Use Python3 constructor call as suggested by @julien-c

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

.github/workflows/github-torch-hub.yml

@@ -21,7 +21,7 @@ jobs:
     - name: Install dependencies
       run: |
         pip install torch
-        pip install numpy tokenizers filelock requests tqdm regex sentencepiece sacremoses packaging
+        pip install numpy tokenizers filelock requests tqdm regex sentencepiece sacremoses
 
     - name: Torch hub list
       run: |

.github/workflows/self-push.yml

@@ -1,13 +1,9 @@
 name: Self-hosted runner (push)
 
 on: 
-  push:
-    branches:
-      - master
-    paths: 
-      - "src/**"
-      - "tests/**"
-      - ".github/**"
+  # push:
+  #   branches:
+  #     - master
   # pull_request:
   repository_dispatch:
 
@@ -35,8 +31,8 @@ jobs:
     - name: Install dependencies
       run: |
         source .env/bin/activate
-        pip install torch
-        pip install .[sklearn,testing]
+        pip install .[sklearn,tf,torch,testing]
+        pip uninstall -y tensorflow
 
     - name: Are GPUs recognized by our DL frameworks
       run: |

CONTRIBUTING.md

@@ -198,12 +198,11 @@ Follow these steps to start contributing:
    are useful to avoid duplicated work, and to differentiate it from PRs ready
    to be merged;
 4. Make sure existing tests pass;
-5. Add high-coverage tests. No quality testing = no merge. 
+5. Add high-coverage tests. No quality test, no merge. 
  - If you are adding a new model, make sure that you use `ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,...)`, which triggers the common tests.
  - If you are adding new `@slow` tests, make sure they pass using `RUN_SLOW=1 python -m pytest tests/test_my_new_model.py`. 
- - If you are adding a new tokenizer, write tests, and make sure `RUN_SLOW=1 python -m pytest tests/test_tokenization_{your_model_name}.py` passes.
 CircleCI does not run them. 
-6. All public methods must have informative docstrings that work nicely with sphinx. See `modeling_ctrl.py` for an example.
+6. All public methods must have informative docstrings;
 
 ### Tests
 

README.md

@@ -164,10 +164,8 @@ At some point in the future, you'll be able to seamlessly move from pre-training
 17. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
 18. **[DialoGPT](https://huggingface.co/transformers/model_doc/dialogpt.html)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
 19. **[Reformer](https://huggingface.co/transformers/model_doc/reformer.html)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-20. **[MarianMT](https://huggingface.co/transformers/model_doc/marian.html)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
-21. **[Longformer](https://huggingface.co/transformers/model_doc/longformer.html)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-22. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
-23. 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.
+20. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
+21. 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 Peason 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).
 

docs/README.md

@@ -67,131 +67,3 @@ It should build the static app that will be available under `/docs/_build/html`
 
 Accepted files are reStructuredText (.rst) and Markdown (.md). Create a file with its extension and put it
 in the source directory. You can then link it to the toc-tree by putting the filename without the extension.
-
-## Writing Documentation - Specification
-
-The `huggingface/transformers` documentation follows the
-[Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style. It is
-mostly written in ReStructuredText 
-([Sphinx simple documentation](https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html), 
-[Sourceforge complete documentation](https://docutils.sourceforge.io/docs/ref/rst/restructuredtext.html))
-
-### Adding a new section
-
-A section is a page held in the `Notes` toc-tree on the documentation. Adding a new section is done in two steps:
-
-- Add a new file under `./source`. This file can either be ReStructuredText (.rst) or Markdown (.md).
-- Link that file in `./source/index.rst` on the correct toc-tree.
-
-### Adding a new model
-
-When adding a new model:
- 
-- Create a file `xxx.rst` under `./source/model_doc`. 
-- Link that file in `./source/index.rst` on the `model_doc` toc-tree.
-- Write a short overview of the model:
-    - Overview with paper & authors
-    - Paper abstract
-    - Tips and tricks and how to use it best
-- Add the classes that should be linked in the model. This generally includes the configuration, the tokenizer, and
-  every model of that class (the base model, alongside models with additional heads), both in PyTorch and TensorFlow.
-  The order is generally: 
-    - Configuration, 
-    - Tokenizer
-    - PyTorch base model
-    - PyTorch head models
-    - TensorFlow base model
-    - TensorFlow head models
-
-These classes should be added using the RST syntax. Usually as follows:
-```
-XXXConfig
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XXXConfig
-    :members:
-```
-
-This will include every public method of the configuration. If for some reason you wish for a method not to be displayed
-in the documentation, you can do so by specifying which methods should be in the docs:
-
-```
-XXXTokenizer
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XXXTokenizer
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
-
-```
-
-### Writing source documentation
-
-Values that should be put in `code` should either be surrounded by double backticks: \`\`like so\`\` or be written as an object
-using the :obj: syntax: :obj:\`like so\`.
-
-When mentionning a class, it is recommended to use the :class: syntax as the mentioned class will be automatically
-linked by Sphinx: :class:\`transformers.XXXClass\`
-
-When mentioning a function, it is recommended to use the :func: syntax as the mentioned method will be automatically
-linked by Sphinx: :func:\`transformers.XXXClass.method\`
-
-Links should be done as so (note the double underscore at the end): \`text for the link <./local-link-or-global-link#loc>\`__
-
-#### Defining arguments in a method
-
-Arguments should be defined with the `Args:` prefix, followed by a line return and an indentation. 
-The argument should be followed by its type, with its shape if it is a tensor, and a line return.
-Another indentation is necessary before writing the description of the argument.
-
-Here's an example showcasing everything so far:
-
-```
-    Args:
-        input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary.
-
-            Indices can be obtained using :class:`transformers.AlbertTokenizer`.
-            See :func:`transformers.PreTrainedTokenizer.encode` and
-            :func:`transformers.PreTrainedTokenizer.encode_plus` for details.
-
-            `What are input IDs? <../glossary.html#input-ids>`__
-```
-
-#### Writing a multi-line code block 
-
-Multi-line code blocks can be useful for displaying examples. They are done like so:
-
-```
-Example::
-
-    # first line of code
-    # second line
-    # etc
-```
-
-The `Example` string at the beginning can be replaced by anything as long as there are two semicolons following it.
-
-#### Writing a return block
-
-Arguments should be defined with the `Args:` prefix, followed by a line return and an indentation. 
-The first line should be the type of the return, followed by a line return. No need to indent further for the elements
-building the return.
-
-Here's an example for tuple return, comprising several objects:
-
-```
-    Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
-        loss (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
-            Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
-        prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
-            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-```
-
-Here's an example for a single value return:
-
-```
-    Returns:
-        A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-```

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'2.10.0'
+release = u'2.9.0'
 
 
 # -- General configuration ---------------------------------------------------

docs/source/converting_tensorflow_models.rst

@@ -12,7 +12,7 @@ A command-line interface is provided to convert original Bert/GPT/GPT-2/Transfor
 BERT
 ^^^^
 
-You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google <https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the `convert_bert_original_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/transformers/blob/master/src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py>`_ script.
+You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google <https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the `convert_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/transformers/blob/master/transformers/convert_tf_checkpoint_to_pytorch.py>`_ script.
 
 This CLI takes as input a TensorFlow checkpoint (three files starting with ``bert_model.ckpt``\ ) and the associated configuration file (\ ``bert_config.json``\ ), and creates a PyTorch model for this configuration, loads the weights from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can be imported using ``torch.load()`` (see examples in `run_bert_extract_features.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_extract_features.py>`_\ , `run_bert_classifier.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_classifier.py>`_ and `run_bert_squad.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_squad.py>`_\ ).
 
@@ -33,26 +33,6 @@ Here is an example of the conversion process for a pre-trained ``BERT-Base Uncas
 
 You can download Google's pre-trained models for the conversion `here <https://github.com/google-research/bert#pre-trained-models>`__.
 
-ALBERT
-^^^^^^
-
-Convert TensorFlow model checkpoints of ALBERT to PyTorch using the `convert_albert_original_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/transformers/blob/master/src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py>`_ script.
-
-The CLI takes as input a TensorFlow checkpoint (three files starting with ``model.ckpt-best``\ ) and the accompanying configuration file (\ ``albert_config.json``\ ), then creates and saves a PyTorch model. To run this conversion you will need to have TensorFlow and PyTorch installed.
-
-Here is an example of the conversion process for the pre-trained ``ALBERT Base`` model:
-
-.. code-block:: shell
-
-   export ALBERT_BASE_DIR=/path/to/albert/albert_base
-
-   transformers-cli convert --model_type albert \
-     --tf_checkpoint $ALBERT_BASE_DIR/model.ckpt-best \
-     --config $ALBERT_BASE_DIR/albert_config.json \
-     --pytorch_dump_output $ALBERT_BASE_DIR/pytorch_model.bin
-
-You can download Google's pre-trained models for the conversion `here <https://github.com/google-research/albert#pre-trained-models>`__.
-
 OpenAI GPT
 ^^^^^^^^^^
 

docs/source/examples.md

@@ -23,7 +23,7 @@ pip install -r ./examples/requirements.txt
 | [GLUE](#glue) | Examples running BERT/XLM/XLNet/RoBERTa on the 9 GLUE tasks. Examples feature distributed training as well as half-precision. |
 | [SQuAD](#squad) | Using BERT/RoBERTa/XLNet/XLM for question answering, examples with distributed training. |
 | [Multiple Choice](#multiple-choice) | Examples running BERT/XLNet/RoBERTa on the SWAG/RACE/ARC tasks. |
-| [Named Entity Recognition](https://github.com/huggingface/transformers/tree/master/examples/token-classification) | Using BERT for Named Entity Recognition (NER) on the CoNLL 2003 dataset, examples with distributed training. |
+| [Named Entity Recognition](https://github.com/huggingface/transformers/tree/master/examples/ner) | Using BERT for Named Entity Recognition (NER) on the CoNLL 2003 dataset, examples with distributed training. |
 | [XNLI](#xnli) | Examples running BERT/XLM on the XNLI benchmark. |
 | [Adversarial evaluation of model performances](#adversarial-evaluation-of-model-performances) | Testing a model with adversarial evaluation of natural language inference on the Heuristic Analysis for NLI Systems (HANS) dataset (McCoy et al., 2019.) |
 

docs/source/index.rst

@@ -108,5 +108,3 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
     model_doc/electra
     model_doc/dialogpt
     model_doc/reformer
-    model_doc/marian
-    model_doc/longformer

docs/source/migration.md

@@ -1,18 +1,5 @@
-# Migrating from previous packages
+# Migrating from pytorch-pretrained-bert
 
-## Migrating from pytorch-transformers to transformers
-
-Here is a quick summary of what you should take care of when migrating from `pytorch-transformers` to `transformers`.
-
-### Positional order of some models' keywords inputs (`attention_mask`, `token_type_ids`...) changed
-
-To be able to use Torchscript (see #1010, #1204 and #1195) the specific order of some models **keywords inputs** (`attention_mask`, `token_type_ids`...) has been changed.
-
-If you used to call the models with keyword names for keyword arguments, e.g. `model(inputs_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)`, this should not cause any change.
-
-If you used to call the models with positional inputs for keyword arguments, e.g. `model(inputs_ids, attention_mask, token_type_ids)`, you may have to double check the exact order of input arguments.
-
-## Migrating from pytorch-pretrained-bert
 
 Here is a quick summary of what you should take care of when migrating from `pytorch-pretrained-bert` to `transformers`
 

docs/source/model_doc/albert.rst

@@ -6,7 +6,7 @@ Overview
 
 The ALBERT model was proposed in `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations <https://arxiv.org/abs/1909.11942>`_
 by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut. It presents
-two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT:
+two parameter-reduction techniques to lower memory consumption and increase the trainig speed of BERT:
 
 - Splitting the embedding matrix into two smaller matrices
 - Using repeating layers split among groups

docs/source/model_doc/bart.rst

@@ -1,6 +1,6 @@
 Bart
 ----------------------------------------------------
-**DISCLAIMER:** If you see something strange,
+**DISCLAIMER:** This model is still a work in progress, if you see something strange,
 file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
 @sshleifer
 

docs/source/model_doc/longformer.rst

@@ -1,69 +0,0 @@
-Longformer
-----------------------------------------------------
-**DISCLAIMER:** This model is still a work in progress, if you see something strange,
-file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`_
-
-Overview
-~~~~~
-The Longformer model was presented in `Longformer: The Long-Document Transformer <https://arxiv.org/pdf/2004.05150.pdf>`_ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-Here the abstract: 
-
-*Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer. Longformer's attention mechanism is a drop-in replacement for the standard self-attention and combines a local windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on WikiHop and TriviaQA.*
-
-The Authors' code can be found `here <https://github.com/allenai/longformer>`_ .
-
-Longformer Self Attention
-~~~~~~~~~~~~~~~~~~~~
-Longformer self attention employs self attention on both a "local" context and a "global" context.
-Most tokens only attend "locally" to each other meaning that each token attends to its :math:`\frac{1}{2} w` previous tokens and :math:`\frac{1}{2} w` succeding tokens with :math:`w` being the window length as defined in `config.attention_window`. Note that `config.attention_window` can be of type ``list`` to define a different :math:`w` for each layer. 
-A selecetd few tokens attend "globally" to all other tokens, as it is conventionally done for all tokens in *e.g.* `BertSelfAttention`.
-
-Note that "locally" and "globally" attending tokens are projected by different query, key and value matrices.
-Also note that every "locally" attending token not only attends to tokens within its window :math:`w`, but also to all "globally" attending tokens so that global attention is *symmetric*.
-
-The user can define which tokens are masked, which tokens attend "locally" and which tokens attend "globally" by setting the `config.attention_mask` `torch.Tensor` appropriately. In contrast to other models `Longformer` accepts the following values in `config.attention_mask`: `0` - the token is masked and not attended at all (as is done in other models), `1` - the token attends "locally", `2` - token attends "globally". For more information please also refer to :func:`~transformers.LongformerModel.forward` method.
-
-Using Longformer self attention, the memory and time complexity of the query-key matmul operation, which usually represents the memory and time bottleneck, can be reduced from :math:`\mathcal{O}(n_s \times n_s)` to :math:`\mathcal{O}(n_s \times w)`, with :math:`n_s` being the sequence length and :math:`w` being the average window size. It is assumed that the number of "globally" attending tokens is insignificant as compared to the number of "locally" attending tokens.
-
-For more information, please refer to the official `paper <https://arxiv.org/pdf/2004.05150.pdf>`_ .
-
-
-Training
-~~~~~~~~~~~~~~~~~~~~
-``LongformerForMaskedLM`` is trained the exact same way, ``RobertaForMaskedLM`` is trained and 
-should be used as follows:
-
-::
-
-  input_ids = tokenizer.encode('This is a sentence from [MASK] training data', return_tensors='pt')
-  mlm_labels = tokenizer.encode('This is a sentence from the training data', return_tensors='pt')
-
-  loss = model(input_ids, labels=input_ids, masked_lm_labels=mlm_labels)[0]
-
-
-LongformerConfig
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LongformerConfig
-    :members:
-
-
-LongformerTokenizer
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LongformerTokenizer
-    :members: 
-
-
-LongformerModel
-~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LongformerModel
-    :members:
-
-
-LongformerForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LongformerForMaskedLM
-    :members:

docs/source/model_doc/marian.rst

@@ -1,105 +0,0 @@
-MarianMT
-----------------------------------------------------
-**DISCLAIMER:** If you see something strange,
-file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
-@sshleifer. Translations should be similar, but not identical to, output in the test set linked to in each model card.
-
-Implementation Notes
-~~~~~~~~~~~~~~~~~~~~
-- each model is about 298 MB on disk, there are 1,000+ models.
-- The list of supported language pairs can be found `here <https://huggingface.co/Helsinki-NLP>`__.
-- The 1,000+ models were originally trained by `Jörg Tiedemann <https://researchportal.helsinki.fi/en/persons/j%C3%B6rg-tiedemann>`__ using the `Marian <https://marian-nmt.github.io/>`_ C++ library, which supports fast training and translation.
-- All models are transformer encoder-decoders with 6 layers in each component. Each model's performance is documented in a model card.
-- the 80 opus models that require BPE preprocessing are not supported.
-- The modeling code is the same as ``BartForConditionalGeneration`` with a few minor modifications:
-    - static (sinusoid) positional embeddings (``MarianConfig.static_position_embeddings=True``)
-    - a new final_logits_bias (``MarianConfig.add_bias_logits=True``)
-    - no layernorm_embedding (``MarianConfig.normalize_embedding=False``)
-    - the model starts generating with pad_token_id (which has 0 token_embedding) as the prefix. (Bart uses <s/>)
-- Code to bulk convert models can be found in ``convert_marian_to_pytorch.py``
-
-Naming
-~~~~~~
-- All  model names use the following format: ``Helsinki-NLP/opus-mt-{src}-{tgt}``
-- The language codes used to name models are inconsistent. Two digit codes can usually be found `here <https://developers.google.com/admin-sdk/directory/v1/languages>`_, three digit codes require googling "language code {code}".
-- Codes formatted like ``es_AR`` are usually ``code_{region}``. That one is spanish documents from Argentina.
-
-
-Multilingual Models
-~~~~~~~~~~~~~~~~~~~~
-
-All  model names use the following format: ``Helsinki-NLP/opus-mt-{src}-{tgt}``:
-    - if ``src`` is in all caps, the model supports multiple input languages, you can figure out which ones by looking at the model card, or the Group Members `mapping <https://gist.github.com/sshleifer/6d20e7761931b08e73c3219027b97b8a>`_ .
-    - if ``tgt`` is in all caps, the model can output multiple languages, and you should specify a language code by prepending the desired output language to the src_text
-    - You can see a tokenizer's supported language codes in ``tokenizer.supported_language_codes``
-
-Example of translating english to many romance languages, using language codes:
-
-.. code-block:: python
-
-    from transformers import MarianMTModel, MarianTokenizer
-    src_text = [
-        '>>fr<< this is a sentence in english that we want to translate to french',
-        '>>pt<< This should go to portuguese',
-        '>>es<< And this to Spanish'
-    ]
-
-    model_name = 'Helsinki-NLP/opus-mt-en-ROMANCE'
-    tokenizer = MarianTokenizer.from_pretrained(model_name)
-    print(tokenizer.supported_language_codes)
-    model = MarianMTModel.from_pretrained(model_name)
-    translated = model.generate(**tokenizer.prepare_translation_batch(src_text))
-    tgt_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
-    # ["c'est une phrase en anglais que nous voulons traduire en français",
-    # 'Isto deve ir para o português.',
-    # 'Y esto al español']
-
-Sometimes, models were trained on collections of languages that do not resolve to a group. In this case, _ is used as a separator for src or tgt, as in ``'Helsinki-NLP/opus-mt-en_el_es_fi-en_el_es_fi'``. These still require language codes.
-There are many supported regional language codes, like ``>>es_ES<<`` (Spain) and ``>>es_AR<<`` (Argentina), that do not seem to change translations. I have not found these to provide different results than just using ``>>es<<``.
-
-For Example:
-    - ``Helsinki-NLP/opus-mt-NORTH_EU-NORTH_EU``: translates from all NORTH_EU languages (see `mapping <https://gist.github.com/sshleifer/6d20e7761931b08e73c3219027b97b8a>`_) to all NORTH_EU languages. Use a special language code like ``>>de<<`` to specify output language.
-    - ``Helsinki-NLP/opus-mt-ROMANCE-en``: translates from many romance languages to english, no codes needed since there is only 1 tgt language.
-
-
-
-.. code-block:: python
-
-    GROUP_MEMBERS = {
-     'ZH': ['cmn', 'cn', 'yue', 'ze_zh', 'zh_cn', 'zh_CN', 'zh_HK', 'zh_tw', 'zh_TW', 'zh_yue', 'zhs', 'zht', 'zh'],
-     'ROMANCE': ['fr', 'fr_BE', 'fr_CA', 'fr_FR', 'wa', 'frp', 'oc', 'ca', 'rm', 'lld', 'fur', 'lij', 'lmo', 'es', 'es_AR', 'es_CL', 'es_CO', 'es_CR', 'es_DO', 'es_EC', 'es_ES', 'es_GT', 'es_HN', 'es_MX', 'es_NI', 'es_PA', 'es_PE', 'es_PR', 'es_SV', 'es_UY', 'es_VE', 'pt', 'pt_br', 'pt_BR', 'pt_PT', 'gl', 'lad', 'an', 'mwl', 'it', 'it_IT', 'co', 'nap', 'scn', 'vec', 'sc', 'ro', 'la'],
-     'NORTH_EU': ['de', 'nl', 'fy', 'af', 'da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
-     'SCANDINAVIA': ['da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
-     'SAMI': ['se', 'sma', 'smj', 'smn', 'sms'],
-     'NORWAY': ['nb_NO', 'nb', 'nn_NO', 'nn', 'nog', 'no_nb', 'no'],
-     'CELTIC': ['ga', 'cy', 'br', 'gd', 'kw', 'gv']
-    }
-
-Code to see available pretrained models:
-
-.. code-block:: python
-
-    from transformers.hf_api import HfApi
-    model_list = HfApi().model_list()
-    org = "Helsinki-NLP"
-    model_ids = [x.modelId for x in model_list if x.modelId.startswith(org)]
-    suffix = [x.split('/')[1] for x in model_ids]
-    multi_models = [f'{org}/{s}' for s in suffix if s != s.lower()]
-
-MarianMTModel
-~~~~~~~~~~~~~
-
-Pytorch version of marian-nmt's transformer.h (c++). Designed for the OPUS-NMT translation checkpoints.
-Model API is identical to BartForConditionalGeneration.
-Available models are listed at `Model List <https://huggingface.co/models?search=Helsinki-NLP>`__
-This class inherits all functionality from ``BartForConditionalGeneration``, see that page for method signatures.
-
-.. autoclass:: transformers.MarianMTModel
-    :members:
-
-
-MarianTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MarianTokenizer
-    :members: prepare_translation_batch

