-mode: normal"><path d="M0,172v-172h172v172z" fill="none"></path><g fill="#e67e22"><path d="M37.625,21.5v86h96.75v-86h-5.375zM48.375,32.25h10.75v10.75h-10.75zM69.875,32.25h10.75v10.75h-10.75zM91.375,32.25h32.25v10.75h-32.25zM48.375,53.75h75.25v43h-75.25zM80.625,112.875v17.61572c-1.61558,0.93921 -2.94506,2.2687 -3.88428,3.88428h-49.86572v10.75h49.86572c1.8612,3.20153 5.28744,5.375 9.25928,5.375c3.97183,0 7.39808,-2.17347 9.25928,-5.375h49.86572v-10.75h-49.86572c-0.93921,-1.61558 -2.2687,-2.94506 -3.88428,-3.88428v-17.61572z"></path></g></g></svg>[Homepage](https://de.linkedin.com/company/amberoad) |  [Email](info@amberoad.de)
+
+
+
+

model_cards/dccuchile/bert-base-spanish-wwm-cased/README.md

@@ -0,0 +1,3 @@
+---
+language: es
+---

model_cards/dccuchile/bert-base-spanish-wwm-uncased/README.md

@@ -0,0 +1,3 @@
+---
+language: es
+---

model_cards/etalab-ia/camembert-base-squadFR-fquad-piaf/README.md

@@ -1,7 +1,7 @@
 ---
 language: fr
 datasets:
-- PIAF
+- piaf
 - FQuAD
 - SQuAD-FR
 widget:

model_cards/flexudy/t5-base-multi-sentence-doctor/README.md

@@ -0,0 +1,109 @@
+![avatar](sent-banner.png)
+
+# Sentence-Doctor
+Sentence doctor is a T5 model that attempts to correct the errors or mistakes found in sentences. Model works on English, German and French text.
+
+## 1. Problem:
+Many NLP models depend on tasks like *Text Extraction Libraries, OCR, Speech to Text libraries* and **Sentence Boundary Detection**
+As a consequence errors caused by these tasks in your NLP pipeline can affect the quality of models in applications. Especially since models are often trained on **clean** input.
+
+## 2. Solution:
+Here we provide a model that **attempts** to reconstruct sentences based on the its context (sourrounding text). The task is pretty straightforward:
+* `Given an "erroneous" sentence, and its context, reconstruct the "intended" sentence`.
+
+## 3. Use Cases:
+* Attempt to repair noisy sentences that where extracted with OCR software or text extractors.
+* Attempt to repair sentence boundaries.
+  * Example (in German): **Input: "und ich bin im**", 
+    * Prefix_Context: "Hallo! Mein Name ist John", Postfix_Context: "Januar 1990 geboren."
+    * Output: "John und ich bin im Jahr 1990 geboren"
+* Possibly sentence level spelling correction -- Although this is not the intended use.
+ * Input: "I went to church **las yesteday**" => Output: "I went to church last Sunday".
+ 
+## 4. Disclaimer
+Note how we always emphises on the word *attempt*. The current version of the model was only trained on **150K** sentences from the tatoeba dataset: https://tatoeba.org/eng. (50K per language -- En, Fr, De).
+Hence, we strongly encourage you to finetune the model on your dataset. We might release a version trained on more data.
+
+## 5. Datasets
+We generated synthetic data from the tatoeba dataset: https://tatoeba.org/eng. Randomly applying different transformations on words and characters based on some probabilities. The datasets are available in the data folder (where **sentence_doctor_dataset_300K** is a larger dataset with 100K sentences for each language).
+
+## 6. Usage
+
+### 6.1 Preprocessing
+* Let us assume we have the following text (Note that there are no punctuation marks in the text):
+
+```python
+text = "That is my job I am a medical doctor I save lives"
+```
+* You decided extract the sentences and for some obscure reason, you obtained these sentences:
+
+```python
+sentences = ["That is my job I a", "m a medical doct", "I save lives"]
+```
+* You now wish to correct the sentence **"m a medical doct"**.
+
+Here is the single preprocessing step for the model:
+
+```python
+input_text = "repair_sentence: " + sentences[1] + " context: {" + sentences[0] + "}{" + sentences[2] + "} </s>"
+```
+
+**Explanation**:</br>
+* We are telling the model to repair the sentence with the prefix "repair_sentence: "
+* Then append the sentence we want to repair **sentence[1]** which is "m a medical doct"
+* Next we give some context to the model. In the case, the context is some text that occured before the sentence and some text that appeard after the sentence in the original text.
+ * To do that, we append the keyword "context :"
+ * Append **{sentence[0]}** "{That is my job I a}". (Note how it is sourrounded by curly braces).
+ * Append **{sentence[2]}** "{I save lives}". 
+* At last we tell the model this is the end of the input with </s>.
+
+```python
+print(input_text) # repair_sentence: m a medical doct context: {That is my job I a}{or I save lives} </s>
+```
+
+<br/>
+
+**The context is optional**, so the input could also be ```repair_sentence: m a medical doct context: {}{} </s>```
+
+### 6.2 Inference
+
+```python
+
+from transformers import AutoTokenizer, AutoModelWithLMHead
+
+tokenizer = AutoTokenizer.from_pretrained("flexudy/t5-base-multi-sentence-doctor")
+
+model = AutoModelWithLMHead.from_pretrained("flexudy/t5-base-multi-sentence-doctor")
+
+input_text = "repair_sentence: m a medical doct context: {That is my job I a}{or I save lives} </s>"
+
+input_ids = tokenizer.encode(input_text, return_tensors="pt")
+
+outputs = model.generate(input_ids, max_length=32, num_beams=1)
+
+sentence = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
+
+assert sentence == "I am a medical doctor."
+```
+
+## 7. Fine-tuning
+We also provide a script `train_any_t5_task.py` that might help you fine-tune any Text2Text Task with T5. We added #TODO comments all over to help you use train with ease. For example:
+
+```python
+# TODO Set your training epochs
+config.TRAIN_EPOCHS = 3
+``` 
+If you don't want to read the #TODO comments, just pass in your data like this
+
+```python
+# TODO Where is your data ? Enter the path
+trainer.start("data/sentence_doctor_dataset_300.csv")
+```
+and voila!! Please feel free to correct any mistakes in the code and make a pull request.
+
+## 8. Attribution
+* [Huggingface](https://huggingface.co/) transformer lib for making this possible
+* Abhishek Kumar Mishra's transformer [tutorial](https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_summarization_wandb.ipynb) on text summarisation. Our training code is just a modified version of their code. So many thanks.
+* We finetuned this model from the huggingface hub: WikinewsSum/t5-base-multi-combine-wiki-news. Thanks to the [authors](https://huggingface.co/WikinewsSum)
+* We also read a lot of work from [Suraj Patil](https://github.com/patil-suraj)
+* No one has been forgotten, hopefully :)

model_cards/google/bert2bert_L-24_wmt_de_en/README.md

@@ -0,0 +1,37 @@
+---
+language: 
+- en
+- de
+license: apache-2.0
+datasets:
+- wmt14
+---
+
+# bert2bert_L-24_wmt_de_en EncoderDecoder model
+
+The model was introduced in 
+[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev/google/bertseq2seq/bert24_de_en/1). 
+
+The model is an encoder-decoder model that was initialized on the `bert-large` checkpoints for both the encoder 
+and decoder and fine-tuned on German to English translation on the WMT dataset, which is linked above.
+
+Disclaimer: The model card has been written by the Hugging Face team.
+
+## How to use
+
+You can use this model for translation, *e.g.*
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/bert2bert_L-24_wmt_de_en", pad_token="<pad>", eos_token="</s>", bos_token="<s>")
+model = AutoModelForSeq2SeqLM.from_pretrained("google/bert2bert_L-24_wmt_de_en")
+
+sentence = "Willst du einen Kaffee trinken gehen mit mir?"
+
+input_ids = tokenizer(sentence, return_tensors="pt", add_special_tokens=False).input_ids
+output_ids = model.generate(input_ids)[0]
+print(tokenizer.decode(output_ids, skip_special_tokens=True))
+# should output
+# Want to drink a kaffee go with me? .
+```

model_cards/google/bert2bert_L-24_wmt_en_de/README.md

@@ -0,0 +1,36 @@
+---
+language: 
+- en
+- de
+license: apache-2.0
+datasets:
+- wmt14
+---
+
+# bert2bert_L-24_wmt_en_de EncoderDecoder model
+
+The model was introduced in 
+[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev/google/bertseq2seq/bert24_en_de/1). 
+
+The model is an encoder-decoder model that was initialized on the `bert-large` checkpoints for both the encoder 
+and decoder and fine-tuned on English to German translation on the WMT dataset, which is linked above.
+
+Disclaimer: The model card has been written by the Hugging Face team.
+
+## How to use
+
+You can use this model for translation, *e.g.*
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/bert2bert_L-24_wmt_en_de", pad_token="<pad>", eos_token="</s>", bos_token="<s>")
+model = AutoModelForSeq2SeqLM.from_pretrained("google/bert2bert_L-24_wmt_en_de")
+
+sentence = "Would you like to grab a coffee with me this week?"
+
+input_ids = tokenizer(sentence, return_tensors="pt", add_special_tokens=False).input_ids
+output_ids = model.generate(input_ids)[0]
+print(tokenizer.decode(output_ids, skip_special_tokens=True))
+# should output
+# Möchten Sie diese Woche einen Kaffee mit mir schnappen?