docs/source/pretrained_models.rst

@@ -275,7 +275,7 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
 |                   |                                                            | | FlauBERT large architecture                                                                                                         |
 |                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Bart              | ``bart-large``                                             | | 24-layer, 1024-hidden, 16-heads, 406M parameters                                                                                    |
+| Bart              | ``bart-large``                                             | | 12-layer, 1024-hidden, 16-heads, 406M parameters                                                                                    |
 |                   |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_)                                                       |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``bart-large-mnli``                                        | | Adds a 2 layer classification head with 1 million parameters                                                                        |
@@ -296,18 +296,6 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
 |                   | ``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>`_)                                                                           |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Longformer        | ``longformer-base-4096``                                   | | 12-layer, 768-hidden, 12-heads, ~149M parameters                                                                                    |
-|                   |                                                            | | Starting from RoBERTa-base checkpoint, trained on documents of max length 4,096                                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``longformer-large-4096``                                  | | 24-layer, 1024-hidden, 16-heads, ~435M parameters                                                                                   |
-|                   |                                                            | | Starting from RoBERTa-large checkpoint, trained on documents of max length 4,096                                                    |
+| Reformer          | ``reformer-crime-and-punishment``                          | | 6-layer, 256-hidden, 2-heads, 3M parameters                                                                                         |
+|                   |                                                            | | Trained on English text: Crime and Punishment novel by Fyodor Dostoyevsky                                                           |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+

docs/source/quickstart.md

@@ -8,7 +8,7 @@ The library was designed with two strong goals in mind:
 
 - be as easy and fast to use as possible:
 
-  - we strongly limited the number of user-facing abstractions to learn, in fact, there are almost no abstractions, just three standard classes required to use each model: configuration, models and tokenizer,
+  - we strongly limited the number of user-facing abstractions to learn, in fact there are almost no abstractions, just three standard classes required to use each model: configuration, models and tokenizer,
   - all of these classes can be initialized in a simple and unified way from pretrained instances by using a common `from_pretrained()` instantiation method which will take care of downloading (if needed), caching and loading the related class from a pretrained instance supplied in the library or your own saved instance.
   - as a consequence, this library is NOT a modular toolbox of building blocks for neural nets. If you want to extend/build-upon the library, just use regular Python/PyTorch modules and inherit from the base classes of the library to reuse functionalities like model loading/saving.
 
@@ -31,27 +31,27 @@ A few other goals:
 
 ## Main concepts
 
-The library is build around three types of classes for each model:
+The library is build around three type of classes for each models:
 
-- **model classes**  e.g., `BertModel` which are 20+ PyTorch models (`torch.nn.Modules`) that work with the pretrained weights provided in the library. In TF2, these are `tf.keras.Model`.
-- **configuration classes** which store all the parameters required to build a model, e.g., `BertConfig`. You don't always need to instantiate these your-self. In particular, if you are using a pretrained model without any modification, creating the model will automatically take care of instantiating the configuration (which is part of the model)
-- **tokenizer classes** which store the vocabulary for each model and provide methods for encoding/decoding strings in a list of token embeddings indices to be fed to a model, e.g., `BertTokenizer`
+- **model classes** which are PyTorch models (`torch.nn.Modules`) of the 8 models architectures currently provided in the library, e.g. `BertModel`
+- **configuration classes** which store all the parameters required to build a model, e.g. `BertConfig`. You don't always need to instantiate these your-self, in particular if you are using a pretrained model without any modification, creating the model will automatically take care of instantiating the configuration (which is part of the model)
+- **tokenizer classes** which store the vocabulary for each model and provide methods for encoding/decoding strings in list of token embeddings indices to be fed to a model, e.g. `BertTokenizer`
 
 All these classes can be instantiated from pretrained instances and saved locally using two methods:
 
 - `from_pretrained()` let you instantiate a model/configuration/tokenizer from a pretrained version either provided by the library itself (currently 27 models are provided as listed [here](https://huggingface.co/transformers/pretrained_models.html)) or stored locally (or on a server) by the user,
 - `save_pretrained()` let you save a model/configuration/tokenizer locally so that it can be reloaded using `from_pretrained()`.
 
-We'll finish this quickstart tour by going through a few simple quick-start examples to see how we can instantiate and use these classes. The rest of the documentation is organized into two parts:
+We'll finish this quickstart tour by going through a few simple quick-start examples to see how we can instantiate and use these classes. The rest of the documentation is organized in two parts:
 
 - the **MAIN CLASSES** section details the common functionalities/method/attributes of the three main type of classes (configuration, model, tokenizer) plus some optimization related classes provided as utilities for training,
-- the **PACKAGE REFERENCE** section details all the variants of each class for each model architectures and, in particular, the input/output that you should expect when calling each of them.
+- the **PACKAGE REFERENCE** section details all the variants of each class for each model architectures and in particular the input/output that you should expect when calling each of them.
 
 ## Quick tour: Usage
 
 Here are two examples showcasing a few `Bert` and `GPT2` classes and pre-trained models.
 
-See the full API reference for examples of each model class.
+See full API reference for examples for each model class.
 
 ### BERT example
 
@@ -191,7 +191,7 @@ Examples for each model class of each model architecture (Bert, GPT, GPT-2, Tran
 
 #### Using the past
 
-GPT-2, as well as some other models (GPT, XLNet, Transfo-XL, CTRL), make use of a `past` or `mems` attribute which can be used to prevent re-computing the key/value pairs when using sequential decoding. It is useful when generating sequences as a big part of the attention mechanism benefits from previous computations.
+GPT-2 as well as some other models (GPT, XLNet, Transfo-XL, CTRL) make use of a `past` or `mems` attribute which can be used to prevent re-computing the key/value pairs when using sequential decoding. It is useful when generating sequences as a big part of the attention mechanism benefits from previous computations.
 
 Here is a fully-working example using the `past` with `GPT2LMHeadModel` and argmax decoding (which should only be used as an example, as argmax decoding introduces a lot of repetition):
 

docs/source/usage.rst

@@ -404,150 +404,48 @@ Causal language modeling is the task of predicting the token following a sequenc
 model only attends to the left context (tokens on the left of the mask). Such a training is particularly interesting
 for generation tasks.
 
-Usually, the next token is predicted by sampling from the logits of the last hidden state the model produces from the input sequence.
+There is currently no pipeline to do causal language modeling/generation.
 
-Here is an example using the tokenizer and model and leveraging the :func:`~transformers.PreTrainedModel.top_k_top_p_filtering` method to sample the next token following an input sequence of tokens.
-
-::
-
-    ## PYTORCH CODE
-    from transformers import AutoModelWithLMHead, AutoTokenizer, top_k_top_p_filtering
-    import torch
-    from torch.nn import functional as F
-
-
-    tokenizer = AutoTokenizer.from_pretrained("gpt2")
-    model = AutoModelWithLMHead.from_pretrained("gpt2")
-
-    sequence = f"Hugging Face is based in DUMBO, New York City, and "
-
-    input_ids = tokenizer.encode(sequence, return_tensors="pt")
-
-    # get logits of last hidden state
-    next_token_logits = model(input_ids)[0][:, -1, :]
-
-    # filter
-    filtered_next_token_logits = top_k_top_p_filtering(next_token_logits, top_k=50, top_p=1.0)
-
-    # sample
-    probs = F.softmax(filtered_next_token_logits, dim=-1)
-    next_token = torch.multinomial(probs, num_samples=1)
-
-    generated = torch.cat([input_ids, next_token], dim=-1)
-
-    resulting_string = tokenizer.decode(generated.tolist()[0])
-    print(resulting_string)
-    ## TENSORFLOW CODE
-    from transformers import TFAutoModelWithLMHead, AutoTokenizer, tf_top_k_top_p_filtering
-    import tensorflow as tf
-
-    tokenizer = AutoTokenizer.from_pretrained("gpt2")
-    model = TFAutoModelWithLMHead.from_pretrained("gpt2")
-
-    sequence = f"Hugging Face is based in DUMBO, New York City, and "
-
-    input_ids = tokenizer.encode(sequence, return_tensors="tf")
-
-    # get logits of last hidden state
-    next_token_logits = model(input_ids)[0][:, -1, :]
-
-    # filter
-    filtered_next_token_logits = tf_top_k_top_p_filtering(next_token_logits, top_k=50, top_p=1.0)
-
-    # sample
-    next_token = tf.random.categorical(filtered_next_token_logits, dtype=tf.int32, num_samples=1)
-
-    generated = tf.concat([input_ids, next_token], axis=1)
-
-    resulting_string = tokenizer.decode(generated.numpy().tolist()[0])
-    print(resulting_string)
-
-
-This outputs a (hopefully) coherent next token following the original sequence, which is in our case is the word *has*:
-
-::
-
-    Hugging Face is based in DUMBO, New York City, and has
-
-In the next section, we show how this functionality is leveraged in :func:`~transformers.PreTrainedModel.generate` to generate multiple tokens up to a user-defined length.
-
-Text Generation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-In text generation (*a.k.a* *open-ended text generation*) the goal is to create a coherent portion of text that is a continuation from the given context. As an example, is it shown how *GPT-2* can be used in pipelines to generate text. As a default all models apply *Top-K* sampling when used in pipelines as configured in their respective configurations (see `gpt-2 config <https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json>`_ for example).
-
-::
-
-    from transformers import pipeline
-
-    text_generator = pipeline("text-generation")
-    print(text_generator("As far as I am concerned, I will", max_length=50))
-
-
-Here the model generates a random text with a total maximal length of *50* tokens from context *"As far as I am concerned, I will"*.
-The default arguments of ``PreTrainedModel.generate()`` can directly be overriden in the pipeline as is shown above for the argument ``max_length``.
-
-Here is an example for text generation using XLNet and its tokenzier. 
+Here is an example using the tokenizer and model. leveraging the :func:`~transformers.PreTrainedModel.generate` method
+to generate the tokens following the initial sequence in PyTorch, and creating a simple loop in TensorFlow.
 
 ::
 
     ## PYTORCH CODE
     from transformers import AutoModelWithLMHead, AutoTokenizer
 
-    model = AutoModelWithLMHead.from_pretrained("xlnet-base-cased")
-    tokenizer = AutoTokenizer.from_pretrained("xlnet-base-cased")
+    tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    model = AutoModelWithLMHead.from_pretrained("gpt2")
 
-    # Padding text helps XLNet with short prompts - proposed by Aman Rusia in https://github.com/rusiaaman/XLNet-gen#methodology
-    PADDING_TEXT = """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,
-    a young Grigori Rasputin is asked by his father and a group of men to perform magic.
-    Rasputin has a vision and denounces one of the men as a horse thief. Although his
-    father initially slaps him for making such an accusation, Rasputin watches as the
-    man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
-    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
-    with people, even a bishop, begging for his blessing. <eod> </s> <eos>""" 
+    sequence = f"Hugging Face is based in DUMBO, New York City, and is"
 
-    prompt = "Today the weather is really nice and I am planning on "
-    inputs = tokenizer.encode(PADDING_TEXT + prompt, add_special_tokens=False, return_tensors="pt")
-    
-    prompt_length = len(tokenizer.decode(inputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))
-    outputs = model.generate(inputs, max_length=250, do_sample=True, top_p=0.95, top_k=60)
-    generated = prompt + tokenizer.decode(outputs[0])[prompt_length:]
+    input = tokenizer.encode(sequence, return_tensors="pt")
+    generated = model.generate(input, max_length=50, do_sample=True)
 
-    print(generated)
+    resulting_string = tokenizer.decode(generated.tolist()[0])
+    print(resulting_string)
     ## TENSORFLOW CODE
     from transformers import TFAutoModelWithLMHead, AutoTokenizer
+    import tensorflow as tf
 
-    model = TFAutoModelWithLMHead.from_pretrained("xlnet-base-cased")
-    tokenizer = AutoTokenizer.from_pretrained("xlnet-base-cased")
+    tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    model = TFAutoModelWithLMHead.from_pretrained("gpt2")
 
-    # Padding text helps XLNet with short prompts - proposed by Aman Rusia in https://github.com/rusiaaman/XLNet-gen#methodology
-    PADDING_TEXT = """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,
-    a young Grigori Rasputin is asked by his father and a group of men to perform magic.
-    Rasputin has a vision and denounces one of the men as a horse thief. Although his
-    father initially slaps him for making such an accusation, Rasputin watches as the
-    man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
-    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
-    with people, even a bishop, begging for his blessing. <eod> </s> <eos>""" 
+    sequence = f"Hugging Face is based in DUMBO, New York City, and is"
+    input = tokenizer.encode(sequence, return_tensors="tf")
+    generated = model.generate(input, max_length=50, do_sample=True)
 
-    prompt = "Today the weather is really nice and I am planning on "
-    inputs = tokenizer.encode(PADDING_TEXT + prompt, add_special_tokens=False, return_tensors="tf")
+    resulting_string = tokenizer.decode(generated.tolist()[0])
+    print(resulting_string)
 
-    prompt_length = len(tokenizer.decode(inputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))
-    outputs = model.generate(inputs, max_length=250, do_sample=True, top_p=0.95, top_k=60)
-    generated = prompt + tokenizer.decode(outputs[0])[prompt_length:]
 
-    print(generated)
+This outputs a (hopefully) coherent string from the original sequence, as the
+:func:`~transformers.PreTrainedModel.generate` samples from a top_p/tok_k distribution:
 
-Text generation is currently possible with *GPT-2*, *OpenAi-GPT*, *CTRL*, *XLNet*, *Transfo-XL* and *Reformer* in PyTorch and for most models in Tensorflow as well. As can be seen in the example above *XLNet* and *Transfo-xl* often need to be padded to work well.
-GPT-2 is usually a good choice for *open-ended text generation* because it was trained on millions on webpages with a causal language modeling objective.
+::
 
-For more information on how to apply different decoding strategies for text generation, please also refer to our generation blog post `here <https://huggingface.co/blog/how-to-generate>`_.
+    Hugging Face is based in DUMBO, New York City, and is a live-action TV series based on the novel by John
+    Carpenter, and its producers, David Kustlin and Steve Pichar. The film is directed by!
 
 
 Named Entity Recognition

examples/README.md

@@ -1,6 +1,6 @@
-## Examples
+# Examples
 
-Version 2.9 of `transformers` introduces a new [`Trainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py) class for PyTorch, and its equivalent [`TFTrainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer_tf.py) for TF 2.
+Version 2.9 of `transformers` introduces a new `Trainer` class for PyTorch, and its equivalent `TFTrainer` for TF 2.
 