model_cards/google/roberta2roberta_L-24_bbc/README.md

@@ -0,0 +1,56 @@
+---
+language: en
+license: apache-2.0
+datasets:
+- xsum
+---
+
+# Roberta2Roberta_L-24_bbc EncoderDecoder model
+
+The model was introduced in 
+[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev/google/bertseq2seq/roberta24_bbc/1). 
+
+The model is an encoder-decoder model that was initialized on the `roberta-large` checkpoints for both the encoder 
+and decoder and fine-tuned on extreme summarization on the BBC XSum dataset, which is linked above.
+
+Disclaimer: The model card has been written by the Hugging Face team.
+
+## How to use
+
+You can use this model for extreme summarization, *e.g.*
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_bbc")
+model = AutoModelForSeq2SeqLM.from_pretrained("google/roberta2roberta_L-24_bbc")
+
+article = """The problem is affecting people using the older
+versions of the PlayStation 3, called the "Fat"
+model.The problem isn't affecting the newer PS3
+Slim systems that have been on sale since
+September last year.Sony have also said they are
+aiming to have the problem fixed shortly but is
+advising some users to avoid using their console
+for the time being."We hope to resolve this
+problem within the next 24 hours," a statement
+reads. "In the meantime, if you have a model other
+than the new slim PS3, we advise that you do not
+use your PS3 system, as doing so may result in
+errors in some functionality, such as recording
+obtained trophies, and not being able to restore
+certain data."We believe we have identified that
+this problem is being caused by a bug in the clock
+functionality incorporated in the system."The
+PlayStation Network is used by millions of people
+around the world.It allows users to play their
+friends at games like Fifa over the internet and
+also do things like download software or visit
+online stores."""
+
+input_ids = tokenizer(article, return_tensors="pt").input_ids
+output_ids = model.generate(input_ids)[0]
+print(tokenizer.decode(output_ids, skip_special_tokens=True))
+# should output
+# Some Sony PlayStation gamers are being advised to stay away from the network because of a problem with the PlayStation 3 network.
+```

model_cards/google/roberta2roberta_L-24_cnn_daily_mail/README.md

@@ -0,0 +1,73 @@
+---
+language: en
+license: apache-2.0
+datasets:
+- cnn_dailymail
+---
+
+# Roberta2Roberta_L-24_cnn_daily_mail EncoderDecoder model
+
+The model was introduced in 
+[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev/google/bertseq2seq/roberta24_cnndm/1). 
+
+The model is an encoder-decoder model that was initialized on the `roberta-large` checkpoints for both the encoder 
+and decoder and fine-tuned on summarization on the CNN / Dailymail dataset, which is linked above.
+
+Disclaimer: The model card has been written by the Hugging Face team.
+
+## How to use
+
+You can use this model for summarization, *e.g.*
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_cnn_daily_mail")
+model = AutoModelForSeq2SeqLM.from_pretrained("google/roberta2roberta_L-24_cnn_daily_mail")
+
+article = """	(The Hollywood Reporter)"The Rocky Horror Picture
+Show" is the latest musical getting the small-
+screen treatment. Fox is developing a two-hour
+remake of the 1975 cult classic to be directed,
+executive-produced and choreographed by Kenneth
+Ortega ("High School Musical"). The project,
+tentatively titled "The Rocky Horror Picture Show
+Event," is casting-contingent. The special will be
+filmed in advance and not air live, but few
+details beyond that are known. In addition to
+Ortega, Gail Berman and Lou Adler, who produced
+the original film, are also attached as executive
+producers. The special will be produced by Fox 21
+Television Studios, and Berman's The Jackal Group.
+The special is timed to celebrate the 40th
+anniversary of the film, which has grossed more
+than $112 million and still plays in theaters
+across the country. TV premiere dates: The
+complete guide . This isn't the first stab at
+adapting "The Rocky Horror Picture Show." In 2002,
+Fox unveiled plans for an adaptation timed to the
+30th anniversary that never came to fruition. The
+faces of pilot season 2015 . Fox's "Glee" covered
+several of the show's most popular songs for a
+Season 2 episode and even released a special "The
+Rocky Horror Glee Show" EP. There is no plan yet
+for when the adaptation will air. Fox also has a
+live musical production of "Grease", starring
+Julianne Hough and Vanessa Hudgens, scheduled to
+air on Jan. 31, 2016. Broadcast TV scorecard .
+Following in the footsteps of "The Sound of Music"
+and "Peter Pan," NBC recently announced plans to
+air a live version of The Wiz later this year.
+Ortega's credits include "Gilmore Girls," "This Is
+It" and "Hocus Pocus." He is repped by Paradigm
+and Hanson, Jacobson. ©2015 The Hollywood
+Reporter. All rights reserved."""
+
+input_ids = tokenizer(article, return_tensors="pt").input_ids
+output_ids = model.generate(input_ids)[0]
+print(tokenizer.decode(output_ids, skip_special_tokens=True))
+# should output
+# Fox is developing a two-hour remake of the 1975 cult classic. The special will be directed, executive-produced and choreographed by Kenneth Ortega. 
+# The special is timed to celebrate the 40th anniversary of the film, which has grossed more than $112 million.
+
+```

model_cards/google/roberta2roberta_L-24_discofuse/README.md

@@ -0,0 +1,35 @@
+---
+language: en
+license: apache-2.0
+datasets:
+- discofuse
+---
+
+# Roberta2Roberta_L-24_discofuse EncoderDecoder model
+
+The model was introduced in 
+[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev/google/bertseq2seq/roberta24_discofuse/1). 
+
+The model is an encoder-decoder model that was initialized on the `roberta-large` checkpoints for both the encoder 
+and decoder and fine-tuned on sentencefusion on the discofuse dataset, which is linked above.
+
+Disclaimer: The model card has been written by the Hugging Face team.
+
+## How to use
+
+You can use this model for sentence fusion, *e.g.*
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_discofuse")
+model = AutoModelForSeq2SeqLM.from_pretrained("google/roberta2roberta_L-24_discofuse")
+
+discofuse = """As a run-blocker, Zeitler moves relatively well. Zeitler often struggles at the point of contact in space."""
+
+input_ids = tokenizer(discofuse, return_tensors="pt").input_ids
+output_ids = model.generate(input_ids)[0]
+print(tokenizer.decode(output_ids, skip_special_tokens=True))
+# should output
+# As a run-blocker, Zeitler moves relatively well. However, Zeitler often struggles at the point of contact in space.  
+```