 Here is the list of all our examples:
 - **grouped by task** (all official examples work for multiple models)
@@ -12,24 +12,31 @@ Here is the list of all our examples:
 This is still a work-in-progress – in particular documentation is still sparse – so please **contribute improvements/pull requests.**
 
 
-# The Big Table of Tasks
+## Tasks built on Trainer
 
-| Task | Example datasets | Trainer support | TFTrainer support | pytorch-lightning | Colab
-|---|---|:---:|:---:|:---:|:---:|
-| [**`language-modeling`**](./language-modeling)       | Raw text        | ✅ | -  | -  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)
-| [**`text-classification`**](./text-classification)   | GLUE, XNLI      | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/trainer/01_text_classification.ipynb)
-| [**`token-classification`**](./token-classification) | CoNLL NER       | ✅ | ✅ | ✅ | -
-| [**`multiple-choice`**](./multiple-choice)           | SWAG, RACE, ARC | ✅ | ✅ | -  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb)
-| [**`question-answering`**](./question-answering)     | SQuAD           | -  | ✅ | -  | -
-| [**`text-generation`**](./text-generation)     | -           | -  | - | -  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)
-| [**`distillation`**](./distillation)       | All               | -  | -  | -  | -
-| [**`summarization`**](./summarization)     | CNN/Daily Mail    | -  | -  | -  | -
-| [**`translation`**](./translation)         | WMT               | -  | -  | -  | -
-| [**`bertology`**](./bertology)             | -                 | -  | -  | -  | -
-| [**`adversarial`**](./adversarial)         | HANS              | -  | -  | -  | -
+| Task | Example datasets | Trainer support | TFTrainer support | pytorch-lightning | Colab | One-click Deploy to Azure (wip) | 
+|---|---|:---:|:---:|:---:|:---:|:---:|
+| [`language-modeling`](./language-modeling) | Raw text | ✅ | - | - | - | - |
+| [`text-classification`](./text-classification) | GLUE, XNLI | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/trainer/01_text_classification.ipynb) | [![Deploy to Azure](https://aka.ms/deploytoazurebutton)](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2F101-storage-account-create%2Fazuredeploy.json) |
+| [`token-classification`](./token-classification) | CoNLL NER | ✅ | ✅ | ✅ | - | - |
+| [`multiple-choice`](./multiple-choice) | SWAG, RACE, ARC | ✅ | - | - | - | - |
 
 
-<br>
+
+## Other examples and how-to's
+
+| Section | Description |
+|---|---|
+| [TensorFlow 2.0 models on GLUE](./text-classification) | Examples running BERT TensorFlow 2.0 model on the GLUE tasks. |
+| [Running on TPUs](#running-on-tpus) | Examples on running fine-tuning tasks on Google TPUs to accelerate workloads. |
+| [Language Model training](./language-modeling) | Fine-tuning (or training from scratch) the library models for language modeling on a text dataset. Causal language modeling for GPT/GPT-2, masked language modeling for BERT/RoBERTa. |
+| [Language Generation](./text-generation) | Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL and XLNet. |
+| [GLUE](./text-classification) | Examples running BERT/XLM/XLNet/RoBERTa on the 9 GLUE tasks. Examples feature distributed training as well as half-precision. |
+| [SQuAD](./question-answering) | Using BERT/RoBERTa/XLNet/XLM for question answering, examples with distributed training. |
+| [Multiple Choice](./multiple-choice) | Examples running BERT/XLNet/RoBERTa on the SWAG/RACE/ARC tasks. |
+| [Named Entity Recognition](./token-classification) | Using BERT for Named Entity Recognition (NER) on the CoNLL 2003 dataset, examples with distributed training. |
+| [XNLI](./text-classification) | Examples running BERT/XLM on the XNLI benchmark. |
+| [Adversarial evaluation of model performances](./adversarial) | Testing a model with adversarial evaluation of natural language inference on the Heuristic Analysis for NLI Systems (HANS) dataset (McCoy et al., 2019.) |
 
 ## Important note
 
@@ -44,12 +51,6 @@ pip install .
 pip install -r ./examples/requirements.txt
 ```
 
-## One-click Deploy to Cloud (wip)
-
-#### Azure
-
-[![Deploy to Azure](https://aka.ms/deploytoazurebutton)](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2F101-storage-account-create%2Fazuredeploy.json)
-
 ## Running on TPUs
 
 When using Tensorflow, TPUs are supported out of the box as a `tf.distribute.Strategy`.

examples/benchmarks.py

@@ -478,7 +478,7 @@ def _compute_pytorch(
                             dictionary[model_name]["memory"][batch_size][slice_size] = "N/A"
 
                         if not no_speed:
-                            print_fn("Going through model with sequence of shape {}".format(sequence.shape))
+                            print_fn("Going through model with sequence of shape".format(sequence.shape))
                             runtimes = timeit.repeat(lambda: inference(sequence), repeat=average_over, number=3)
                             average_time = sum(runtimes) / float(len(runtimes)) / 3.0
                             dictionary[model_name]["time"][batch_size][slice_size] = average_time

examples/bertology/run_bertology.py

@@ -64,7 +64,7 @@ def print_2d_tensor(tensor):
 
 
 def compute_heads_importance(
-    args, model, eval_dataloader, compute_entropy=True, compute_importance=True, head_mask=None, actually_pruned=False
+    args, model, eval_dataloader, compute_entropy=True, compute_importance=True, head_mask=None
 ):
     """ This method shows how to compute:
         - head attention entropy
@@ -77,12 +77,7 @@ def compute_heads_importance(
 
     if head_mask is None:
         head_mask = torch.ones(n_layers, n_heads).to(args.device)
-
     head_mask.requires_grad_(requires_grad=True)
-    # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
-    if actually_pruned:
-        head_mask = None
-
     preds = None
     labels = None
     tot_tokens = 0.0
@@ -177,7 +172,6 @@ def mask_heads(args, model, eval_dataloader):
         new_head_mask = new_head_mask.view(-1)
         new_head_mask[current_heads_to_mask] = 0.0
         new_head_mask = new_head_mask.view_as(head_mask)
-        new_head_mask = new_head_mask.clone().detach()
         print_2d_tensor(new_head_mask)
 
         # Compute metric and head importance again
@@ -187,7 +181,7 @@ def mask_heads(args, model, eval_dataloader):
         preds = np.argmax(preds, axis=1) if args.output_mode == "classification" else np.squeeze(preds)
         current_score = glue_compute_metrics(args.task_name, preds, labels)[args.metric_name]
         logger.info(
-            "Masking: current score: %f, remaining heads %d (%.1f percents)",
+            "Masking: current score: %f, remaning heads %d (%.1f percents)",
             current_score,
             new_head_mask.sum(),
             new_head_mask.sum() / new_head_mask.numel() * 100,
@@ -215,23 +209,14 @@ def prune_heads(args, model, eval_dataloader, head_mask):
     original_time = datetime.now() - before_time
 
     original_num_params = sum(p.numel() for p in model.parameters())
-    heads_to_prune = dict(
-        (layer, (1 - head_mask[layer].long()).nonzero().squeeze().tolist()) for layer in range(len(head_mask))
-    )
-
+    heads_to_prune = dict((layer, (1 - head_mask[layer].long()).nonzero().tolist()) for layer in range(len(head_mask)))
     assert sum(len(h) for h in heads_to_prune.values()) == (1 - head_mask.long()).sum().item()
     model.prune_heads(heads_to_prune)
     pruned_num_params = sum(p.numel() for p in model.parameters())
 
     before_time = datetime.now()
     _, _, preds, labels = compute_heads_importance(
-        args,
-        model,
-        eval_dataloader,
-        compute_entropy=False,
-        compute_importance=False,
-        head_mask=None,
-        actually_pruned=True,
+        args, model, eval_dataloader, compute_entropy=False, compute_importance=False, head_mask=None
     )
     preds = np.argmax(preds, axis=1) if args.output_mode == "classification" else np.squeeze(preds)
     score_pruning = glue_compute_metrics(args.task_name, preds, labels)[args.metric_name]
@@ -419,7 +404,7 @@ def main():
     logger.info("Training/evaluation parameters %s", args)
 
     # Prepare dataset for the GLUE task
-    eval_dataset = GlueDataset(args, tokenizer=tokenizer, mode="dev")
+    eval_dataset = GlueDataset(args, tokenizer=tokenizer, evaluate=True)
     if args.data_subset > 0:
         eval_dataset = Subset(eval_dataset, list(range(min(args.data_subset, len(eval_dataset)))))
     eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)

examples/contrib/run_transfo_xl.py

@@ -80,7 +80,7 @@ def main():
 
     # Load a pre-trained model
     model = TransfoXLLMHeadModel.from_pretrained(args.model_name)
-    model.to(device)
+    model = model.to(device)
 
     logger.info(
         "Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}".format(

examples/distillation/distiller.py

@@ -80,7 +80,7 @@ class Distiller:
 
         self.mlm = params.mlm
         if self.mlm:
-            logger.info("Using MLM loss for LM step.")
+            logger.info(f"Using MLM loss for LM step.")
             self.mlm_mask_prop = params.mlm_mask_prop
             assert 0.0 <= self.mlm_mask_prop <= 1.0
             assert params.word_mask + params.word_keep + params.word_rand == 1.0
@@ -91,7 +91,7 @@ class Distiller:
                 self.pred_probs = self.pred_probs.half()
                 self.token_probs = self.token_probs.half()
         else:
-            logger.info("Using CLM loss for LM step.")
+            logger.info(f"Using CLM loss for LM step.")
 
         self.epoch = 0
         self.n_iter = 0
@@ -365,8 +365,8 @@ class Distiller:
             self.end_epoch()
 
         if self.is_master:
-            logger.info("Save very last checkpoint as `pytorch_model.bin`.")
-            self.save_checkpoint(checkpoint_name="pytorch_model.bin")
+            logger.info(f"Save very last checkpoint as `pytorch_model.bin`.")
+            self.save_checkpoint(checkpoint_name=f"pytorch_model.bin")
             logger.info("Training is finished")
 
     def step(self, input_ids: torch.tensor, attention_mask: torch.tensor, lm_labels: torch.tensor):

examples/distillation/scripts/binarized_data.py

@@ -60,7 +60,7 @@ def main():
     with open(args.file_path, "r", encoding="utf8") as fp:
         data = fp.readlines()
 
-    logger.info("Start encoding")
+    logger.info(f"Start encoding")
     logger.info(f"{len(data)} examples to process.")
 
     rslt = []

examples/distillation/scripts/extract.py

@@ -93,7 +93,7 @@ if __name__ == "__main__":
     elif args.model_type == "gpt2":
         for w in ["weight", "bias"]:
             compressed_sd[f"{prefix}.ln_f.{w}"] = state_dict[f"{prefix}.ln_f.{w}"]
-        compressed_sd["lm_head.weight"] = state_dict["lm_head.weight"]
+        compressed_sd[f"lm_head.weight"] = state_dict[f"lm_head.weight"]
 
     print(f"N layers selected for distillation: {std_idx}")
     print(f"Number of params transfered for distillation: {len(compressed_sd.keys())}")

examples/distillation/scripts/extract_distilbert.py

@@ -37,7 +37,7 @@ if __name__ == "__main__":
         model = BertForMaskedLM.from_pretrained(args.model_name)
         prefix = "bert"
     else:
-        raise ValueError('args.model_type should be "bert".')
+        raise ValueError(f'args.model_type should be "bert".')
 
     state_dict = model.state_dict()
     compressed_sd = {}
@@ -78,8 +78,8 @@ if __name__ == "__main__":
             ]
         std_idx += 1
 
-    compressed_sd["vocab_projector.weight"] = state_dict["cls.predictions.decoder.weight"]
-    compressed_sd["vocab_projector.bias"] = state_dict["cls.predictions.bias"]
+    compressed_sd[f"vocab_projector.weight"] = state_dict[f"cls.predictions.decoder.weight"]
+    compressed_sd[f"vocab_projector.bias"] = state_dict[f"cls.predictions.bias"]
     if args.vocab_transform:
         for w in ["weight", "bias"]:
             compressed_sd[f"vocab_transform.{w}"] = state_dict[f"cls.predictions.transform.dense.{w}"]

examples/distillation/train.py

@@ -273,7 +273,7 @@ def main():
         token_probs = None
 
     train_lm_seq_dataset = LmSeqsDataset(params=args, data=data)
-    logger.info("Data loader created.")
+    logger.info(f"Data loader created.")
 
     # STUDENT #
     logger.info(f"Loading student config from {args.student_config}")
@@ -288,7 +288,7 @@ def main():
 
     if args.n_gpu > 0:
         student.to(f"cuda:{args.local_rank}")
-    logger.info("Student loaded.")
+    logger.info(f"Student loaded.")
 
     # TEACHER #
     teacher = teacher_model_class.from_pretrained(args.teacher_name, output_hidden_states=True)

examples/language-modeling/run_language_modeling.py

@@ -115,13 +115,15 @@ class DataTrainingArguments:
     )
 
 
-def get_dataset(args: DataTrainingArguments, tokenizer: PreTrainedTokenizer, evaluate=False):
+def get_dataset(args: DataTrainingArguments, tokenizer: PreTrainedTokenizer, evaluate=False, local_rank=-1):
     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)
+        return LineByLineTextDataset(
+            tokenizer=tokenizer, file_path=file_path, block_size=args.block_size, local_rank=local_rank
+        )
     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, local_rank=local_rank,
         )
 
 
@@ -218,9 +220,16 @@ def main():
         data_args.block_size = min(data_args.block_size, tokenizer.max_len)
 
     # 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, local_rank=training_args.local_rank)
+        if training_args.do_train
+        else None
+    )
+    eval_dataset = (
+        get_dataset(data_args, tokenizer=tokenizer, local_rank=training_args.local_rank, evaluate=True)
+        if training_args.do_eval
+        else None
+    )
     data_collator = DataCollatorForLanguageModeling(
         tokenizer=tokenizer, mlm=data_args.mlm, mlm_probability=data_args.mlm_probability
     )
@@ -251,21 +260,20 @@ def main():
 
     # Evaluation
     results = {}
-    if training_args.do_eval:
+    if training_args.do_eval and training_args.local_rank in [-1, 0]:
         logger.info("*** Evaluate ***")
 
         eval_output = trainer.evaluate()
 
-        perplexity = math.exp(eval_output["eval_loss"])
+        perplexity = math.exp(eval_output["loss"])
         result = {"perplexity": perplexity}
 
         output_eval_file = os.path.join(training_args.output_dir, "eval_results_lm.txt")
-        if trainer.is_world_master():
-            with open(output_eval_file, "w") as writer:
-                logger.info("***** Eval results *****")
-                for key in sorted(result.keys()):
-                    logger.info("  %s = %s", key, str(result[key]))
-                    writer.write("%s = %s\n" % (key, str(result[key])))
+        with open(output_eval_file, "w") as writer:
+            logger.info("***** Eval results *****")
+            for key in sorted(result.keys()):
+                logger.info("  %s = %s", key, str(result[key]))
+                writer.write("%s = %s\n" % (key, str(result[key])))
 
         results.update(result)
 

examples/multiple-choice/README.md

@@ -29,28 +29,3 @@ Training with the defined hyper-parameters yields the following results:
 eval_acc = 0.8338998300509847
 eval_loss = 0.44457291918821606
 ```
-
-
-## Tensorflow
-
-```bash
-export SWAG_DIR=/path/to/swag_data_dir
-python ./examples/multiple-choice/run_tf_multiple_choice.py \
---task_name swag \
---model_name_or_path bert-base-cased \
---do_train \
---do_eval \
---data_dir $SWAG_DIR \
---learning_rate 5e-5 \
---num_train_epochs 3 \
---max_seq_length 80 \
---output_dir models_bert/swag_base \
---per_gpu_eval_batch_size=16 \
---per_gpu_train_batch_size=16 \
---logging-dir logs \
---gradient_accumulation_steps 2 \
---overwrite_output
-```
-
-# Run it in colab
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb)

examples/multiple-choice/run_multiple_choice.py

@@ -159,6 +159,7 @@ def main():
             max_seq_length=data_args.max_seq_length,
             overwrite_cache=data_args.overwrite_cache,
             mode=Split.train,
+            local_rank=training_args.local_rank,
         )
         if training_args.do_train
         else None
@@ -171,6 +172,7 @@ def main():
             max_seq_length=data_args.max_seq_length,
             overwrite_cache=data_args.overwrite_cache,
             mode=Split.dev,
+            local_rank=training_args.local_rank,
         )
         if training_args.do_eval
         else None
@@ -202,20 +204,19 @@ def main():
 
     # Evaluation
     results = {}
-    if training_args.do_eval:
+    if training_args.do_eval and training_args.local_rank in [-1, 0]:
         logger.info("*** Evaluate ***")
 
         result = trainer.evaluate()
 
         output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
-        if trainer.is_world_master():
-            with open(output_eval_file, "w") as writer:
-                logger.info("***** Eval results *****")
-                for key, value in result.items():
-                    logger.info("  %s = %s", key, value)
-                    writer.write("%s = %s\n" % (key, value))
+        with open(output_eval_file, "w") as writer:
+            logger.info("***** Eval results *****")
+            for key, value in result.items():
+                logger.info("  %s = %s", key, value)
+                writer.write("%s = %s\n" % (key, value))
 
-                results.update(result)
+            results.update(result)
 
     return results
 

examples/multiple-choice/run_tf_multiple_choice.py

@@ -1,211 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# 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 multiple choice (Bert, Roberta, XLNet)."""
-
-
-import logging
-import os
-from dataclasses import dataclass, field
-from typing import Dict, Optional
-
-import numpy as np
-
-from transformers import (
-    AutoConfig,
-    AutoTokenizer,
-    EvalPrediction,
-    HfArgumentParser,
-    TFAutoModelForMultipleChoice,
-    TFTrainer,
-    TFTrainingArguments,
-    set_seed,
-)
-from utils_multiple_choice import Split, TFMultipleChoiceDataset, processors
-
-
-logger = logging.getLogger(__name__)
-
-
-def simple_accuracy(preds, labels):
-    return (preds == labels).mean()
-
-
-@dataclass
-class ModelArguments:
-    """
-    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
-    """
-
-    model_name_or_path: str = field(
-        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
-    )
-    config_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
-    )
-    tokenizer_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
-    )
-    cache_dir: Optional[str] = field(
-        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
-    )
-
-
-@dataclass
-class DataTrainingArguments:
-    """
-    Arguments pertaining to what data we are going to input our model for training and eval.
-    """
-
-    task_name: str = field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys())})
-    data_dir: str = field(metadata={"help": "Should contain the data files for the task."})
-    max_seq_length: int = field(
-        default=128,
-        metadata={
-            "help": "The maximum total input sequence length after tokenization. Sequences longer "
-            "than this will be truncated, sequences shorter will be padded."
-        },
-    )
-    overwrite_cache: bool = field(
-        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
-    )
-
-
-def main():
-    # See all possible arguments in src/transformers/training_args.py
-    # or by passing the --help flag to this script.
-    # We now keep distinct sets of args, for a cleaner separation of concerns.
-
-    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
-    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
-    if (
-        os.path.exists(training_args.output_dir)
-        and os.listdir(training_args.output_dir)
-        and training_args.do_train
-        and not training_args.overwrite_output_dir
-    ):
-        raise ValueError(
-            f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
-        )
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    logger.warning(
-        "device: %s, n_gpu: %s, 16-bits training: %s", training_args.device, training_args.n_gpu, training_args.fp16,
-    )
-    logger.info("Training/evaluation parameters %s", training_args)
-
-    # Set seed
-    set_seed(training_args.seed)
-
-    try:
-        processor = processors[data_args.task_name]()
-        label_list = processor.get_labels()
-        num_labels = len(label_list)
-    except KeyError:
-        raise ValueError("Task not found: %s" % (data_args.task_name))
-
-    # Load pretrained model and tokenizer
-    #
-    # Distributed training:
-    # The .from_pretrained methods guarantee that only one local process can concurrently
-    # download model & vocab.
-    config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
-        num_labels=num_labels,
-        finetuning_task=data_args.task_name,
-        cache_dir=model_args.cache_dir,
-    )
-    tokenizer = AutoTokenizer.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
-        cache_dir=model_args.cache_dir,
-    )
-    with training_args.strategy.scope():
-        model = TFAutoModelForMultipleChoice.from_pretrained(
-            model_args.model_name_or_path,
-            from_pt=bool(".bin" in model_args.model_name_or_path),
-            config=config,
-            cache_dir=model_args.cache_dir,
-        )
-    # Get datasets
-    train_dataset = (
-        TFMultipleChoiceDataset(
-            data_dir=data_args.data_dir,
-            tokenizer=tokenizer,
-            task=data_args.task_name,
-            max_seq_length=data_args.max_seq_length,
-            overwrite_cache=data_args.overwrite_cache,
-            mode=Split.train,
-        )
-        if training_args.do_train
-        else None
-    )
-    eval_dataset = (
-        TFMultipleChoiceDataset(
-            data_dir=data_args.data_dir,
-            tokenizer=tokenizer,
-            task=data_args.task_name,
-            max_seq_length=data_args.max_seq_length,
-            overwrite_cache=data_args.overwrite_cache,
-            mode=Split.dev,
-        )
-        if training_args.do_eval
-        else None
-    )
-
-    def compute_metrics(p: EvalPrediction) -> Dict:
-        preds = np.argmax(p.predictions, axis=1)
-        return {"acc": simple_accuracy(preds, p.label_ids)}
-
-    # Initialize our Trainer
-    trainer = TFTrainer(
-        model=model,
-        args=training_args,
-        train_dataset=train_dataset.get_dataset() if train_dataset else None,
-        eval_dataset=eval_dataset.get_dataset() if eval_dataset else None,
-        compute_metrics=compute_metrics,
-    )
-
-    # Training
-    if training_args.do_train:
-        trainer.train()
-        trainer.save_model()
-        tokenizer.save_pretrained(training_args.output_dir)
-    # Evaluation
-    results = {}
-    if training_args.do_eval:
-        logger.info("*** Evaluate ***")
-
-        result = trainer.evaluate()
-
-        output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
-        with open(output_eval_file, "w") as writer:
-            logger.info("***** Eval results *****")
-            for key, value in result.items():
-                logger.info("  %s = %s", key, value)
-                writer.write("%s = %s\n" % (key, value))
-
-            results.update(result)
-
-    return results
-
-
-if __name__ == "__main__":
-    main()

examples/multiple-choice/utils_multiple_choice.py

@@ -25,10 +25,11 @@ from dataclasses import dataclass
 from enum import Enum
 from typing import List, Optional
 
+import torch
 import tqdm
-from filelock import FileLock
+from torch.utils.data.dataset import Dataset
 
-from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
+from transformers import PreTrainedTokenizer, torch_distributed_zero_first
 
 
 logger = logging.getLogger(__name__)
@@ -75,159 +76,66 @@ class Split(Enum):
     test = "test"
 
 
-if is_torch_available():
-    import torch
-    from torch.utils.data.dataset import Dataset
+class MultipleChoiceDataset(Dataset):
+    """
+    This will be superseded by a framework-agnostic approach
+    soon.
+    """
 
-    class MultipleChoiceDataset(Dataset):
-        """
-        This will be superseded by a framework-agnostic approach
-        soon.
-        """
+    features: List[InputFeatures]
 
-        features: List[InputFeatures]
-
-        def __init__(
-            self,
-            data_dir: str,
-            tokenizer: PreTrainedTokenizer,
-            task: str,
-            max_seq_length: Optional[int] = None,
-            overwrite_cache=False,
-            mode: Split = Split.train,
-        ):
-            processor = processors[task]()
-
-            cached_features_file = os.path.join(
-                data_dir,
-                "cached_{}_{}_{}_{}".format(mode.value, tokenizer.__class__.__name__, str(max_seq_length), task,),
-            )
+    def __init__(
+        self,
+        data_dir: str,
+        tokenizer: PreTrainedTokenizer,
+        task: str,
+        max_seq_length: Optional[int] = None,
+        overwrite_cache=False,
+        mode: Split = Split.train,
+        local_rank=-1,
+    ):
+        processor = processors[task]()
 
+        cached_features_file = os.path.join(
+            data_dir,
+            "cached_{}_{}_{}_{}".format(mode.value, tokenizer.__class__.__name__, str(max_seq_length), task,),
+        )
+        with torch_distributed_zero_first(local_rank):
             # Make sure only the first process in distributed training processes the dataset,
             # and the others will use the cache.
-            lock_path = cached_features_file + ".lock"
-            with FileLock(lock_path):
 
-                if os.path.exists(cached_features_file) and not overwrite_cache:
-                    logger.info(f"Loading features from cached file {cached_features_file}")
-                    self.features = torch.load(cached_features_file)
+            if os.path.exists(cached_features_file) and not overwrite_cache:
+                logger.info(f"Loading features from cached file {cached_features_file}")
+                self.features = torch.load(cached_features_file)
+            else:
+                logger.info(f"Creating features from dataset file at {data_dir}")
+                label_list = processor.get_labels()
+                if mode == Split.dev:
+                    examples = processor.get_dev_examples(data_dir)
+                elif mode == Split.test:
+                    examples = processor.get_test_examples(data_dir)
                 else:
-                    logger.info(f"Creating features from dataset file at {data_dir}")
-                    label_list = processor.get_labels()
-                    if mode == Split.dev:
-                        examples = processor.get_dev_examples(data_dir)
-                    elif mode == Split.test:
-                        examples = processor.get_test_examples(data_dir)
-                    else:
-                        examples = processor.get_train_examples(data_dir)
-                    logger.info("Training examples: %s", len(examples))
-                    # TODO clean up all this to leverage built-in features of tokenizers
-                    self.features = convert_examples_to_features(
-                        examples,
-                        label_list,
-                        max_seq_length,
-                        tokenizer,
-                        pad_on_left=bool(tokenizer.padding_side == "left"),
-                        pad_token=tokenizer.pad_token_id,
-                        pad_token_segment_id=tokenizer.pad_token_type_id,
-                    )
+                    examples = processor.get_train_examples(data_dir)
+                logger.info("Training examples: %s", len(examples))
+                # TODO clean up all this to leverage built-in features of tokenizers
+                self.features = convert_examples_to_features(
+                    examples,
+                    label_list,
+                    max_seq_length,
+                    tokenizer,
+                    pad_on_left=bool(tokenizer.padding_side == "left"),
+                    pad_token=tokenizer.pad_token_id,
+                    pad_token_segment_id=tokenizer.pad_token_type_id,
+                )
+                if local_rank in [-1, 0]:
                     logger.info("Saving features into cached file %s", cached_features_file)
                     torch.save(self.features, cached_features_file)
 
-        def __len__(self):
-            return len(self.features)
+    def __len__(self):
+        return len(self.features)
 
-        def __getitem__(self, i) -> InputFeatures:
-            return self.features[i]
-
-
-if is_tf_available():
-    import tensorflow as tf
-
-    class TFMultipleChoiceDataset:
-        """
-        This will be superseded by a framework-agnostic approach
-        soon.
-        """
-
-        features: List[InputFeatures]
-
-        def __init__(
-            self,
-            data_dir: str,
-            tokenizer: PreTrainedTokenizer,
-            task: str,
-            max_seq_length: Optional[int] = 128,
-            overwrite_cache=False,
-            mode: Split = Split.train,
-        ):
-            processor = processors[task]()
-
-            logger.info(f"Creating features from dataset file at {data_dir}")
-            label_list = processor.get_labels()
-            if mode == Split.dev:
-                examples = processor.get_dev_examples(data_dir)
-            elif mode == Split.test:
-                examples = processor.get_test_examples(data_dir)
-            else:
-                examples = processor.get_train_examples(data_dir)
-            logger.info("Training examples: %s", len(examples))
-            # TODO clean up all this to leverage built-in features of tokenizers
-            self.features = convert_examples_to_features(
-                examples,
-                label_list,
-                max_seq_length,
-                tokenizer,
-                pad_on_left=bool(tokenizer.padding_side == "left"),
-                pad_token=tokenizer.pad_token_id,
-                pad_token_segment_id=tokenizer.pad_token_type_id,
-            )
-
-            def gen():
-                for (ex_index, ex) in tqdm.tqdm(enumerate(self.features), desc="convert examples to features"):
-                    if ex_index % 10000 == 0:
-                        logger.info("Writing example %d of %d" % (ex_index, len(examples)))
-
-                    yield (
-                        {
-                            "example_id": 0,
-                            "input_ids": ex.input_ids,
-                            "attention_mask": ex.attention_mask,
-                            "token_type_ids": ex.token_type_ids,
-                        },
-                        ex.label,
-                    )
-
-            self.dataset = tf.data.Dataset.from_generator(
-                gen,
-                (
-                    {
-                        "example_id": tf.int32,
-                        "input_ids": tf.int32,
-                        "attention_mask": tf.int32,
-                        "token_type_ids": tf.int32,
-                    },
-                    tf.int64,
-                ),
-                (
-                    {
-                        "example_id": tf.TensorShape([]),
-                        "input_ids": tf.TensorShape([None, None]),
-                        "attention_mask": tf.TensorShape([None, None]),
-                        "token_type_ids": tf.TensorShape([None, None]),
-                    },
-                    tf.TensorShape([]),
-                ),
-            )
-
-        def get_dataset(self):
-            return self.dataset
-
-        def __len__(self):
-            return len(self.features)
-
-        def __getitem__(self, i) -> InputFeatures:
-            return self.features[i]
+    def __getitem__(self, i) -> InputFeatures:
+        return self.features[i]
 
 
 class DataProcessor:
@@ -317,52 +225,6 @@ class RaceProcessor(DataProcessor):
         return examples
 
 
-class SynonymProcessor(DataProcessor):
-    """Processor for the Synonym data set."""
-
-    def get_train_examples(self, data_dir):
-        """See base class."""
-        logger.info("LOOKING AT {} train".format(data_dir))
-        return self._create_examples(self._read_csv(os.path.join(data_dir, "mctrain.csv")), "train")
-
-    def get_dev_examples(self, data_dir):
-        """See base class."""
-        logger.info("LOOKING AT {} dev".format(data_dir))
-        return self._create_examples(self._read_csv(os.path.join(data_dir, "mchp.csv")), "dev")
-
-    def get_test_examples(self, data_dir):
-        """See base class."""
-        logger.info("LOOKING AT {} dev".format(data_dir))
-
-        return self._create_examples(self._read_csv(os.path.join(data_dir, "mctest.csv")), "test")
-
-    def get_labels(self):
-        """See base class."""
-        return ["0", "1", "2", "3", "4"]
-
-    def _read_csv(self, input_file):
-        with open(input_file, "r", encoding="utf-8") as f:
-            return list(csv.reader(f))
-
-    def _create_examples(self, lines: List[List[str]], type: str):
-        """Creates examples for the training and dev sets."""
-
-        examples = [
-            InputExample(
-                example_id=line[0],
-                question="",  # in the swag dataset, the
-                # common beginning of each
-                # choice is stored in "sent2".
-                contexts=[line[1], line[1], line[1], line[1], line[1]],
-                endings=[line[2], line[3], line[4], line[5], line[6]],
-                label=line[7],
-            )
-            for line in lines  # we skip the line with the column names
-        ]
-
-        return examples
-
-
 class SwagProcessor(DataProcessor):
     """Processor for the SWAG data set."""
 
@@ -535,12 +397,7 @@ def convert_examples_to_features(
                 text_b = example.question + " " + ending
 
             inputs = tokenizer.encode_plus(
-                text_a,
-                text_b,
-                add_special_tokens=True,
-                max_length=max_length,
-                pad_to_max_length=True,
-                return_overflowing_tokens=True,
+                text_a, text_b, add_special_tokens=True, max_length=max_length, pad_to_max_length=True,
             )
             if "num_truncated_tokens" in inputs and inputs["num_truncated_tokens"] > 0:
                 logger.info(
@@ -578,5 +435,7 @@ def convert_examples_to_features(
     return features
 
 
-processors = {"race": RaceProcessor, "swag": SwagProcessor, "arc": ArcProcessor, "syn": SynonymProcessor}
-MULTIPLE_CHOICE_TASKS_NUM_LABELS = {"race", 4, "swag", 4, "arc", 4, "syn", 5}
+processors = {"race": RaceProcessor, "swag": SwagProcessor, "arc": ArcProcessor}
+
+
+MULTIPLE_CHOICE_TASKS_NUM_LABELS = {"race", 4, "swag", 4, "arc", 4}