model_cards/google/roberta2roberta_L-24_gigaword/README.md

@@ -0,0 +1,37 @@
+---
+language: en
+license: apache-2.0
+datasets:
+- gigaword
+---
+
+# Roberta2Roberta_L-24_gigaword EncoderDecoder model
+
+The model was introduced in 
+[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev/google/bertseq2seq/roberta24_gigaword/1). 
+
+The model is an encoder-decoder model that was initialized on the `roberta-large` checkpoints for both the encoder 
+and decoder and fine-tuned on headline generation using the Gigaword dataset, which is linked above.
+
+Disclaimer: The model card has been written by the Hugging Face team.
+
+## How to use
+
+You can use this model for extreme summarization, *e.g.*
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_gigaword")
+model = AutoModelForSeq2SeqLM.from_pretrained("google/roberta2roberta_L-24_gigaword")
+
+article = """australian shares closed down #.# percent monday
+following a weak lead from the united states and
+lower commodity prices , dealers said ."""
+
+input_ids = tokenizer(article, return_tensors="pt").input_ids
+output_ids = model.generate(input_ids)[0]
+print(tokenizer.decode(output_ids, skip_special_tokens=True))
+# should output
+# australian shares close down #.# percent.
+```

model_cards/google/roberta2roberta_L-24_wikisplit/README.md

@@ -0,0 +1,34 @@
+---
+language: en
+license: apache-2.0
+---
+
+# Roberta2Roberta_L-24_wikisplit EncoderDecoder model
+
+The model was introduced in 
+[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev/google/bertseq2seq/roberta24_cnndm/1). 
+
+The model is an encoder-decoder model that was initialized on the `roberta-large` checkpoints for both the encoder 
+and decoder and fine-tuned on sentence splitting on the [WikiSplit](https://github.com/google-research-datasets/wiki-split) dataset.
+
+Disclaimer: The model card has been written by the Hugging Face team.
+
+## How to use
+
+You can use this model for sentence splitting, *e.g.*
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_wikisplit")
+model = AutoModelForSeq2SeqLM.from_pretrained("google/roberta2roberta_L-24_wikisplit")
+
+long_sentence = """Due to the hurricane, Lobsterfest has been canceled, making Bob very happy about it and he decides to open Bob 's Burgers for customers who were planning on going to Lobsterfest."""
+
+input_ids = tokenizer(long_sentence, return_tensors="pt").input_ids
+output_ids = model.generate(input_ids)[0]
+print(tokenizer.decode(output_ids, skip_special_tokens=True))
+# should output
+# Due Due hurricane, Lobsterfest has been canceled, making Bob very happy about it. He decides to open B
+# ob's Burgers for customers who were planning on going to Lobsterfest.com.
+```

model_cards/iarfmoose/bert-base-cased-qa-evaluator/README.md

@@ -0,0 +1,28 @@
+# BERT-base-cased-qa-evaluator
+
+This model takes a question answer pair as an input and outputs a value representing its prediction about whether the input was a valid question and answer pair or not. The model is a pretrained [BERT-base-cased](https://huggingface.co/bert-base-cased) with a sequence classification head.
+
+## Intended uses
+
+The QA evaluator was originally designed to be used with the [t5-base-question-generator](https://huggingface.co/iarfmoose/t5-base-question-generator) for evaluating the quality of generated questions. 
+
+The input for the QA evaluator follows the format for `BertForSequenceClassification`, but using the question and answer as the two sequences. Inputs should take the following format:
+```
+[CLS] <question> [SEP] <answer [SEP]
+```
+
+## Limitations and bias
+
+The model is trained to evaluate if a question and answer are semantically related, but cannot determine whether an answer is actually true/correct or not.
+
+## Training data
+
+The training data was made up of question-answer pairs from the following datasets: 
+- [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/)
+- [RACE](http://www.cs.cmu.edu/~glai1/data/race/)
+- [CoQA](https://stanfordnlp.github.io/coqa/)
+- [MSMARCO](https://microsoft.github.io/msmarco/)
+
+## Training procedure
+
+The question and answer were concatenated 50% of the time. In the other 50% of the time a corruption operation was performed (either swapping the answer for an unrelated answer, or by copying part of the question into the answer). The model was then trained to predict whether the input sequence represented one of the original QA pairs or a corrupted input.

model_cards/iarfmoose/roberta-base-bulgarian-pos/README.md

@@ -0,0 +1,26 @@
+---
+language: bg
+---
+
+# RoBERTa-base-bulgarian-POS
+
+
+The RoBERTa model was originally introduced in [this paper](https://arxiv.org/abs/1907.11692). This model is a version of [RoBERTa-base-Bulgarian](https://huggingface.co/iarfmoose/roberta-base-bulgarian) fine-tuned for part-of-speech tagging.
+
+## Intended uses
+
+The model can be used to predict part-of-speech tags in Bulgarian text. Since the tokenizer uses byte-pair encoding, each word in the text may be split into more than one token. When predicting POS-tags, the last token from each word can be used. Using the last token was found to slightly outperform predictions based on the first token.
+
+An example of this can be found [here](https://github.com/iarfmoose/bulgarian-nlp/blob/master/models/postagger.py).
+
+## Limitations and bias
+
+The pretraining data is unfiltered text from the internet and may contain all sorts of biases.
+
+## Training data
+
+In addition to the pretraining data used in [RoBERTa-base-Bulgarian]([RoBERTa-base-Bulgarian](https://huggingface.co/iarfmoose/roberta-base-bulgarian)), the model was trained on the UPOS tags from [UD_Bulgarian-BTB](https://github.com/UniversalDependencies/UD_Bulgarian-BTB).
+
+## Training procedure
+
+The model was trained for 5 epochs over the training set. The loss was calculated based on label predictions for the last POS-tag for each word. The model achieves 97% on the test set.

model_cards/iarfmoose/roberta-base-bulgarian/README.md

@@ -0,0 +1,29 @@
+---
+language: bg
+---
+
+# RoBERTa-base-bulgarian
+
+
+The RoBERTa model was originally introduced in [this paper](https://arxiv.org/abs/1907.11692). This is a version of [RoBERTa-base](https://huggingface.co/roberta-base) pretrained on Bulgarian text.
+
+## Intended uses
+
+This model can be used for cloze tasks (masked language modeling) or finetuned on other tasks in Bulgarian.
+
+## Limitations and bias
+
+The training data is unfiltered text from the internet and may contain all sorts of biases.
+
+## Training data
+
+This model was trained on the following data:
+- [bg_dedup from OSCAR](https://oscar-corpus.com/)
+- [Newscrawl 1 million sentences 2017 from Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download/bulgarian)
+- [Wikipedia 1 million sentences 2016 from Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download/bulgarian)
+
+## Training procedure
+
+The model was pretrained using a masked language-modeling objective with dynamic masking as described [here](https://huggingface.co/roberta-base#preprocessing)
+
+It was trained for 200k steps. The batch size was limited to 8 due to GPU memory limitations.

model_cards/iarfmoose/roberta-small-bulgarian-pos/README.md

@@ -0,0 +1,26 @@
+---
+language: bg
+---
+
+# RoBERTa-small-bulgarian-POS
+
+
+The RoBERTa model was originally introduced in [this paper](https://arxiv.org/abs/1907.11692). This model is a version of [RoBERTa-small-Bulgarian](https://huggingface.co/iarfmoose/roberta-small-bulgarian) fine-tuned for part-of-speech tagging.
+
+## Intended uses
+
+The model can be used to predict part-of-speech tags in Bulgarian text. Since the tokenizer uses byte-pair encoding, each word in the text may be split into more than one token. When predicting POS-tags, the last token from each word can be used. Using the last token was found to slightly outperform predictions based on the first token.
+
+An example of this can be found [here](https://github.com/iarfmoose/bulgarian-nlp/blob/master/models/postagger.py).
+
+## Limitations and bias
+
+The pretraining data is unfiltered text from the internet and may contain all sorts of biases.
+
+## Training data
+
+In addition to the pretraining data used in [RoBERTa-base-Bulgarian]([RoBERTa-base-Bulgarian](https://huggingface.co/iarfmoose/roberta-base-bulgarian)), the model was trained on the UPOS tags from (UD_Bulgarian-BTB)[https://github.com/UniversalDependencies/UD_Bulgarian-BTB].
+
+## Training procedure
+
+The model was trained for 5 epochs over the training set. The loss was calculated based on label predictions for the last POS-tag for each word. The model achieves 98% on the test set.

model_cards/iarfmoose/roberta-small-bulgarian/README.md

@@ -0,0 +1,29 @@
+---
+language: bg
+---
+
+# RoBERTa-small-bulgarian
+
+
+The RoBERTa model was originally introduced in [this paper](https://arxiv.org/abs/1907.11692). This is a smaller version of [RoBERTa-base-bulgarian](https://huggingface.co/iarfmoose/roberta-small-bulgarian) with only 6 hidden layers, but similar performance.
+
+## Intended uses
+
+This model can be used for cloze tasks (masked language modeling) or finetuned on other tasks in Bulgarian.
+
+## Limitations and bias
+
+The training data is unfiltered text from the internet and may contain all sorts of biases.
+
+## Training data
+
+This model was trained on the following data:
+- [bg_dedup from OSCAR](https://oscar-corpus.com/)
+- [Newscrawl 1 million sentences 2017 from Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download/bulgarian)
+- [Wikipedia 1 million sentences 2016 from Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download/bulgarian)
+
+## Training procedure
+
+The model was pretrained using a masked language-modeling objective with dynamic masking as described [here](https://huggingface.co/roberta-base#preprocessing)
+
+It was trained for 160k steps. The batch size was limited to 8 due to GPU memory limitations.