examples/question-answering/README.md

@@ -157,23 +157,3 @@ Larger batch size may improve the performance while costing more memory.
 }
 ```
 
-## SQuAD with the Tensorflow Trainer
-
-```bash
-python run_tf_squad.py \
-    --model_name_or_path bert-base-uncased \
-    --output_dir model \
-    --max-seq-length 384 \
-    --num_train_epochs 2 \
-    --per_gpu_train_batch_size 8 \
-    --per_gpu_eval_batch_size 16 \
-    --do_train \
-    --logging_dir logs \
-    --mode question-answering \
-    --logging_steps 10 \
-    --learning_rate 3e-5 \
-    --doc_stride 128 \
-    --optimizer_name adamw
-```
-
-For the moment the evaluation is not available in the Tensorflow Trainer only the training.

examples/question-answering/run_tf_squad.py

@@ -1,237 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# 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.
-""" Fine-tuning the library models for question-answering."""
-
-
-import logging
-import os
-from dataclasses import dataclass, field
-from typing import Optional
-
-from transformers import (
-    AutoConfig,
-    AutoTokenizer,
-    HfArgumentParser,
-    TFAutoModelForQuestionAnswering,
-    TFTrainer,
-    TFTrainingArguments,
-    squad_convert_examples_to_features,
-)
-from transformers.data.processors.squad import SquadV1Processor, SquadV2Processor
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class ModelArguments:
-    """
-    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
-    """
-
-    model_name_or_path: str = field(
-        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
-    )
-    config_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
-    )
-    tokenizer_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
-    )
-    use_fast: bool = field(default=False, metadata={"help": "Set this flag to use fast tokenization."})
-    # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
-    # or just modify its tokenizer_config.json.
-    cache_dir: Optional[str] = field(
-        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
-    )
-
-
-@dataclass
-class DataTrainingArguments:
-    """
-    Arguments pertaining to what data we are going to input our model for training and eval.
-    """
-
-    data_dir: Optional[str] = field(
-        default=None, metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."}
-    )
-    max_seq_length: int = field(
-        default=128,
-        metadata={
-            "help": "The maximum total input sequence length after tokenization. Sequences longer "
-            "than this will be truncated, sequences shorter will be padded."
-        },
-    )
-    doc_stride: int = field(
-        default=128,
-        metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."},
-    )
-    max_query_length: int = field(
-        default=64,
-        metadata={
-            "help": "The maximum number of tokens for the question. Questions longer than this will "
-            "be truncated to this length."
-        },
-    )
-    max_answer_length: int = field(
-        default=30,
-        metadata={
-            "help": "The maximum length of an answer that can be generated. This is needed because the start "
-            "and end predictions are not conditioned on one another."
-        },
-    )
-    overwrite_cache: bool = field(
-        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
-    )
-    version_2_with_negative: bool = field(
-        default=False, metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."}
-    )
-    null_score_diff_threshold: float = field(
-        default=0.0, metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}
-    )
-    n_best_size: int = field(
-        default=20, metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}
-    )
-    lang_id: int = field(
-        default=0,
-        metadata={
-            "help": "language id of input for language-specific xlm models (see tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
-        },
-    )
-
-
-def main():
-    # See all possible arguments in src/transformers/training_args.py
-    # or by passing the --help flag to this script.
-    # We now keep distinct sets of args, for a cleaner separation of concerns.
-    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
-    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
-    if (
-        os.path.exists(training_args.output_dir)
-        and os.listdir(training_args.output_dir)
-        and training_args.do_train
-        and not training_args.overwrite_output_dir
-    ):
-        raise ValueError(
-            f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
-        )
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    logger.info(
-        "n_gpu: %s, distributed training: %s, 16-bits training: %s",
-        training_args.n_gpu,
-        bool(training_args.n_gpu > 1),
-        training_args.fp16,
-    )
-    logger.info("Training/evaluation parameters %s", training_args)
-
-    # Prepare Question-Answering task
-    # Load pretrained model and tokenizer
-    #
-    # Distributed training:
-    # The .from_pretrained methods guarantee that only one local process can concurrently
-    # download model & vocab.
-
-    config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
-        cache_dir=model_args.cache_dir,
-    )
-    tokenizer = AutoTokenizer.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
-        cache_dir=model_args.cache_dir,
-        use_fast=model_args.use_fast,
-    )
-
-    with training_args.strategy.scope():
-        model = TFAutoModelForQuestionAnswering.from_pretrained(
-            model_args.model_name_or_path,
-            from_pt=bool(".bin" in model_args.model_name_or_path),
-            config=config,
-            cache_dir=model_args.cache_dir,
-        )
-
-    # Get datasets
-    if not data_args.data_dir:
-        if data_args.version_2_with_negative:
-            logger.warn("tensorflow_datasets does not handle version 2 of SQuAD. Switch to version 1 automatically")
-
-        try:
-            import tensorflow_datasets as tfds
-        except ImportError:
-            raise ImportError("If not data_dir is specified, tensorflow_datasets needs to be installed.")
-
-        tfds_examples = tfds.load("squad")
-        train_examples = (
-            SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=False)
-            if training_args.do_train
-            else None
-        )
-        eval_examples = (
-            SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=True)
-            if training_args.do_eval
-            else None
-        )
-    else:
-        processor = SquadV2Processor() if data_args.version_2_with_negative else SquadV1Processor()
-        train_examples = processor.get_train_examples(data_args.data_dir) if training_args.do_train else None
-        eval_examples = processor.get_dev_examples(data_args.data_dir) if training_args.do_eval else None
-
-    train_dataset = (
-        squad_convert_examples_to_features(
-            examples=train_examples,
-            tokenizer=tokenizer,
-            max_seq_length=data_args.max_seq_length,
-            doc_stride=data_args.doc_stride,
-            max_query_length=data_args.max_query_length,
-            is_training=True,
-            return_dataset="tf",
-        )
-        if training_args.do_train
-        else None
-    )
-
-    eval_dataset = (
-        squad_convert_examples_to_features(
-            examples=eval_examples,
-            tokenizer=tokenizer,
-            max_seq_length=data_args.max_seq_length,
-            doc_stride=data_args.doc_stride,
-            max_query_length=data_args.max_query_length,
-            is_training=False,
-            return_dataset="tf",
-        )
-        if training_args.do_eval
-        else None
-    )
-
-    # Initialize our Trainer
-    trainer = TFTrainer(model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset,)
-
-    # Training
-    if training_args.do_train:
-        trainer.train()
-        trainer.save_model()
-        tokenizer.save_pretrained(training_args.output_dir)
-
-
-if __name__ == "__main__":
-    main()

examples/test_examples.py

@@ -72,7 +72,7 @@ class ExamplesTests(unittest.TestCase):
             """.split()
         with patch.object(sys, "argv", testargs):
             result = run_glue.main()
-            del result["eval_loss"]
+            del result["loss"]
             for value in result.values():
                 self.assertGreaterEqual(value, 0.75)
 

examples/text-classification/run_glue.py

@@ -135,8 +135,7 @@ def main():
 
     # Get datasets
     train_dataset = GlueDataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
-    eval_dataset = GlueDataset(data_args, tokenizer=tokenizer, mode="dev") if training_args.do_eval else None
-    test_dataset = GlueDataset(data_args, tokenizer=tokenizer, mode="test") if training_args.do_predict else None
+    eval_dataset = GlueDataset(data_args, tokenizer=tokenizer, evaluate=True) if training_args.do_eval else None
 
     def compute_metrics(p: EvalPrediction) -> Dict:
         if output_mode == "classification":
@@ -166,57 +165,31 @@ def main():
             tokenizer.save_pretrained(training_args.output_dir)
 
     # Evaluation
-    eval_results = {}
-    if training_args.do_eval:
+    results = {}
+    if training_args.do_eval and training_args.local_rank in [-1, 0]:
         logger.info("*** Evaluate ***")
 
         # Loop to handle MNLI double evaluation (matched, mis-matched)
         eval_datasets = [eval_dataset]
         if data_args.task_name == "mnli":
             mnli_mm_data_args = dataclasses.replace(data_args, task_name="mnli-mm")
-            eval_datasets.append(GlueDataset(mnli_mm_data_args, tokenizer=tokenizer, mode="dev"))
+            eval_datasets.append(GlueDataset(mnli_mm_data_args, tokenizer=tokenizer, evaluate=True))
 
         for eval_dataset in eval_datasets:
-            eval_result = trainer.evaluate(eval_dataset=eval_dataset)
+            result = trainer.evaluate(eval_dataset=eval_dataset)
 
             output_eval_file = os.path.join(
                 training_args.output_dir, f"eval_results_{eval_dataset.args.task_name}.txt"
             )
-            if trainer.is_world_master():
-                with open(output_eval_file, "w") as writer:
-                    logger.info("***** Eval results {} *****".format(eval_dataset.args.task_name))
-                    for key, value in eval_result.items():
-                        logger.info("  %s = %s", key, value)
-                        writer.write("%s = %s\n" % (key, value))
+            with open(output_eval_file, "w") as writer:
+                logger.info("***** Eval results {} *****".format(eval_dataset.args.task_name))
+                for key, value in result.items():
+                    logger.info("  %s = %s", key, value)
+                    writer.write("%s = %s\n" % (key, value))
 
-            eval_results.update(eval_result)
+            results.update(result)
 
-    if training_args.do_predict:
-        logging.info("*** Test ***")
-        test_datasets = [test_dataset]
-        if data_args.task_name == "mnli":
-            mnli_mm_data_args = dataclasses.replace(data_args, task_name="mnli-mm")
-            test_datasets.append(GlueDataset(mnli_mm_data_args, tokenizer=tokenizer, mode="test"))
-
-        for test_dataset in test_datasets:
-            predictions = trainer.predict(test_dataset=test_dataset).predictions
-            if output_mode == "classification":
-                predictions = np.argmax(predictions, axis=1)
-
-            output_test_file = os.path.join(
-                training_args.output_dir, f"test_results_{test_dataset.args.task_name}.txt"
-            )
-            if trainer.is_world_master():
-                with open(output_test_file, "w") as writer:
-                    logger.info("***** Test results {} *****".format(test_dataset.args.task_name))
-                    writer.write("index\tprediction\n")
-                    for index, item in enumerate(predictions):
-                        if output_mode == "regression":
-                            writer.write("%d\t%3.3f\n" % (index, item))
-                        else:
-                            item = test_dataset.get_labels()[item]
-                            writer.write("%d\t%s\n" % (index, item))
-    return eval_results
+    return results
 
 
 def _mp_fn(index):

examples/token-classification/README.md

@@ -1,7 +1,7 @@
 ## Named Entity Recognition
 
-Based on the scripts [`run_ner.py`](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner.py) for Pytorch and
-[`run_tf_ner.py`](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_tf_ner.py) for Tensorflow 2.
+Based on the scripts [`run_ner.py`](https://github.com/huggingface/transformers/blob/master/examples/ner/run_ner.py) for Pytorch and
+[`run_tf_ner.py`](https://github.com/huggingface/transformers/blob/master/examples/ner/run_tf_ner.py) for Tensorflow 2.
 This example fine-tune Bert Multilingual on GermEval 2014 (German NER).
 Details and results for the fine-tuning provided by @stefan-it.
 

examples/token-classification/run_ner.py

@@ -171,6 +171,7 @@ def main():
             max_seq_length=data_args.max_seq_length,
             overwrite_cache=data_args.overwrite_cache,
             mode=Split.train,
+            local_rank=training_args.local_rank,
         )
         if training_args.do_train
         else None
@@ -184,6 +185,7 @@ def main():
             max_seq_length=data_args.max_seq_length,
             overwrite_cache=data_args.overwrite_cache,
             mode=Split.dev,
+            local_rank=training_args.local_rank,
         )
         if training_args.do_eval
         else None
@@ -235,23 +237,22 @@ def main():
 
     # Evaluation
     results = {}
-    if training_args.do_eval:
+    if training_args.do_eval and training_args.local_rank in [-1, 0]:
         logger.info("*** Evaluate ***")
 
         result = trainer.evaluate()
 
         output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
-        if trainer.is_world_master():
-            with open(output_eval_file, "w") as writer:
-                logger.info("***** Eval results *****")
-                for key, value in result.items():
-                    logger.info("  %s = %s", key, value)
-                    writer.write("%s = %s\n" % (key, value))
+        with open(output_eval_file, "w") as writer:
+            logger.info("***** Eval results *****")
+            for key, value in result.items():
+                logger.info("  %s = %s", key, value)
+                writer.write("%s = %s\n" % (key, value))
 
             results.update(result)
 
     # Predict
-    if training_args.do_predict:
+    if training_args.do_predict and training_args.local_rank in [-1, 0]:
         test_dataset = NerDataset(
             data_dir=data_args.data_dir,
             tokenizer=tokenizer,
@@ -260,36 +261,33 @@ def main():
             max_seq_length=data_args.max_seq_length,
             overwrite_cache=data_args.overwrite_cache,
             mode=Split.test,
+            local_rank=training_args.local_rank,
         )
 
         predictions, label_ids, metrics = trainer.predict(test_dataset)
         preds_list, _ = align_predictions(predictions, label_ids)
 
         output_test_results_file = os.path.join(training_args.output_dir, "test_results.txt")
-        if trainer.is_world_master():
-            with open(output_test_results_file, "w") as writer:
-                for key, value in metrics.items():
-                    logger.info("  %s = %s", key, value)
-                    writer.write("%s = %s\n" % (key, value))
+        with open(output_test_results_file, "w") as writer:
+            for key, value in metrics.items():
+                logger.info("  %s = %s", key, value)
+                writer.write("%s = %s\n" % (key, value))
 
         # Save predictions
         output_test_predictions_file = os.path.join(training_args.output_dir, "test_predictions.txt")
-        if trainer.is_world_master():
-            with open(output_test_predictions_file, "w") as writer:
-                with open(os.path.join(data_args.data_dir, "test.txt"), "r") as f:
-                    example_id = 0
-                    for line in f:
-                        if line.startswith("-DOCSTART-") or line == "" or line == "\n":
-                            writer.write(line)
-                            if not preds_list[example_id]:
-                                example_id += 1
-                        elif preds_list[example_id]:
-                            output_line = line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"
-                            writer.write(output_line)
-                        else:
-                            logger.warning(
-                                "Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0]
-                            )
+        with open(output_test_predictions_file, "w") as writer:
+            with open(os.path.join(data_args.data_dir, "test.txt"), "r") as f:
+                example_id = 0
+                for line in f:
+                    if line.startswith("-DOCSTART-") or line == "" or line == "\n":
+                        writer.write(line)
+                        if not preds_list[example_id]:
+                            example_id += 1
+                    elif preds_list[example_id]:
+                        output_line = line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"
+                        writer.write(output_line)
+                    else:
+                        logger.warning("Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0])
 
     return results
 

examples/token-classification/test_ner_examples.py

@@ -6,7 +6,7 @@ from unittest.mock import patch
 import run_ner
 
 
-logging.basicConfig(level=logging.INFO)
+logging.basicConfig(level=logging.DEBUG)
 
 logger = logging.getLogger()
 
@@ -30,4 +30,4 @@ class ExamplesTests(unittest.TestCase):
             """.split()
         with patch.object(sys, "argv", ["run.py"] + testargs):
             result = run_ner.main()
-            self.assertLess(result["eval_loss"], 1.5)
+            self.assertLess(result["loss"], 1.5)

examples/token-classification/utils_ner.py

@@ -22,8 +22,6 @@ from dataclasses import dataclass
 from enum import Enum
 from typing import List, Optional, Union
 
-from filelock import FileLock
-
 from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
 
 
@@ -70,6 +68,7 @@ if is_torch_available():
     import torch
     from torch import nn
     from torch.utils.data.dataset import Dataset
+    from transformers import torch_distributed_zero_first
 
     class NerDataset(Dataset):
         """
@@ -91,16 +90,16 @@ if is_torch_available():
             max_seq_length: Optional[int] = None,
             overwrite_cache=False,
             mode: Split = Split.train,
+            local_rank=-1,
         ):
             # Load data features from cache or dataset file
             cached_features_file = os.path.join(
                 data_dir, "cached_{}_{}_{}".format(mode.value, tokenizer.__class__.__name__, str(max_seq_length)),
             )
 
-            # Make sure only the first process in distributed training processes the dataset,
-            # and the others will use the cache.
-            lock_path = cached_features_file + ".lock"
-            with FileLock(lock_path):
+            with torch_distributed_zero_first(local_rank):
+                # Make sure only the first process in distributed training processes the dataset,
+                # and the others will use the cache.
 
                 if os.path.exists(cached_features_file) and not overwrite_cache:
                     logger.info(f"Loading features from cached file {cached_features_file}")
@@ -126,8 +125,9 @@ if is_torch_available():
                         pad_token_segment_id=tokenizer.pad_token_type_id,
                         pad_token_label_id=self.pad_token_label_id,
                     )
-                    logger.info(f"Saving features into cached file {cached_features_file}")
-                    torch.save(self.features, cached_features_file)
+                    if local_rank in [-1, 0]:
+                        logger.info(f"Saving features into cached file {cached_features_file}")
+                        torch.save(self.features, cached_features_file)
 
         def __len__(self):
             return len(self.features)

model_cards/Tereveni-AI/gpt2-124M-uk-fiction/README.md

@@ -1,23 +0,0 @@
-Note: **default code snippet above won't work** because we are using `AlbertTokenizer` with `GPT2LMHeadModel`, see [issue](https://github.com/huggingface/transformers/issues/4285).
-
-## GPT2 124M Trained on Ukranian Fiction
-
-Example usage:
-```python
-from transformers import AlbertTokenizer, GPT2LMHeadModel
-
-tokenizer = AlbertTokenizer.from_pretrained("Tereveni-AI/gpt2-124M-uk-fiction")
-model = GPT2LMHeadModel.from_pretrained("Tereveni-AI/gpt2-124M-uk-fiction")
-
-input_ids = tokenizer.encode('Но зла Юнона, суча дочка,', add_special_tokens=False, return_tensors='pt')
-
-outputs = model.generate(
-    input_ids,
-    do_sample=True,
-    num_return_sequences=3,
-    max_length=50
-)
-
-for i, out in enumerate(outputs):
-    print('{}: {}'.format(i, tokenizer.decode(out)))
-```

model_cards/ViktorAlm/electra-base-norwegian-uncased-discriminator/README.md

@@ -1,25 +0,0 @@
----
-language: norwegian
-thumbnail: https://i.imgur.com/QqSEC5I.png
----
-
-# Norwegian Electra
-![Image of norwegian electra](https://i.imgur.com/QqSEC5I.png)
-
-Trained on Oscar + wikipedia + opensubtitles + some other data I had with the awesome power of TPUs(V3-8)
-
-Use with caution. I have no downstream tasks in Norwegian to test on so I have no idea of its performance yet.
-# Model
-## Electra: Pre-training Text Encoders as Discriminators Rather Than Generators
-Kevin Clark and Minh-Thang Luong and Quoc V. Le and Christopher D. Manning
-- https://openreview.net/pdf?id=r1xMH1BtvB
-- https://github.com/google-research/electra
-# Acknowledgments
-### TensorFlow Research Cloud
-Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ❤️
-- https://www.tensorflow.org/tfrc
-#### OSCAR corpus
-- https://oscar-corpus.com/
-#### OPUS
-- http://opus.nlpl.eu/
-- http://www.opensubtitles.org/

model_cards/activebus/BERT-DK_laptop/README.md

@@ -1,43 +0,0 @@
-# ReviewBERT
-
-BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.  
-
-`BERT-DK_laptop` is trained from 100MB laptop corpus under `Electronics/Computers & Accessories/Laptops`. 
-
-
-## Model Description
-
-The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus.  
-Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/).  
-
-`BERT-DK_laptop` is trained from 100MB laptop corpus under `Electronics/Computers & Accessories/Laptops`. 
-
-## Instructions
-Loading the post-trained weights are as simple as, e.g., 
-
-```python
-import torch
-from transformers import AutoModel, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-DK_laptop")
-model = AutoModel.from_pretrained("activebus/BERT-DK_laptop")
-
-```
-
-
-## Evaluation Results
-
-Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf) 
-
-
-## Citation
-If you find this work useful, please cite as following.
-```
-@inproceedings{xu_bert2019,
-    title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis",
-    author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.",
-    booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics",
-    month = "jun",
-    year = "2019",
-}
-```

model_cards/activebus/BERT-DK_rest/README.md

@@ -1,41 +0,0 @@
-# ReviewBERT
-
-BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
-
-`BERT-DK_rest` is trained from 1G (19 types) restaurants from Yelp.  
-
-## Model Description
-
-The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus.  
-Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/).  
-
-
-## Instructions
-Loading the post-trained weights are as simple as, e.g., 
-
-```python
-import torch
-from transformers import AutoModel, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-DK_rest")
-model = AutoModel.from_pretrained("activebus/BERT-DK_rest")
-
-```
-
-
-## Evaluation Results
-
-Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf) 
-
-
-## Citation
-If you find this work useful, please cite as following.
-```
-@inproceedings{xu_bert2019,
-    title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis",
-    author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.",
-    booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics",
-    month = "jun",
-    year = "2019",
-}
-```

model_cards/activebus/BERT-PT_laptop/README.md

@@ -1,41 +0,0 @@
-# ReviewBERT
-
-BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.  
-
-`BERT-DK_laptop` is trained from 100MB laptop corpus under `Electronics/Computers & Accessories/Laptops`. 
-`BERT-PT_*` addtionally uses SQuAD 1.1.  
-
-## Model Description
-
-The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus.  
-Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/).  
-
-
-## Instructions
-Loading the post-trained weights are as simple as, e.g., 
-
-```python
-import torch
-from transformers import AutoModel, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-PT_laptop")
-model = AutoModel.from_pretrained("activebus/BERT-PT_laptop")
-
-```
-
-## Evaluation Results
-
-Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf) 
-
-
-## Citation
-If you find this work useful, please cite as following.
-```
-@inproceedings{xu_bert2019,
-    title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis",
-    author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.",
-    booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics",
-    month = "jun",
-    year = "2019",
-}
-```

model_cards/activebus/BERT-PT_rest/README.md

@@ -1,42 +0,0 @@
-# ReviewBERT
-
-BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.  
-
-`BERT-DK_rest` is trained from 1G (19 types) restaurants from Yelp.
-`BERT-PT_*` addtionally uses SQuAD 1.1.  
-
-## Model Description
-
-The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus.  
-Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/).  
-
-
-## Instructions
-Loading the post-trained weights are as simple as, e.g., 
-
-```python
-import torch
-from transformers import AutoModel, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-PT_rest")
-model = AutoModel.from_pretrained("activebus/BERT-PT_rest")
-
-```
-
-
-## Evaluation Results
-
-Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf) 
-
-
-## Citation
-If you find this work useful, please cite as following.
-```
-@inproceedings{xu_bert2019,
-    title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis",
-    author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.",
-    booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics",
-    month = "jun",
-    year = "2019",
-}
-```

model_cards/activebus/BERT-XD_Review/README.md

@@ -1,44 +0,0 @@
-# ReviewBERT
-
-BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.  
-Please visit https://github.com/howardhsu/BERT-for-RRC-ABSA for details.  
-
-`BERT-XD_Review` is a cross-domain (beyond just `laptop` and `restaurant`) language model, where each example is from a single product / restaurant with the same rating, post-trained (fine-tuned) on a combination of 5-core Amazon reviews and all Yelp data, expected to be 22 G in total. It is trained for 4 epochs on `bert-base-uncased`.
-The preprocessing code [here](https://github.com/howardhsu/BERT-for-RRC-ABSA/transformers).
-
-## Model Description
-
-The original model is from `BERT-base-uncased`.  
-Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/).  
-
-
-## Instructions
-Loading the post-trained weights are as simple as, e.g., 
-
-```python
-import torch
-from transformers import AutoModel, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-XD_Review")
-model = AutoModel.from_pretrained("activebus/BERT-XD_Review")
-
-```
-
-
-## Evaluation Results
-
-Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf) 
-`BERT_Review` is expected to have similar performance on domain-specific tasks (such as aspect extraction) as `BERT-DK`, but much better on general tasks such as aspect sentiment classification (different domains mostly share similar sentiment words).
-
-
-## Citation
-If you find this work useful, please cite as following.
-```
-@inproceedings{xu_bert2019,
-    title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis",
-    author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.",
-    booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics",
-    month = "jun",
-    year = "2019",
-}
-```

model_cards/activebus/BERT_Review/README.md

@@ -1,44 +0,0 @@
-# ReviewBERT
-
-BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.  
-
-`BERT_Review` is cross-domain (beyond just `laptop` and `restaurant`) language model with one example from randomly mixed domains, post-trained (fine-tuned) on a combination of 5-core Amazon reviews and all Yelp data, expected to be 22 G in total. It is trained for 4 epochs on `bert-base-uncased`.
-The preprocessing code [here](https://github.com/howardhsu/BERT-for-RRC-ABSA/transformers).
-
-
-## Model Description
-
-The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus.  
-Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/).  
-
-
-## Instructions
-Loading the post-trained weights are as simple as, e.g., 
-
-```python
-import torch
-from transformers import AutoModel, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("activebus/BERT_Review")
-model = AutoModel.from_pretrained("activebus/BERT_Review")
-
-```
-
-
-## Evaluation Results
-
-Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf) 
-`BERT_Review` is expected to have similar performance on domain-specific tasks (such as aspect extraction) as `BERT-DK`, but much better on general tasks such as aspect sentiment classification (different domains mostly share similar sentiment words).
-
-
-## Citation
-If you find this work useful, please cite as following.
-```
-@inproceedings{xu_bert2019,
-    title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis",
-    author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.",
-    booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics",
-    month = "jun",
-    year = "2019",
-}
-```

model_cards/bert-base-german-cased-README.md

@@ -18,16 +18,13 @@ tags:
 **Eval data:** Conll03 (NER), GermEval14 (NER), GermEval18 (Classification), GNAD (Classification)  
 **Infrastructure**: 1x TPU v2  
 **Published**: Jun 14th, 2019
-
-**Update April 3rd, 2020**: we updated the vocabulary file on deepset's s3 to conform with the default tokenization of punctuation tokens. 
-For details see the related [FARM issue](https://github.com/deepset-ai/FARM/issues/60). If you want to use the old vocab we have also uploaded a ["deepset/bert-base-german-cased-oldvocab"](https://huggingface.co/deepset/bert-base-german-cased-oldvocab) model.
  
 ## Details
 - We trained using Google's Tensorflow code on a single cloud TPU v2 with standard settings.
 - We trained 810k steps with a batch size of 1024 for sequence length 128 and 30k steps with sequence length 512. Training took about 9 days.
 - As training data we used the latest German Wikipedia dump (6GB of raw txt files), the OpenLegalData dump (2.4 GB) and news articles (3.6 GB).
 - We cleaned the data dumps with tailored scripts and segmented sentences with spacy v2.1. To create tensorflow records we used the recommended sentencepiece library for creating the word piece vocabulary and tensorflow scripts to convert the text to data usable by BERT.
-
+- Update April 3rd, 2020: updated the vocab file on deepset s3 to adjust tokenization of punctuation.
 
 See https://deepset.ai/german-bert for more details
 

model_cards/dbmdz/electra-base-turkish-cased-discriminator/README.md

@@ -1,79 +0,0 @@
----
-language: turkish
-license: mit
----
-
-# 🤗 + 📚 dbmdz Turkish ELECTRA model
-
-In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
-Library open sources a cased ELECTRA base model for Turkish 🎉
-
-# Turkish ELECTRA model
-
-We release a base ELEC**TR**A model for Turkish, that was trained on the same data as *BERTurk*.
-
-> ELECTRA is a new method for self-supervised language representation learning. It can be used to
-> pre-train transformer networks using relatively little compute. ELECTRA models are trained to
-> distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to
-> the discriminator of a GAN.
-
-More details about ELECTRA can be found in the [ICLR paper](https://openreview.net/forum?id=r1xMH1BtvB)
-or in the [official ELECTRA repository](https://github.com/google-research/electra) on GitHub.
-
-## Stats
-
-The current version of the model is trained on a filtered and sentence
-segmented version of the Turkish [OSCAR corpus](https://traces1.inria.fr/oscar/),
-a recent Wikipedia dump, various [OPUS corpora](http://opus.nlpl.eu/) and a
-special corpus provided by [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/).
-
-The final training corpus has a size of 35GB and 44,04,976,662 tokens.
-
-Thanks to Google's TensorFlow Research Cloud (TFRC) we could train a cased model
-on a TPU v3-8 for 1M steps.
-
-## Model weights
-
-[Transformers](https://github.com/huggingface/transformers)
-compatible weights for both PyTorch and TensorFlow are available.
-
-| Model                                            | Downloads
-| ------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------
-| `dbmdz/electra-base-turkish-cased-discriminator` | [`config.json`](https://cdn.huggingface.co/dbmdz/electra-base-turkish-cased-discriminator/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/electra-base-turkish-cased-discriminator/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/electra-base-turkish-cased-discriminator/vocab.txt)
-
-## Usage
-
-With Transformers >= 2.8 our ELECTRA base cased model can be loaded like:
-
-```python
-from transformers import AutoModelWithLMHead, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("dbmdz/electra-base-turkish-cased-discriminator")
-model = AutoModelWithLMHead.from_pretrained("dbmdz/electra-base-turkish-cased-discriminator")
-```
-
-## Results
-
-For results on PoS tagging or NER tasks, please refer to
-[this repository](https://github.com/stefan-it/turkish-bert/electra).
-
-# Huggingface model hub
-
-All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz).
-
-# Contact (Bugs, Feedback, Contribution and more)
-
-For questions about our ELECTRA models just open an issue
-[here](https://github.com/dbmdz/berts/issues/new) 🤗
-
-# Acknowledgments
-
-Thanks to [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/) for providing us
-additional large corpora for Turkish. Many thanks to Reyyan Yeniterzi for providing
-us the Turkish NER dataset for evaluation.
-
-Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).
-Thanks for providing access to the TFRC ❤️
-
-Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team,
-it is possible to download both cased and uncased models from their S3 storage 🤗

model_cards/dbmdz/electra-small-turkish-cased-discriminator/README.md

@@ -1,79 +0,0 @@
----
-language: turkish
-license: mit
----
-
-# 🤗 + 📚 dbmdz Turkish ELECTRA model
-
-In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
-Library open sources a cased ELECTRA small model for Turkish 🎉
-
-# Turkish ELECTRA model
-
-We release a small ELEC**TR**A model for Turkish, that was trained on the same data as *BERTurk*.
-
-> ELECTRA is a new method for self-supervised language representation learning. It can be used to
-> pre-train transformer networks using relatively little compute. ELECTRA models are trained to
-> distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to
-> the discriminator of a GAN.
-
-More details about ELECTRA can be found in the [ICLR paper](https://openreview.net/forum?id=r1xMH1BtvB)
-or in the [official ELECTRA repository](https://github.com/google-research/electra) on GitHub.
-
-## Stats
-
-The current version of the model is trained on a filtered and sentence
-segmented version of the Turkish [OSCAR corpus](https://traces1.inria.fr/oscar/),
-a recent Wikipedia dump, various [OPUS corpora](http://opus.nlpl.eu/) and a
-special corpus provided by [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/).
-
-The final training corpus has a size of 35GB and 44,04,976,662 tokens.
-
-Thanks to Google's TensorFlow Research Cloud (TFRC) we could train a cased model
-on a TPU v3-8 for 1M steps.
-
-## Model weights
-
-[Transformers](https://github.com/huggingface/transformers)
-compatible weights for both PyTorch and TensorFlow are available.
-
-| Model                                             | Downloads
-| ------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------
-| `dbmdz/electra-small-turkish-cased-discriminator` | [`config.json`](https://cdn.huggingface.co/dbmdz/electra-small-turkish-cased-discriminator/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/electra-small-turkish-cased-discriminator/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/electra-small-turkish-cased-discriminator/vocab.txt)
-
-## Usage
-
-With Transformers >= 2.8 our ELECTRA small cased model can be loaded like:
-
-```python
-from transformers import AutoModelWithLMHead, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("dbmdz/electra-small-turkish-cased-discriminator")
-model = AutoModelWithLMHead.from_pretrained("dbmdz/electra-small-turkish-cased-discriminator")
-```
-
-## Results
-
-For results on PoS tagging or NER tasks, please refer to
-[this repository](https://github.com/stefan-it/turkish-bert/electra).
-
-# Huggingface model hub
-
-All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz).
-
-# Contact (Bugs, Feedback, Contribution and more)
-
-For questions about our ELECTRA models just open an issue
-[here](https://github.com/dbmdz/berts/issues/new) 🤗
-
-# Acknowledgments
-
-Thanks to [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/) for providing us
-additional large corpora for Turkish. Many thanks to Reyyan Yeniterzi for providing
-us the Turkish NER dataset for evaluation.
-
-Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).
-Thanks for providing access to the TFRC ❤️
-
-Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team,
-it is possible to download both cased and uncased models from their S3 storage 🤗

model_cards/deepset/bert-base-german-cased-oldvocab/README.md

@@ -1,28 +0,0 @@
----
-language: german
-thumbnail: https://static.tildacdn.com/tild6438-3730-4164-b266-613634323466/german_bert.png
-tags:
-- exbert
----
-
-<a href="https://huggingface.co/exbert/?model=bert-base-german-cased">
-	<img width="300px" src="https://hf-dinosaur.huggingface.co/exbert/button.png">
-</a>
-
-# German BERT with old vocabulary
-For details see the related [FARM issue](https://github.com/deepset-ai/FARM/issues/60).
-
-
-## About us
-![deepset logo](https://raw.githubusercontent.com/deepset-ai/FARM/master/docs/img/deepset_logo.png)
-
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our work: 
-- [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert)
-- [FARM](https://github.com/deepset-ai/FARM)
-- [Haystack](https://github.com/deepset-ai/haystack/)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Website](https://deepset.ai)  

model_cards/digitalepidemiologylab/covid-twitter-bert/README.md

@@ -1,18 +0,0 @@
-# COVID-Twitter-BERT (CT-BERT)
-BERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19
-
-## Overview
-This model was trained on 160M tweets collected between January 12 and April 16, 2020 containing at least one of the keywords "wuhan", "ncov", "coronavirus", "covid", or "sars-cov-2". These tweets were filtered and preprocessed to reach a final sample of 22.5M tweets (containing 40.7M sentences and 633M tokens) which were used for training.
-
-This model was evaluated based on downstream classification tasks, but it could be used for any other NLP task which can leverage contextual embeddings. 
-
-In order to achieve best results, make sure to use the same text preprocessing as we did for pretraining. This involves replacing user mentions, urls and emojis. You can find a script on our projects [GitHub repo](https://github.com/digitalepidemiologylab/covid-twitter-bert).
-
-## Example usage
-```python
-tokenizer = AutoTokenizer.from_pretrained("digitalepidemiologylab/covid-twitter-bert")
-model = TFAutoModel.from_pretrained("digitalepidemiologylab/covid-twitter-bert")
-```
-
-## References
-[1] Martin Müller, Marcel Salaté, Per E Kummervold. "COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter" arXiv preprint arXiv:2005.07503 (2020).

model_cards/dumitrescustefan/bert-base-romanian-cased-v1/README.md

@@ -1,48 +0,0 @@
----
-language: romanian
----
-
-# bert-base-romanian-cased-v1
-
-The BERT **base**, **cased** model for Romanian, trained on a 15GB corpus, version ![v1.0](https://img.shields.io/badge/v1.0-21%20Apr%202020-ff6666)
-
-### How to use
-
-```python
-from transformers import AutoTokenizer, AutoModel
-import torch
-# load tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("dumitrescustefan/bert-base-romanian-cased-v1")
-model = AutoModel.from_pretrained("dumitrescustefan/bert-base-romanian-cased-v1")
-# tokenize a sentence and run through the model
-input_ids = torch.tensor(tokenizer.encode("Acesta este un test.", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
-outputs = model(input_ids)
-# get encoding
-last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
-```
-
-### Evaluation
-
-Evaluation is performed on Universal Dependencies [Romanian RRT](https://universaldependencies.org/treebanks/ro_rrt/index.html) UPOS, XPOS and LAS, and on a NER task based on [RONEC](https://github.com/dumitrescustefan/ronec). Details, as well as more in-depth tests not shown here, are given in the dedicated [evaluation page](https://github.com/dumitrescustefan/Romanian-Transformers/tree/master/evaluation/README.md). 
-
-The baseline is the [Multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) model ``bert-base-multilingual-(un)cased``, as at the time of writing it was the only available BERT model that works on Romanian.
-
-| Model                          |  UPOS |  XPOS  |  NER  |  LAS  |
-|--------------------------------|:-----:|:------:|:-----:|:-----:|
-| bert-base-multilingual-cased   | 97.87 |  96.16 | 84.13 | 88.04 |
-| bert-base-romanian-cased-v1    | **98.00** |  **96.46** | **85.88** | **89.69** |
-
-### Corpus 
-
-The model is trained on the following corpora (stats in the table below are after cleaning):
-
-| Corpus    	| Lines(M) 	| Words(M) 	| Chars(B) 	| Size(GB) 	|
-|-----------	|:--------:	|:--------:	|:--------:	|:--------:	|
-| OPUS      	|   55.05  	|  635.04  	|   4.045  	|    3.8   	|
-| OSCAR     	|   33.56  	|  1725.82 	|  11.411  	|    11    	|
-| Wikipedia 	|   1.54   	|   60.47  	|   0.411  	|    0.4   	|
-| **Total**     	|   **90.15**  	|  **2421.33** 	|  **15.867**  	|   **15.2**   	|
-
-#### Acknowledgements
-
-- We'd like to thank [Sampo Pyysalo](https://github.com/spyysalo) from TurkuNLP for helping us out with the compute needed to pretrain the v1.0 BERT models. He's awesome!

model_cards/dumitrescustefan/bert-base-romanian-uncased-v1/README.md

@@ -1,51 +0,0 @@
----
-language: romanian
----
-
-# bert-base-romanian-uncased-v1
-
-The BERT **base**, **uncased** model for Romanian, trained on a 15GB corpus, version ![v1.0](https://img.shields.io/badge/v1.0-21%20Apr%202020-ff6666)
-
-### How to use
-
-```python
-from transformers import AutoTokenizer, AutoModel
-import torch
-
-# load tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("dumitrescustefan/bert-base-romanian-uncased-v1", do_lower_case=True)
-model = AutoModel.from_pretrained("dumitrescustefan/bert-base-romanian-uncased-v1")
-
-# tokenize a sentence and run through the model
-input_ids = torch.tensor(tokenizer.encode("Acesta este un test.", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
-outputs = model(input_ids)
-
-# get encoding
-last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
-```
-
-### Evaluation
-
-Evaluation is performed on Universal Dependencies [Romanian RRT](https://universaldependencies.org/treebanks/ro_rrt/index.html) UPOS, XPOS and LAS, and on a NER task based on [RONEC](https://github.com/dumitrescustefan/ronec). Details, as well as more in-depth tests not shown here, are given in the dedicated [evaluation page](https://github.com/dumitrescustefan/Romanian-Transformers/tree/master/evaluation/README.md). 
-
-The baseline is the [Multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) model ``bert-base-multilingual-(un)cased``, as at the time of writing it was the only available BERT model that works on Romanian.
-
-| Model                          |  UPOS |  XPOS  |  NER  |  LAS  |
-|--------------------------------|:-----:|:------:|:-----:|:-----:|
-| bert-base-multilingual-uncased | 97.65 |  95.72 | 83.91 | 87.65 |
-| bert-base-romanian-uncased-v1  | **98.18** |  **96.84** | **85.26** | **89.61** |
-
-### Corpus 
-
-The model is trained on the following corpora (stats in the table below are after cleaning):
-
-| Corpus    	| Lines(M) 	| Words(M) 	| Chars(B) 	| Size(GB) 	|
-|-----------	|:--------:	|:--------:	|:--------:	|:--------:	|
-| OPUS      	|   55.05  	|  635.04  	|   4.045  	|    3.8   	|
-| OSCAR     	|   33.56  	|  1725.82 	|  11.411  	|    11    	|
-| Wikipedia 	|   1.54   	|   60.47  	|   0.411  	|    0.4   	|
-| **Total**     	|   **90.15**  	|  **2421.33** 	|  **15.867**  	|   **15.2**   	|
-
-#### Acknowledgements
-
-- We'd like to thank [Sampo Pyysalo](https://github.com/spyysalo) from TurkuNLP for helping us out with the compute needed to pretrain the v1.0 BERT models. He's awesome!

model_cards/google/electra-base-generator/README.md

@@ -25,7 +25,7 @@ fill_mask = pipeline(
 )
 
 print(
-	fill_mask(f"HuggingFace is creating a {fill_mask.tokenizer.mask_token} that the community uses to solve NLP tasks.")
+	fill_mask(f"HuggingFace is creating a {nlp.tokenizer.mask_token} that the community uses to solve NLP tasks.")
 )
 