model_cards/joeddav/xlm-roberta-large-xnli/README.md

@@ -0,0 +1,117 @@
+---
+language: multilingual
+tags:
+- text-classification
+- pytorch
+- tensorflow
+datasets:
+- mnli
+- xnli
+widget:
+- text: "За кого вы голосуете в 2020 году? <sep> This text is about politique."
+---
+
+# xlm-roberta-large-xnli
+
+## Model Description
+
+This model takes [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) and fine-tunes it on a combination of NLI data in 15 languages. It is intended to be used for zero-shot text classification, such as with the Hugging Face [ZeroShotClassificationPipeline](https://huggingface.co/transformers/master/main_classes/pipelines.html#transformers.ZeroShotClassificationPipeline).
+
+You can play with an interactive demo of this zero-shot technique with this model [here](https://huggingface.co/zero-shot/).
+
+## Inteded Usage
+
+This model is intended to be used for zero-shot text classification, especially in languages other than English. It is fine-tuned on XNLI, which is a multilingual NLI dataset. The model can therefore be used with any of the languages in the XNLI corpus:
+
+- English
+- French
+- Spanish
+- German
+- Greek
+- Bulgarian
+- Russian
+- Turkish
+- Arabic
+- Vietnamese
+- Thai
+- Chinese
+- Hindi
+- Swahili
+- Urdu
+
+Since the base model was pre-trained trained on 100 different languages (see the full list in appendix A of the [XLM
+Roberata paper](https://arxiv.org/abs/1911.02116)), the model may have some limited effectiveness in other languages as
+well.
+
+For English-only classification, it is recommended to use
+[bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) or
+[bart-large-mnli-yahoo-answers](https://huggingface.co/joeddav/bart-large-mnli-yahoo-answers).
+
+#### With the zero-shot classification pipeline
+
+The model can be loaded with the `zero-shot-classification` pipeline like so:
+
+```python
+from transformers import pipeline
+classifier = pipeline("zero-shot-classification",
+                      model="joeddav/xlm-roberta-large-xnli")
+```
+
+You can then classify in any of the above languages. You can even pass the labels in one language and the sequence to
+classify in another:
+
+```python
+# we will classify the Russian translation of, "Who are you voting for in 2020?"
+sequence_to_classify = "За кого вы голосуете в 2020 году?"
+# we can specify candidate labels in Russian or any other language above:
+candidate_labels = ["Europe", "public health", "politics"]
+classifier(sequence_to_classify, candidate_labels)
+# {'labels': ['politics', 'Europe', 'public health'],
+#  'scores': [0.9048484563827515, 0.05722189322113991, 0.03792969882488251],
+#  'sequence': 'За кого вы голосуете в 2020 году?'}
+```
+
+The default hypothesis template is the English, `This text is {}`. If you are working strictly within one language, it
+may be worthwhile to translate this to the language you are working with:
+
+```python
+sequence_to_classify = "¿A quién vas a votar en 2020?"
+candidate_labels = ["Europa", "salud pública", "política"]
+hypothesis_template = "Este ejemplo es {}."
+classifier(sequence_to_classify, candidate_labels, hypothesis_template=hypothesis_template)
+# {'labels': ['política', 'Europa', 'salud pública'],
+#  'scores': [0.9109585881233215, 0.05954807624220848, 0.029493311420083046],
+#  'sequence': '¿A quién vas a votar en 2020?'}
+```
+
+#### With manual PyTorch
+
+```python
+# pose sequence as a NLI premise and label as a hypothesis
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+nli_model = AutoModelForSequenceClassification.from_pretrained('joeddav/xlm-roberta-large-xnli')
+tokenizer = AutoTokenizer.from_pretrained('joeddav/xlm-roberta-large-xnli')
+
+premise = sequence
+hypothesis = f'This example is {label}.'
+
+# run through model pre-trained on MNLI
+x = tokenizer.encode(premise, hypothesis, return_tensors='pt',
+                     truncation_strategy='only_first')
+logits = nli_model(x.to(device))[0]
+
+# we throw away "neutral" (dim 1) and take the probability of
+# "entailment" (2) as the probability of the label being true 
+entail_contradiction_logits = logits[:,[0,2]]
+probs = entail_contradiction_logits.softmax(dim=1)
+prob_label_is_true = probs[:,1]
+```
+
+## Training
+
+This model was pre-trained on set of 100 languages, as described in
+[the original paper](https://arxiv.org/abs/1911.02116). It was then fine-tuned on the task of NLI on the concatenated
+MNLI train set and the XNLI validation and test sets. Finally, it was trained for one additional epoch on only XNLI
+data where the translations for the premise and hypothesis are shuffled such that the premise and hypothesis for
+each example come from the same original English example but the premise and hypothesis are of different languages.
+

model_cards/jplu/tf-xlm-r-ner-40-lang/README.md

@@ -1,7 +1,10 @@
+---
+language: multilingual
+---
 
 # XLM-R + NER
 
-This model is a fine-tuned  [XLM-Roberta-base](https://arxiv.org/abs/1911.02116) over the 40 languages proposed in [XTREME]([https://github.com/google-research/xtreme](https://github.com/google-research/xtreme)) from [Wikiann](https://aclweb.org/anthology/P17-1178). This is still an on-going work and the results will be updated everytime an improvement is reached. 
+This model is a fine-tuned  [XLM-Roberta-base](https://arxiv.org/abs/1911.02116) over the 40 languages proposed in [XTREME](https://github.com/google-research/xtreme) from [Wikiann](https://aclweb.org/anthology/P17-1178). This is still an on-going work and the results will be updated everytime an improvement is reached. 
 
 The covered labels are:
 ```
@@ -596,4 +599,4 @@ nlp_ner(test_zh)
 nlp_ner(test_ar)
 #Output: [{'word': '▁با', 'score': 0.9903655648231506, 'entity': 'PER'}, {'word': 'راك', 'score': 0.9850614666938782, 'entity': 'PER'}, {'word': '▁أوباما', 'score': 0.9850308299064636, 'entity': 'PER'}, {'word': '▁ها', 'score': 0.9477543234825134, 'entity': 'LOC'}, {'word': 'وا', 'score': 0.9428229928016663, 'entity': 'LOC'}, {'word': 'ي', 'score': 0.9319471716880798, 'entity': 'LOC'}]
 
-```
+```

model_cards/kuisailab/albert-base-arabic/README.md

@@ -0,0 +1,65 @@
+---
+language: ar
+datasets:
+- oscar
+- wikipedia
+tags:
+- ar
+- masked-lm
+- lm-head
+---
+
+
+# Arabic-ALBERT Base
+
+Arabic edition of ALBERT Base pretrained language model
+
+## Pretraining data
+
+The models were pretrained on ~4.4 Billion words:
+
+- Arabic version of [OSCAR](https://oscar-corpus.com/) (unshuffled version of the corpus) - filtered from [Common Crawl](http://commoncrawl.org/)
+- Recent dump of Arabic [Wikipedia](https://dumps.wikimedia.org/backup-index.html)
+
+__Notes on training data:__
+
+- Our final version of corpus contains some non-Arabic words inlines, which we did not remove from sentences since that would affect some tasks like NER.
+- Although non-Arabic characters were lowered as a preprocessing step, since Arabic characters do not have upper or lower case, there is no cased and uncased version of the model.
+- The corpus and vocabulary set are not restricted to Modern Standard Arabic, they contain some dialectical Arabic too.
+
+## Pretraining details
+
+- These models were trained using Google ALBERT's github [repository](https://github.com/google-research/albert) on a single TPU v3-8 provided for free from [TFRC](https://www.tensorflow.org/tfrc).
+- Our pretraining procedure follows training settings of bert with some changes: trained for 7M training steps with batchsize of 64, instead of 125K with batchsize of 4096.
+
+## Models
+
+|  | albert-base | albert-large | albert-xlarge |
+|:---:|:---:|:---:|:---:|
+| Hidden Layers | 12 | 24 | 24 |
+| Attention heads | 12 | 16 | 32 |
+| Hidden size | 768 | 1024 | 2048 |
+
+## Results
+
+For further details on the models performance or any other queries, please refer to [Arabic-ALBERT](https://github.com/KUIS-AI-Lab/Arabic-ALBERT/)
+
+## How to use
+
+You can use these models by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:  
+
+```python
+
+from transformers import AutoTokenizer, AutoModel
+
+# loading the tokenizer
+base_tokenizer    = AutoTokenizer.from_pretrained("kuisailab/albert-base-arabic")
+
+# loading the model
+base_model   = AutoModel.from_pretrained("kuisailab/albert-base-arabic")
+
+```
+
+## Acknowledgement
+
+Thanks to Google for providing free TPU for the training process and for Huggingface for hosting these models on their servers 😊

model_cards/kuisailab/albert-large-arabic/README.md

@@ -0,0 +1,65 @@
+---
+language: ar
+datasets:
+- oscar
+- wikipedia
+tags:
+- ar
+- masked-lm
+- lm-head
+---
+
+
+# Arabic-ALBERT Large
+
+Arabic edition of ALBERT Large pretrained language model
+
+## Pretraining data
+
+The models were pretrained on ~4.4 Billion words:
+
+- Arabic version of [OSCAR](https://oscar-corpus.com/) (unshuffled version of the corpus) - filtered from [Common Crawl](http://commoncrawl.org/)
+- Recent dump of Arabic [Wikipedia](https://dumps.wikimedia.org/backup-index.html)
+
+__Notes on training data:__
+
+- Our final version of corpus contains some non-Arabic words inlines, which we did not remove from sentences since that would affect some tasks like NER.
+- Although non-Arabic characters were lowered as a preprocessing step, since Arabic characters do not have upper or lower case, there is no cased and uncased version of the model.
+- The corpus and vocabulary set are not restricted to Modern Standard Arabic, they contain some dialectical Arabic too.
+
+## Pretraining details
+
+- These models were trained using Google ALBERT's github [repository](https://github.com/google-research/albert) on a single TPU v3-8 provided for free from [TFRC](https://www.tensorflow.org/tfrc).
+- Our pretraining procedure follows training settings of bert with some changes: trained for 7M training steps with batchsize of 64, instead of 125K with batchsize of 4096.
+
+## Models
+
+|  | albert-base | albert-large | albert-xlarge |
+|:---:|:---:|:---:|:---:|
+| Hidden Layers | 12 | 24 | 24 |
+| Attention heads | 12 | 16 | 32 |
+| Hidden size | 768 | 1024 | 2048 |
+
+## Results
+
+For further details on the models performance or any other queries, please refer to [Arabic-ALBERT](https://github.com/KUIS-AI-Lab/Arabic-ALBERT/)
+
+## How to use
+
+You can use these models by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:  
+
+```python
+
+from transformers import AutoTokenizer, AutoModel
+
+# loading the tokenizer
+tokenizer    = AutoTokenizer.from_pretrained("kuisailab/albert-large-arabic")
+
+# loading the model
+model   = AutoModel.from_pretrained("kuisailab/albert-large-arabic")
+
+```
+
+## Acknowledgement
+