 ```

model_cards/google/reformer-enwik8/README.md

@@ -1,57 +0,0 @@
-## Reformer Language model on character level and trained on enwik8. 
-
-*enwik8* is a dataset based on Wikipedia and is often used to measure the model's ability to *compress* data, *e.g.* in 
-the scope of the *Hutter prize*: https://en.wikipedia.org/wiki/Hutter_Prize.
-
-`reformer-enwik8` was pretrained on the first 90M chars of *enwik8* whereas the text was chunked into batches of size 65536 chars (=2^16).
-The model's weights were taken from https://console.cloud.google.com/storage/browser/trax-ml/reformer/enwik8 and converted 
-to Hugging Face's PyTorch ReformerLM model `ReformerModelWithLMHead`.
-
-The model is a language model that operates on characters. 
-Therefore, this model does not need a tokenizer. The following function can instead be used for **encoding** and **decoding**:
-
-```python
-import torch
-
-# Encoding
-def encode(list_of_strings, pad_to_max_length=True, pad_token_id=0):
-    max_length = max([len(string) for string in list_of_strings])
-
-    # create emtpy tensors
-    attention_masks = torch.zeros((len(list_of_strings), max_length), dtype=torch.long)
-    input_ids = torch.full((len(list_of_strings), max_length), pad_token_id, dtype=torch.long)
-
-    for idx, string in enumerate(list_of_strings):
-        # make sure string is in byte format
-        if not isinstance(string, bytes):
-            string = str.encode(string)
-
-        input_ids[idx, :len(string)] = torch.tensor([x + 2 for x in string])
-        attention_masks[idx, :len(string)] = 1
-
-    return input_ids, attention_masks
-    
-# Decoding
-def decode(outputs_ids):
-    decoded_outputs = []
-    for output_ids in outputs_ids.tolist():
-        # transform id back to char IDs < 2 are simply transformed to ""
-        decoded_outputs.append("".join([chr(x - 2) if x > 1 else "" for x in output_ids]))
-    return decoded_outputs
-```
-
-Text can be generated as follows:
-
-```python
-from transformers import ReformerModelWithLMHead
-
-model = ReformerModelWithLMHead.from_pretrained("google/reformer-enwik8")
-encoded, attention_masks = encode(["In 1965, Brooks left IBM to found the Department of"])
-decode(model.generate(encoded, do_sample=True, max_length=150))
-
-# gives:
-# In 1965, Brooks left IBM to found the Department of Journalism in 1968. IBM had jurisdiction himself in 1980, while Brooks resolved, nevertheless thro
-
-```
-
-***Note***: Language generation using `ReformerModelWithLMHead` is not optimized yet and is rather slow.

model_cards/huseinzol05/t5-base-bahasa-cased/README.md

@@ -1,74 +0,0 @@
----
-language: malay
----
-
-# Bahasa T5 Model
-
-Pretrained T5 base language model for Malay and Indonesian. 
-
-## Pretraining Corpus
-
-`t5-base-bahasa-cased` model was pretrained on multiple tasks. Below is list of tasks we trained on,
-
-1. [Unsupervised](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/data/preprocessors.py#L1875) on [local Wikipedia](https://github.com/huseinzol05/Malaya-Dataset#wikipedia-1).
-2. [Unsupervised](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/data/preprocessors.py#L1875) on [local news](https://github.com/huseinzol05/Malaya-Dataset#public-news).
-3. [Unsupervised](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/data/preprocessors.py#L1875) on [local parliament text](https://github.com/huseinzol05/Malaya-Dataset#parliament).
-4. [Unsupervised](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/data/preprocessors.py#L1875) on [IIUM Confession](https://github.com/huseinzol05/Malaya-Dataset#iium-confession).
-5. [Unsupervised](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/data/preprocessors.py#L1875) on [Wattpad](https://github.com/huseinzol05/Malaya-Dataset#wattpad).
-6. [Unsupervised](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/data/preprocessors.py#L1875) on [Academia PDF](https://github.com/huseinzol05/Malaya-Dataset#academia-pdf).
-7. [Next sentence prediction](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/data/preprocessors.py#L1129) on [local Wikipedia](https://github.com/huseinzol05/Malaya-Dataset#wikipedia-1).
-8. [Next sentence prediction](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/data/preprocessors.py#L1129) on [local news](https://github.com/huseinzol05/Malaya-Dataset#public-news).
-9. [Next sentence prediction](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/data/preprocessors.py#L1129) on [local parliament text](https://github.com/huseinzol05/Malaya-Dataset#parliament).
-10. [Next sentence prediction](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/data/preprocessors.py#L1129) on [IIUM Confession](https://github.com/huseinzol05/Malaya-Dataset#iium-confession).
-11. [Next sentence prediction](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/data/preprocessors.py#L1129) on [Wattpad](https://github.com/huseinzol05/Malaya-Dataset#wattpad).
-12. [Next sentence prediction](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/data/preprocessors.py#L1129) on [Academia PDF](https://github.com/huseinzol05/Malaya-Dataset#academia-pdf).
-13. [Bahasa SNLI](https://github.com/huseinzol05/Malaya-Dataset#snli).
-14. [Bahasa Question Quora](https://github.com/huseinzol05/Malaya-Dataset#quora).
-15. [Bahasa Natural Questions](https://github.com/huseinzol05/Malaya-Dataset#natural-questions).
-16. [News title summarization](https://github.com/huseinzol05/Malaya-Dataset#crawled-news).
-17. [Stemming to original wikipedia](https://github.com/huseinzol05/Malaya/blob/master/pretrained-model/t5/generate-stemming.ipynb).
-18. [Synonym to original wikipedia](https://github.com/huseinzol05/Malaya/blob/master/pretrained-model/t5/generate-synonym.ipynb).
-
-Preprocessing steps can reproduce from here, [Malaya/pretrained-model/preprocess](https://github.com/huseinzol05/Malaya/tree/master/pretrained-model/preprocess).
-
-## Pretraining details
-
-- This model was trained using Google T5's github [repository](https://github.com/google-research/text-to-text-transfer-transformer) on v3-8 TPU.
-- All steps can reproduce from here, [Malaya/pretrained-model/t5](https://github.com/huseinzol05/Malaya/tree/master/pretrained-model/t5).
-
-## Load Pretrained Model
-
-You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:  
-
-```python
-from transformers import T5Tokenizer, T5Model
-
-model = T5Model.from_pretrained('huseinzol05/t5-base-bahasa-cased')
-tokenizer = T5Tokenizer.from_pretrained('huseinzol05/t5-base-bahasa-cased')
-```
-
-## Example using T5ForConditionalGeneration
-
-```python
-from transformers import T5Tokenizer, T5ForConditionalGeneration
-
-tokenizer = T5Tokenizer.from_pretrained('huseinzol05/t5-base-bahasa-cased')
-model = T5ForConditionalGeneration.from_pretrained('huseinzol05/t5-base-bahasa-cased')
-input_ids = tokenizer.encode('soalan: siapakah perdana menteri malaysia?', return_tensors = 'pt')
-outputs = model.generate(input_ids)
-print(tokenizer.decode(outputs[0]))
-```
-
-Output is,
-
-```
-'Mahathir Mohamad'
-```
-
-## Results
-
-For further details on the model performance, simply checkout accuracy page from Malaya, https://malaya.readthedocs.io/en/latest/Accuracy.html, we compared with traditional models.
-
-## Acknowledgement
-
-Thanks to [Im Big](https://www.facebook.com/imbigofficial/), [LigBlou](https://www.facebook.com/ligblou), [Mesolitica](https://mesolitica.com/) and [KeyReply](https://www.keyreply.com/) for sponsoring AWS, Google and GPU clouds to train T5 for Bahasa. 

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

@@ -1,4 +1,3 @@
-
 # 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. 
@@ -13,7 +12,6 @@ O
 
 ## Metrics on evaluation set:
 ### Average over the 40 languages
-Number of documents: 262300
 ```
            precision    recall  f1-score   support
 
@@ -26,7 +24,6 @@ macro avg       0.86      0.87      0.87    333298
 ```
 
 ### Afrikaans
-Number of documents: 1000
 ```
            precision    recall  f1-score   support
 
@@ -39,7 +36,6 @@ macro avg       0.87      0.91      0.89      1469
 ``` 
 
 ### Arabic
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -52,7 +48,6 @@ macro avg       0.87      0.88      0.88     10754
 ```
 
 ### Basque
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -65,7 +60,6 @@ macro avg       0.89      0.89      0.89     12954
 ```
 
 ### Bengali
-Number of documents: 1000
 ```
            precision    recall  f1-score   support
 
@@ -78,7 +72,6 @@ macro avg       0.91      0.92      0.91      1095
 ```
 
 ### Bulgarian
-Number of documents: 1000
 ```
            precision    recall  f1-score   support
 
@@ -91,7 +84,6 @@ macro avg       0.91      0.92      0.91     14116
 ```
 
 ### Burmese
-Number of documents: 100
 ```
            precision    recall  f1-score   support
 
@@ -104,7 +96,6 @@ macro avg       0.57      0.65      0.60       103
 ```
 
 ### Chinese
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -117,7 +108,6 @@ macro avg       0.76      0.78      0.77     11558
 ```
 
 ### Dutch
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -130,7 +120,6 @@ macro avg       0.91      0.92      0.91     13120
 ```
 
 ### English
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -143,7 +132,6 @@ macro avg       0.82      0.83      0.83     13973
 ```
 
 ### Estonian
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -156,7 +144,6 @@ macro avg       0.90      0.91      0.90     13558
 ```
 
 ### Finnish
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -169,7 +156,6 @@ macro avg       0.89      0.89      0.89     13930
 ```
 
 ### French
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -182,7 +168,6 @@ macro avg       0.89      0.90      0.90     12933
 ```
 
 ### Georgian
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -195,7 +180,6 @@ macro avg       0.84      0.86      0.85     12615
 ```
 
 ### German
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -208,7 +192,6 @@ macro avg       0.86      0.86      0.86     13638
 ```
 
 ### Greek
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -221,7 +204,6 @@ macro avg       0.88      0.90      0.89     12101
 ```
 
 ### Hebrew
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -234,7 +216,6 @@ macro avg       0.82      0.83      0.83     12934
 ```
 
 ### Hindi
-Number of documents: 1000
 ```
            precision    recall  f1-score   support
 
@@ -247,7 +228,6 @@ macro avg       0.84      0.87      0.85      1211
 ```
 
 ### Hungarian
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -260,7 +240,6 @@ macro avg       0.91      0.92      0.91     13879
 ```
 
 ### Indonesian
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -273,7 +252,6 @@ macro avg       0.91      0.92      0.92     11376
 ```
 
 ### Italian
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -286,7 +264,6 @@ macro avg       0.90      0.90      0.90     13412
 ```
 
 ### Japanese
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -299,7 +276,6 @@ macro avg       0.69      0.72      0.70     12277
 ```
 
 ### Javanese
-Number of documents: 100
 ```
            precision    recall  f1-score   support
 
@@ -312,7 +288,6 @@ macro avg       0.78      0.82      0.80       112
 ```
 
 ### Kazakh
-Number of documents: 1000
 ```
            precision    recall  f1-score   support
 
@@ -325,7 +300,6 @@ macro avg       0.81      0.83      0.81      1135
 ```
 
 ### Korean
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -338,7 +312,6 @@ macro avg       0.83      0.83      0.83     13329
 ```
 
 ### Malay
-Number of documents: 1000
 ```
            precision    recall  f1-score   support
 
@@ -351,7 +324,6 @@ macro avg       0.91      0.92      0.91      1088
 ```
 
 ### Malayalam
-Number of documents: 1000
 ```
            precision    recall  f1-score   support
 
@@ -364,7 +336,6 @@ macro avg       0.78      0.80      0.79      1155
 ```
 
 ### Marathi
-Number of documents: 1000
 ```
            precision    recall  f1-score   support
 
@@ -377,7 +348,6 @@ macro avg       0.85      0.86      0.85      1190
 ```
 
 ### Persian
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -390,7 +360,6 @@ macro avg       0.92      0.92      0.92     10494
 ```
 
 ### Portuguese
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -403,7 +372,6 @@ macro avg       0.90      0.91      0.90     12673
 ```
 
 ### Russian
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -416,7 +384,6 @@ macro avg       0.87      0.88      0.88     12051
 ```
 
 ### Spanish
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -429,7 +396,6 @@ macro avg       0.90      0.91      0.90     12153
 ```
 
 ### Swahili
-Number of documents: 1000
 ```
            precision    recall  f1-score   support
 
@@ -442,7 +408,6 @@ macro avg       0.88      0.89      0.88      1202
 ```
 
 ### Tagalog
-Number of documents: 1000
 ```
            precision    recall  f1-score   support
 
@@ -455,7 +420,6 @@ macro avg       0.90      0.92      0.91      1027
 ```
 
 ### Tamil
-Number of documents: 1000
 ```
            precision    recall  f1-score   support
 
@@ -468,7 +432,6 @@ macro avg       0.82      0.83      0.82      1183
 ```
 
 ### Telugu
-Number of documents: 1000
 ```
            precision    recall  f1-score   support
 
@@ -481,7 +444,6 @@ macro avg       0.73      0.77      0.75      1193
 ```
 
 ### Thai
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -494,7 +456,6 @@ macro avg       0.68      0.74      0.71     14722
 ```
 
 ### Turkish
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -507,7 +468,6 @@ macro avg       0.91      0.92      0.91     13360
 ```
 
 ### Urdu
-Number of documents: 1000
 ```
            precision    recall  f1-score   support
 
@@ -520,7 +480,6 @@ macro avg       0.92      0.94      0.93      1011
 ```
 
 ### Vietnamese
-Number of documents: 10000
 ```
            precision    recall  f1-score   support
 
@@ -533,7 +492,6 @@ macro avg       0.89      0.90      0.90     11107
 ```
 
 ### Yoruba
-Number of documents: 100
 ```
            precision    recall  f1-score   support
 
@@ -546,7 +504,7 @@ macro avg       0.63      0.68      0.63       107
 ```
 
 ## Reproduce the results
-Download and prepare the dataset from the [XTREME repo](https://github.com/google-research/xtreme#download-the-data). Next, from the root of the transformers repo run:
+Download and prepare the dataset from the [[https://github.com/google-research/xtreme#download-the-data](https://github.com/google-research/xtreme#download-the-data)](XTREME repo). Next, from the root of the transformers repo run:
 ```
 cd examples/ner
 python run_tf_ner.py \
@@ -575,9 +533,8 @@ nlp_ner = pipeline(
     model="jplu/tf-xlm-r-ner-40-lang",
     tokenizer=(
         'jplu/tf-xlm-r-ner-40-lang',  
-        {"use_fast": True}),
-    framework="tf"
-)
+        {"use_fast": True}
+))
 
 text_fr = "Barack Obama est né à Hawaï."
 text_en = "Barack Obama was born in Hawaii."
@@ -596,4 +553,4 @@ 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/lserinol/bert-turkish-question-answering/README.md

@@ -1,61 +0,0 @@
----
-language: turkish
----
-
-# bert-turkish-question-answering
-
-## Usage
-
-```python
-from transformers import pipeline
-nlp = pipeline('question-answering', model='lserinol/bert-turkish-question-answering', tokenizer='lserinol/bert-turkish-question-answering')
-nlp({
-    'question': "Ankara'da kaç ilçe vardır?",
-    'context': r"""Türkiye'nin başkenti Ankara'dır. Ülkenin en büyük idari birimleri illerdir ve 81 il vardır. Bu iller ilçelere ayrılmıştır, toplamda 973 ilçe mevcuttur."""
-})
-```
-
-```python
-from transformers import AutoTokenizer, AutoModelForQuestionAnswering
-import torch
-
-tokenizer = AutoTokenizer.from_pretrained("lserinol/bert-turkish-question-answering")
-model = AutoModelForQuestionAnswering.from_pretrained("lserinol/bert-turkish-question-answering")
-text = r"""
-Ankara'nın başkent ilan edilmesinin ardından (13 Ekim 1923) şehir hızla gelişmiş ve Türkiye'nin ikinci en kalabalık ili olmuştur.
-Türkiye Cumhuriyeti'nin ilk yıllarında ekonomisi tarım ve hayvancılığa dayanan ilin topraklarının yarısı hâlâ tarım amaçlı 
-kullanılmaktadır. Ekonomik etkinlik büyük oranda ticaret ve sanayiye dayalıdır. Tarım ve hayvancılığın ağırlığı ise giderek 
-azalmaktadır. Ankara ve civarındaki gerek kamu sektörü gerek özel sektör yatırımları, başka illerden büyük bir nüfus göçünü 
-teşvik etmiştir. Cumhuriyetin kuruluşundan günümüze, nüfusu ülke nüfusunun iki katı hızda artmıştır. Nüfusun yaklaşık dörtte 
-üçü hizmet sektörü olarak tanımlanabilecek memuriyet, ulaşım, haberleşme ve ticaret benzeri işlerde, dörtte biri sanayide, 
-%2'si ise tarım alanında çalışır. Sanayi, özellikle tekstil, gıda ve inşaat sektörlerinde yoğunlaşmıştır. Günümüzde ise en çok 
-savunma, metal ve motor sektörlerinde yatırım yapılmaktadır. Türkiye'nin en çok sayıda üniversiteye sahip ili olan Ankara'da 
-ayrıca, üniversite diplomalı kişi oranı ülke ortalamasının iki katıdır. Bu eğitimli nüfus, teknoloji ağırlıklı yatırımların 
-gereksinim duyduğu iş gücünü oluşturur. Ankara'dan otoyollar, demir yolu ve hava yoluyla Türkiye'nin diğer şehirlerine ulaşılır.
-Ankara aynı zamanda başkent olarak Türkiye Büyük Millet Meclisi (TBMM)'ye de ev sahipliği yapmaktadır.
-"""
-
-questions = [
-    "Ankara kaç yılında başkent oldu?",
-    "Ankara ne zaman başkent oldu?",
-    "Ankara'dan başka şehirlere nasıl ulaşılır?",
-    "TBMM neyin kısaltmasıdır?"
-]
-
-for question in questions:
-    inputs = tokenizer.encode_plus(question, text, add_special_tokens=True, return_tensors="pt")
-    input_ids = inputs["input_ids"].tolist()[0]
-
-    text_tokens = tokenizer.convert_ids_to_tokens(input_ids)
-    answer_start_scores, answer_end_scores = model(**inputs)
-
-    answer_start = torch.argmax(
-        answer_start_scores
-    )  # Get the most likely beginning of answer with the argmax of the score
-    answer_end = torch.argmax(answer_end_scores) + 1  # Get the most likely end of answer with the argmax of the score
-
-    answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))
-
-    print(f"Question: {question}")
-    print(f"Answer: {answer}\n")
-  ```

model_cards/mrm8488/RuPERTa-base-finetuned-ner/README.md

@@ -1,92 +0,0 @@
----
-language: spanish
-thumbnail:
----
-
-# RuPERTa-base  (Spanish RoBERTa) + NER 🎃🏷
-
-This model is a fine-tuned on [NER-C](https://www.kaggle.com/nltkdata/conll-corpora) version of [RuPERTa-base](https://huggingface.co/mrm8488/RuPERTa-base) for **NER** downstream task.
-
-## Details of the downstream task (NER) - Dataset
-
-- [Dataset:  CONLL Corpora ES](https://www.kaggle.com/nltkdata/conll-corpora) 📚
-
-| Dataset                | # Examples |
-| ---------------------- | ----- |
-| Train                  |  329 K |
-| Dev                    | 40 K |
-
-
-- [Fine-tune on NER script provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner.py)
-
-- Labels covered:
-
-```
-B-LOC
-B-MISC
-B-ORG
-B-PER
-I-LOC
-I-MISC
-I-ORG
-I-PER
-O
-```
-
-## Metrics on evaluation set 🧾
-
-|                                                      Metric                                                       |  # score  |
-| :------------------------------------------------------------------------------------: | :-------: |
-| F1                                       | **77.55**  
-| Precision                                | **75.53** | 
-| Recall                                   | **79.68** |    
-
-## Model in action 🔨
-
-
-Example of usage:
-
-```python
-import torch
-from transformers import AutoModelForTokenClassification, AutoTokenizer
-
-id2label = {
-    "0": "B-LOC",
-    "1": "B-MISC",
-    "2": "B-ORG",
-    "3": "B-PER",
-    "4": "I-LOC",
-    "5": "I-MISC",
-    "6": "I-ORG",
-    "7": "I-PER",
-    "8": "O"
-}
-
-text ="Julien, CEO de HF, nació en Francia."
-input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0)
-
-outputs = model(input_ids)
-last_hidden_states = outputs[0]
-
-for m in last_hidden_states:
-  for index, n in enumerate(m):
-    if(index > 0 and index <= len(text.split(" "))):
-      print(text.split(" ")[index-1] + ": " + id2label[str(torch.argmax(n).item())])
-      
-'''
-Output:
---------
-Julien,: I-PER
-CEO: O
-de: O
-HF,: B-ORG
-nació: I-PER
-en: I-PER
-Francia.: I-LOC
-'''
-```
-Yeah! Not too bad 🎉
-
-> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
-
-> Made with <span style="color: #e25555;">&hearts;</span> in Spain

model_cards/mrm8488/RuPERTa-base-finetuned-pos/README.md

@@ -1,111 +0,0 @@
----
-language: spanish
-thumbnail:
----
-
-# RuPERTa-base  (Spanish RoBERTa) + POS 🎃🏷
-
-This model is a fine-tuned on [CONLL CORPORA](https://www.kaggle.com/nltkdata/conll-corpora) version of [RuPERTa-base](https://huggingface.co/mrm8488/RuPERTa-base) for **POS** downstream task.
-
-## Details of the downstream task (POS) - Dataset
-
-- [Dataset:  CONLL Corpora ES](https://www.kaggle.com/nltkdata/conll-corpora) 📚
-
-| Dataset                | # Examples |
-| ---------------------- | ----- |
-| Train                  | 445 K |
-| Dev                    | 55 K |
-
-- [Fine-tune on NER script provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner.py)
-
-- Labels covered:
-
-```
-ADJ
-ADP
-ADV
-AUX
-CCONJ
-DET
-INTJ
-NOUN
-NUM
-PART
-PRON
-PROPN
-PUNCT
-SCONJ
-SYM
-VERB
-```
-
-## Metrics on evaluation set 🧾
-
-|                                                      Metric                                                       |  # score  |
-| :------------------------------------------------------------------------------------: | :-------: |
-| F1                                       | **97.39**  
-| Precision                                | **97.47** | 
-| Recall                                   | **9732** |    
-
-## Model in action 🔨
-
-
-Example of usage
-
-```python
-import torch
-from transformers import AutoModelForTokenClassification, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained('mrm8488/RuPERTa-base-finetuned-pos')
-model = AutoModelForTokenClassification.from_pretrained('mrm8488/RuPERTa-base-finetuned-pos')
-
-id2label = {
-    "0": "O",
-    "1": "ADJ",
-    "2": "ADP",
-    "3": "ADV",
-    "4": "AUX",
-    "5": "CCONJ",
-    "6": "DET",
-    "7": "INTJ",
-    "8": "NOUN",
-    "9": "NUM",
-    "10": "PART",
-    "11": "PRON",
-    "12": "PROPN",
-    "13": "PUNCT",
-    "14": "SCONJ",
-    "15": "SYM",
-    "16": "VERB"
-}
-
-text ="Mis amigos están pensando viajar a Londres este verano."
-input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0)
-
-outputs = model(input_ids)
-last_hidden_states = outputs[0]
-
-for m in last_hidden_states:
-  for index, n in enumerate(m):
-    if(index > 0 and index <= len(text.split(" "))):
-      print(text.split(" ")[index-1] + ": " + id2label[str(torch.argmax(n).item())])
-      
-'''
-Output:
---------
-Mis: NUM
-amigos: PRON
-están: AUX
-pensando: ADV
-viajar: VERB
-a: ADP
-Londres: PROPN
-este: DET
-verano..: NOUN
-'''
-```
-Yeah! Not too bad 🎉
-
-> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
-
-> Made with <span style="color: #e25555;">&hearts;</span> in Spain

model_cards/mrm8488/electricidad-small-discriminator/README.md

@@ -43,8 +43,8 @@ import torch
 discriminator = ElectraForPreTraining.from_pretrained("mrm8488/electricidad-small-discriminator")
 tokenizer = ElectraTokenizerFast.from_pretrained("mrm8488/electricidad-small-discriminator")
 
-sentence = "el zorro rojo es muy rápido"
-fake_sentence = "el zorro rojo es muy ser"
+sentence = "El rápido zorro marrón salta sobre el perro perezoso"
+fake_sentence = "El rápido zorro marrón falsea sobre el perro perezoso"
 
 fake_tokens = tokenizer.tokenize(sentence)
 fake_inputs = tokenizer.encode(sentence, return_tensors="pt")
@@ -53,16 +53,9 @@ predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
 
 [print("%7s" % token, end="") for token in fake_tokens]
 
-[print("%7s" % int(prediction), end="") for prediction in predictions.tolist()[1:-1]]
-
-# Output:
-'''
-el  zorro   rojo     es    muy    ser      0      0      0      0      0      1[None, None, None, None, None, None]
-'''
+[print("%7s" % prediction, end="") for prediction in predictions.tolist()]
 ```
 
-As you can see there is a **1** in the place where the model detected the fake token (**ser**). So, it works! 🎉
-
 ## Acknowledgments
 
 I thank [🤗/transformers team](https://github.com/huggingface/transformers) for answering my doubts and Google for helping me with the [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc) program.

model_cards/mrm8488/gpt2-imdb-neg/README.md

@@ -20,9 +20,6 @@ A few examples of the model response to a query before and after optimisation:
 |I have watched 3 episodes |with this guy and he is such a talented actor...|	but the show is just plain awful and there ne...|	2.681171|	-4.512792|
 |We know that firefighters and|	police officers are forced to become populari...|	other chains have going to get this disaster ...|	1.367811|	-3.34017|
 
-## Training logs and metrics <img src="https://gblobscdn.gitbook.com/spaces%2F-Lqya5RvLedGEWPhtkjU%2Favatar.png?alt=media" width="25" height="25">
-Watch the whole training logs and metrics on [W&B](https://app.wandb.ai/mrm8488/gpt2-sentiment-negative?workspace=user-mrm8488)
-
 