+Thanks to Google for providing free TPU for the training process and for Huggingface for hosting these models on their servers 😊

model_cards/kuisailab/albert-xlarge-arabic/README.md

@@ -0,0 +1,65 @@
+---
+language: ar
+datasets:
+- oscar
+- wikipedia
+tags:
+- ar
+- masked-lm
+- lm-head
+---
+
+
+# Arabic-ALBERT Xlarge
+
+Arabic edition of ALBERT Xlarge pretrained language model
+
+## Pretraining data
+
+The models were pretrained on ~4.4 Billion words:
+
+- Arabic version of [OSCAR](https://oscar-corpus.com/) (unshuffled version of the corpus) - filtered from [Common Crawl](http://commoncrawl.org/)
+- Recent dump of Arabic [Wikipedia](https://dumps.wikimedia.org/backup-index.html)
+
+__Notes on training data:__
+
+- Our final version of corpus contains some non-Arabic words inlines, which we did not remove from sentences since that would affect some tasks like NER.
+- Although non-Arabic characters were lowered as a preprocessing step, since Arabic characters do not have upper or lower case, there is no cased and uncased version of the model.
+- The corpus and vocabulary set are not restricted to Modern Standard Arabic, they contain some dialectical Arabic too.
+
+## Pretraining details
+
+- These models were trained using Google ALBERT's github [repository](https://github.com/google-research/albert) on a single TPU v3-8 provided for free from [TFRC](https://www.tensorflow.org/tfrc).
+- Our pretraining procedure follows training settings of bert with some changes: trained for 7M training steps with batchsize of 64, instead of 125K with batchsize of 4096.
+
+## Models
+
+|  | albert-base | albert-large | albert-xlarge |
+|:---:|:---:|:---:|:---:|
+| Hidden Layers | 12 | 24 | 24 |
+| Attention heads | 12 | 16 | 32 |
+| Hidden size | 768 | 1024 | 2048 |
+
+## Results
+
+For further details on the models performance or any other queries, please refer to [Arabic-ALBERT](https://github.com/KUIS-AI-Lab/Arabic-ALBERT/)
+
+## How to use
+
+You can use these models by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:  
+
+```python
+
+from transformers import AutoTokenizer, AutoModel
+
+# loading the tokenizer
+tokenizer    = AutoTokenizer.from_pretrained("kuisailab/albert-xlarge-arabic")
+
+# loading the model
+model   = AutoModel.from_pretrained("kuisailab/albert-xlarge-arabic")
+
+```
+
+## Acknowledgement
+
+Thanks to Google for providing free TPU for the training process and for Huggingface for hosting these models on their servers 😊

model_cards/loodos/albert-base-turkish-uncased/README.md

@@ -0,0 +1,52 @@
+---
+language: tr
+---
+
+# Turkish Language Models with Huggingface's Transformers
+
+As R&D Team at Loodos, we release cased and uncased versions of most recent language models for Turkish. More details about pretrained models and evaluations on downstream tasks can be found [here (our repo)](https://github.com/Loodos/turkish-language-models).
+
+# Turkish ALBERT-Base (uncased)
+
+This is ALBERT-Base model which has 12 repeated encoder layers with 768 hidden layer size trained on uncased Turkish dataset.
+
+## Usage
+
+Using AutoModel and AutoTokenizer from Transformers, you can import the model as described below.
+
+```python
+from transformers import AutoModel, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("loodos/albert-base-turkish-uncased", do_lower_case=False, keep_accents=True)
+
+model = AutoModel.from_pretrained("loodos/albert-base-turkish-uncased")
+ 
+normalizer = TextNormalization()
+normalized_text = normalizer.normalize(text, do_lower_case=True, is_turkish=True)
+
+tokenizer.tokenize(normalized_text)
+```
+
+### Notes on Tokenizers
+Currently, Huggingface's tokenizers (which were written in Python) have a bug concerning letters "ı, i, I, İ" and non-ASCII Turkish specific letters. There are two reasons.
+
+1- Vocabulary and sentence piece model is created with NFC/NFKC normalization but tokenizer uses NFD/NFKD. NFD/NFKD normalization changes text that contains Turkish characters I-ı, İ-i, Ç-ç, Ö-ö, Ş-ş, Ğ-ğ, Ü-ü. This causes wrong tokenization, wrong training and loss of information. Some tokens are never trained.(like "şanlıurfa", "öğün", "çocuk" etc.) NFD/NFKD normalization is not proper for Turkish.
+
+2- Python's default ```string.lower()``` and ```string.upper()``` make the conversions
+
+- "I" and "İ" to 'i'
+- 'i' and 'ı' to 'I'
+
+respectively. However, in Turkish, 'I' and 'İ' are two different letters. 
+
+We opened an [issue](https://github.com/huggingface/transformers/issues/6680) in Huggingface's github repo about this bug. Until it is fixed, in case you want to train your model with uncased data, we provide a simple text normalization module (`TextNormalization()` in the code snippet above) in our [repo](https://github.com/Loodos/turkish-language-models).
+
+
+## Details and Contact
+
+You contact us to ask a question, open an issue or give feedback via our github [repo](https://github.com/Loodos/turkish-language-models).
+
+## Acknowledgments
+
+Many thanks to TFRC Team for providing us cloud TPUs on Tensorflow Research Cloud to train our models.
+