 
 > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)

model_cards/oliverguhr/german-sentiment-bert/README.md

@@ -1,125 +0,0 @@
-# German Sentiment Classification with Bert
-
-This model was trained for sentiment classification of German language texts. To achieve the best results all model inputs needs to be preprocessed with the same procedure, that was applied during the training. To simplify the usage of the model, 
-we provide a Python package that bundles the code need for the preprocessing and inferencing. 
-
-The model uses the Googles Bert architecture and was trained on 1.834 million German-language samples. The training data contains texts from various domains like Twitter, Facebook and movie, app and hotel reviews. 
-You can find more information about the dataset and the training process in the [paper](http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.201.pdf).
-
-## Using the Python package
-
-To get started install the package from [pypi](https://pypi.org/project/germansentiment/):
-
-```bash
-pip install germansentiment
-```
-
-```python
-from germansentiment import SentimentModel
-
-model = SentimentModel()
-
-texts = [
-    "Mit keinem guten Ergebniss","Das ist gar nicht mal so gut",
-    "Total awesome!","nicht so schlecht wie erwartet",
-    "Der Test verlief positiv.","Sie fährt ein grünes Auto."]
-       
-result = model.predict_sentiment(texts)
-print(result)
-```
-
-The code above will output following list:
-
-```python
-["negative","negative","positive","positive","neutral", "neutral"]
-```
-
-## minimal working Sample
-
-
-```python
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-from typing import List
-import torch
-import re
-
-class SentimentModel():
-    def __init__(self, model_name: str):
-        self.model = AutoModelForSequenceClassification.from_pretrained(model_name)
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-        self.clean_chars = re.compile(r'[^A-Za-züöäÖÜÄß ]', re.MULTILINE)
-        self.clean_http_urls = re.compile(r'https*\S+', re.MULTILINE)
-        self.clean_at_mentions = re.compile(r'@\S+', re.MULTILINE)
-
-    def predict_sentiment(self, texts: List[str])-> List[str]:
-        texts = [self.clean_text(text) for text in texts]
-        # Add special tokens takes care of adding [CLS], [SEP], <s>... tokens in the right way for each model.
-        input_ids = self.tokenizer.batch_encode_plus(texts,pad_to_max_length=True, add_special_tokens=True)
-        input_ids = torch.tensor(input_ids["input_ids"])
-
-        with torch.no_grad():
-            logits = self.model(input_ids)    
-
-        label_ids = torch.argmax(logits[0], axis=1)
-
-        labels = [self.model.config.id2label[label_id] for label_id in label_ids.tolist()]
-        return labels
-
-    def replace_numbers(self,text: str) -> str:
-            return text.replace("0"," null").replace("1"," eins").replace("2"," zwei").replace("3"," drei").replace("4"," vier").replace("5"," fünf").replace("6"," sechs").replace("7"," sieben").replace("8"," acht").replace("9"," neun")         
-
-    def clean_text(self,text: str)-> str:    
-            text = text.replace("\n", " ")        
-            text = self.clean_http_urls.sub('',text)
-            text = self.clean_at_mentions.sub('',text)        
-            text = self.replace_numbers(text)                
-            text = self.clean_chars.sub('', text) # use only text chars                          
-            text = ' '.join(text.split()) # substitute multiple whitespace with single whitespace   
-            text = text.strip().lower()
-            return text
-
-texts = ["Mit keinem guten Ergebniss","Das war unfair", "Das ist gar nicht mal so gut",
-        "Total awesome!","nicht so schlecht wie erwartet", "Das ist gar nicht mal so schlecht",
-        "Der Test verlief positiv.","Sie fährt ein grünes Auto.", "Der Fall wurde an die Polzei übergeben."]
-
-model = SentimentModel(model_name = "oliverguhr/german-sentiment-bert")
-
-print(model.predict_sentiment(texts))
-```
-
-## Model and Data
-
-If you are interested in code and data that was used to train this model please have a look at [this repository](https://github.com/oliverguhr/german-sentiment) and our [paper](http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.201.pdf). Here is a table of the F1 scores that his model achieves on following datasets. Since we trained this model on a newer version of the transformer library, the results are slightly better than reported in the paper.
-
-| Dataset                                                      | F1 micro Score |
-| :----------------------------------------------------------- | -------------: |
-| [holidaycheck](https://github.com/oliverguhr/german-sentiment) |         0.9568 |
-| [scare](https://www.romanklinger.de/scare/)                  |         0.9418 |
-| [filmstarts](https://github.com/oliverguhr/german-sentiment) |         0.9021 |
-| [germeval](https://sites.google.com/view/germeval2017-absa/home) |         0.7536 |
-| [PotTS](https://www.aclweb.org/anthology/L16-1181/)          |         0.6780 |
-| [emotions](https://github.com/oliverguhr/german-sentiment)  |         0.9649 |
-| [sb10k](https://www.spinningbytes.com/resources/germansentiment/) |         0.7376 |
-| [Leipzig Wikipedia Corpus 2016](https://wortschatz.uni-leipzig.de/de/download/german) |         0.9967 |
-| all                                                          |         0.9639 |
-
-## Cite
-
-For feedback and questions contact me view mail or Twitter [@oliverguhr](https://twitter.com/oliverguhr). Please cite us if you found this useful:
-
-```
-@InProceedings{guhr-EtAl:2020:LREC,
-  author    = {Guhr, Oliver  and  Schumann, Anne-Kathrin  and  Bahrmann, Frank  and  Böhme, Hans Joachim},
-  title     = {Training a Broad-Coverage German Sentiment Classification Model for Dialog Systems},
-  booktitle      = {Proceedings of The 12th Language Resources and Evaluation Conference},
-  month          = {May},
-  year           = {2020},
-  address        = {Marseille, France},
-  publisher      = {European Language Resources Association},
-  pages     = {1620--1625},
-  url       = {https://www.aclweb.org/anthology/2020.lrec-1.201}
-}
-```
-
-

model_cards/savasy/bert-base-turkish-sentiment-cased/README.md

@@ -1,16 +1,15 @@
-# Bert-base Turkish Sentiment Model
+# Details
 
 https://huggingface.co/savasy/bert-base-turkish-sentiment-cased
 
 This model is used for Sentiment Analysis, which is based on BERTurk for Turkish Language https://huggingface.co/dbmdz/bert-base-turkish-cased
 
 
-## Dataset
+# Dataset
 
-The dataset is taken from the studies [[2]](#paper-2) and [[3]](#paper-3), and merged.
-
-* The study [2] gathered movie and product reviews. The products are book, DVD, electronics, and kitchen.
-The movie dataset is taken from a cinema Web page ([Beyazperde](www.beyazperde.com)) with
+We used product and movie dataset provided by the study [2] . This dataset includes
+movie and product reviews. The products are book, DVD, electronics, and kitchen.
+The movie dataset is taken from a cinema Web page (www.beyazperde.com) with
 5331 positive and 5331 negative sentences. Reviews in the Web page are marked in
 scale from 0 to 5 by the users who made the reviews. The study considered a review
 sentiment positive if the rating is equal to or bigger than 4, and negative if it is less
@@ -19,120 +18,34 @@ Web page. They constructed benchmark dataset consisting of reviews regarding som
 products (book, DVD, etc.). Likewise, reviews are marked in the range from 1 to 5,
 and majority class of reviews are 5. Each category has 700 positive and 700 negative
 reviews in which average rating of negative reviews is 2.27 and of positive reviews
-is 4.5. This dataset is also used by the study [[1]](#paper-1).
+is 4.5.
 
-* The study [[3]](#paper-3) collected tweet dataset. They proposed a new approach for automatically classifying the sentiment of microblog messages. The proposed approach is based on utilizing robust feature representation and fusion. 
 
-*Merged Dataset* 
+The dataset is used by following papers
 
-| *size*   | *data* |
-|--------|----|
-|   8000 |dev.tsv|
-|   8262 |test.tsv|
-|  32000 |train.tsv|
-|  *48290* |*total*|
-
-### The dataset is used by following papers
-
-<a id="paper-1">[1]</a> Yildirim, Savaş. (2020). Comparing Deep Neural Networks to Traditional Models for Sentiment Analysis in Turkish Language. 10.1007/978-981-15-1216-2_12. 
-
-<a id="paper-2">[2]</a> Demirtas, Erkin and Mykola Pechenizkiy. 2013. Cross-lingual polarity detection with machine translation. In Proceedings of the Second International Workshop on Issues of Sentiment
+ 
+* 1 Yildirim, Savaş. (2020). Comparing Deep Neural Networks to Traditional Models for Sentiment Analysis in Turkish Language. 10.1007/978-981-15-1216-2_12. 
+* 2 Demirtas, Erkin and Mykola Pechenizkiy. 2013. Cross-lingual polarity detection with machine translation. In Proceedings of the Second International Workshop on Issues of Sentiment
 Discovery and Opinion Mining (WISDOM ’13)
 
-<a id="paper-3">[3]</a> Hayran, A.,   Sert, M. (2017), "Sentiment Analysis on Microblog Data based on Word Embedding and Fusion Techniques", IEEE 25th Signal Processing and Communications Applications Conference (SIU 2017), Belek, Turkey
+# Code Usage
 
-
-## Training
-
-```shell
-export GLUE_DIR="./sst-2-newall"
-export TASK_NAME=SST-2
-
-python3 run_glue.py \
-  --model_type bert \
-  --model_name_or_path dbmdz/bert-base-turkish-uncased\
-  --task_name "SST-2" \
-  --do_train \
-  --do_eval \
-  --data_dir "./sst-2-newall" \
-  --max_seq_length 128 \
-  --per_gpu_train_batch_size 32 \
-  --learning_rate 2e-5 \
-  --num_train_epochs 3.0 \
-  --output_dir "./model"
 ```
-
-
-## Results
-
-> 05/10/2020 17:00:43 - INFO - transformers.trainer -   \*\*\*\*\* Running Evaluation \*\*\*\*\*  
-> 05/10/2020 17:00:43 - INFO - transformers.trainer -     Num examples = 7999  
-> 05/10/2020 17:00:43 - INFO - transformers.trainer -     Batch size = 8  
-> Evaluation: 100% 1000/1000 [00:34<00:00, 29.04it/s]  
-> 05/10/2020 17:01:17 - INFO - \_\_main__ -   \*\*\*\*\* Eval results sst-2 \*\*\*\*\*  
-> 05/10/2020 17:01:17 - INFO - \_\_main__ -     acc = 0.9539942492811602  
-> 05/10/2020 17:01:17 - INFO - \_\_main__ -     loss = 0.16348013816401363
-
-Accuracy is about **95.4%**
-
-
-## Code Usage
-
-```python
 from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
-
 model = AutoModelForSequenceClassification.from_pretrained("savasy/bert-base-turkish-sentiment-cased")
 tokenizer = AutoTokenizer.from_pretrained("savasy/bert-base-turkish-sentiment-cased")
 sa= pipeline("sentiment-analysis", tokenizer=tokenizer, model=model)
 
-p = sa("bu telefon modelleri çok kaliteli , her parçası çok özel bence")
+p= sa("bu telefon modelleri çok kaliteli , her parçası çok özel bence")
 print(p)
-# [{'label': 'LABEL_1', 'score': 0.9871089}]
-print(p[0]['label'] == 'LABEL_1')
-# True
+#[{'label': 'LABEL_1', 'score': 0.9871089}]
+print (p[0]['label']=='LABEL_1')
+#True
 
-p = sa("Film çok kötü ve çok sahteydi")
+
+p= sa("Film çok kötü ve çok sahteydi")
 print(p)
-# [{'label': 'LABEL_0', 'score': 0.9975505}]
-print(p[0]['label'] == 'LABEL_1')
-# False
-```
-
-
-## Test
-### Data
-
-Suppose your file has lots of lines of comment and label (1 or 0) at the end  (tab seperated)
-
-> comment1 ... \t label  
-> comment2 ... \t label  
-> ...
-
-### Code
-
-```python
-from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
-
-model = AutoModelForSequenceClassification.from_pretrained("savasy/bert-base-turkish-sentiment-cased")
-tokenizer = AutoTokenizer.from_pretrained("savasy/bert-base-turkish-sentiment-cased")
-sa = pipeline("sentiment-analysis", tokenizer=tokenizer, model=model)
-
-input_file = "/path/to/your/file/yourfile.tsv"
-
-i, crr = 0, 0
-for line in open(input_file):
-    lines = line.strip().split("\t")
-    if len(lines) == 2:
-        
-        i = i + 1
-        if i%100 == 0:
-            print(i)
-        
-        pred = sa(lines[0])
-        pred = pred[0]["label"].split("_")[1]
-        
-        if pred == lines[1]:
-        crr = crr + 1
-
-print(crr, i, crr/i)
+#[{'label': 'LABEL_0', 'score': 0.9975505}]
+print (p[0]['label']=='LABEL_1')
+#False
 ```

model_cards/savasy/bert-base-turkish-squad/README.md

@@ -1,67 +0,0 @@
----
-language: turkish
----
-# Turkish SQuAD  Model : Question Answering
-
-I fine-tuned Turkish-Bert-Model for Question-Answering problem with Turkish version of SQuAD; TQuAD 
-* BERT-base: https://huggingface.co/dbmdz/bert-base-turkish-uncased
-* TQuAD dataset:  https://github.com/TQuad/turkish-nlp-qa-dataset
-
-
-# Training Code
-
-```
-!python3 run_squad.py \
-  --model_type bert \
-  --model_name_or_path dbmdz/bert-base-turkish-uncased\
-  --do_train \
-  --do_eval \
-  --train_file trainQ.json \
-  --predict_file dev1.json \
-  --per_gpu_train_batch_size 12 \
-  --learning_rate 3e-5 \
-  --num_train_epochs 5.0 \
-  --max_seq_length 384 \
-  --doc_stride 128 \
-  --output_dir "./model"
-```
-
-
-# Example Usage
-
-> Load Model
-```
-from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
-import torch
-
-tokenizer = AutoTokenizer.from_pretrained("./model")
-model = AutoModelForQuestionAnswering.from_pretrained("./model")
-nlp=pipeline("question-answering", model=model, tokenizer=tokenizer)
-```
-
-> Apply the model
-```
-
-sait="ABASIYANIK, Sait Faik. Hikayeci (Adapazarı 23 Kasım 1906-İstanbul 11 Mayıs 1954). \
-İlk öğrenimine Adapazarı’nda Rehber-i Terakki Mektebi’nde başladı. İki yıl kadar Adapazarı İdadisi’nde okudu.\
-İstanbul Erkek Lisesi’nde devam ettiği orta öğrenimini Bursa Lisesi’nde tamamladı (1928). İstanbul Edebiyat \
-Fakültesi’ne iki yıl devam ettikten sonra babasının isteği üzerine iktisat öğrenimi için İsviçre’ye gitti. \
-Kısa süre sonra iktisat öğrenimini bırakarak Lozan’dan Grenoble’a geçti. Üç yıl başıboş bir edebiyat öğrenimi \
-gördükten sonra babası tarafından geri çağrıldı (1933). Bir müddet Halıcıoğlu Ermeni Yetim Mektebi'nde Türkçe \
-gurup dersleri öğretmenliği yaptı. Ticarete atıldıysa da tutunamadı. Bir ay Haber gazetesinde adliye muhabirliği\
-yaptı (1942). Babasının ölümü üzerine aileden kalan emlakin geliri ile avare bir hayata başladı. Evlenemedi.\
-Yazları Burgaz adasındaki köşklerinde, kışları Şişli’deki apartmanlarında annesi ile beraber geçen bu fazla \
-içkili bohem hayatı ömrünün sonuna kadar sürdü."
-
-print(nlp(question="Ne zaman avare bir hayata başladı?", context=sait))
-print(nlp(question="Sait Faik hangi Lisede orta öğrenimini tamamladı?", context=sait))
-
-```
-```
-# Ask your self ! type your question
-print(nlp(question="...?", context=sait))
-```
-
-
-Check My other Model
-https://huggingface.co/savasy

model_cards/seiya/oubiobert-base-uncased/README.md

@@ -1,51 +0,0 @@
----
-tags:
-- exbert
-license: apache-2.0
----
-
-# ouBioBERT-Base, Uncased
-
-Bidirectional Encoder Representations from Transformers for Biomedical Text Mining by Osaka University (ouBioBERT) is a language model based on the BERT-Base (Devlin, et al., 2019) architecture. We pre-trained ouBioBERT on PubMed abstracts from the PubMed baseline (ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline) via our method.  
-
-The details of the pre-training procedure can be found in Wada, et al. (2020).  
-
-## Evaluation
-
-We evaluated the performance of ouBioBERT in terms of the biomedical language understanding evaluation (BLUE) benchmark (Peng, et al., 2019). The numbers are mean (standard deviation) on five different random seeds.  
-
-
-| Dataset         |  Task Type                   |  Score       |
-|:----------------|:-----------------------------|-------------:|
-| MedSTS          |  Sentence similarity         |  84.9 (0.6)  |
-| BIOSSES         |  Sentence similarity         |  92.3 (0.8)  |
-| BC5CDR-disease  |  Named-entity recognition    |  87.4 (0.1)  |
-| BC5CDR-chemical |  Named-entity recognition    |  93.7 (0.2)  |
-| ShARe/CLEFE     |  Named-entity recognition    |  80.1 (0.4)  |
-| DDI             |  Relation extraction         |  81.1 (1.5)  |
-| ChemProt        |  Relation extraction         |  75.0 (0.3)  |
-| i2b2 2010       |  Relation extraction         |  74.0 (0.8)  |
-| HoC             |  Document classification     |  86.4 (0.5)  |
-| MedNLI          |  Inference                   |  83.6 (0.7)  |
-| **Total**       |  Macro average of the scores |**83.8 (0.3)**|
-
-
-## Code for Fine-tuning
-We made the source code for fine-tuning freely available at [our repository](https://github.com/sy-wada/blue_benchmark_with_transformers).
-
-## Citation
-
-If you use our work in your research, please kindly cite the following paper:  
-
-```bibtex
-@misc{2005.07202,
-Author = {Shoya Wada and Toshihiro Takeda and Shiro Manabe and Shozo Konishi and Jun Kamohara and Yasushi Matsumura},
-Title = {A pre-training technique to localize medical BERT and enhance BioBERT},
-Year = {2020},
-Eprint = {arXiv:2005.07202},
-}
-```
-
-<a href="https://huggingface.co/exbert/?model=seiya/oubiobert-base-uncased&sentence=Coronavirus%20disease%20(COVID-19)%20is%20caused%20by%20SARS-COV2%20and%20represents%20the%20causative%20agent%20of%20a%20potentially%20fatal%20disease%20that%20is%20of%20great%20global%20public%20health%20concern.">
-	<img width="300px" src="https://hf-dinosaur.huggingface.co/exbert/button.png">
-</a>

model_cards/valhalla/t5-base-squad/README.md

@@ -1,38 +0,0 @@
-# T5 for question-answering
-This is T5-base model fine-tuned on SQuAD1.1 for QA using text-to-text approach
-
-## Model training
-This model was trained on colab TPU with 35GB RAM for 4 epochs
-
-## Results:
-| Metric      | #Value  |
-|-------------|---------|
-| Exact Match | 81.5610 |
-| F1          | 89.9601 |
-
-## Model in Action 🚀
-```
-from transformers import AutoModelWithLMHead, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("valhalla/t5-base-squad")
-model = AutoModelWithLMHead.from_pretrained("valhalla/t5-base-squad")
-
-def get_answer(question, context):
-  input_text = "question: %s  context: %s </s>" % (question, context)
-  features = tokenizer.batch_encode_plus([input_text], return_tensors='pt')
-
-  out = model.generate(input_ids=features['input_ids'], 
-               attention_mask=features['attention_mask'])
-  
-  return tokenizer.decode(out[0])
-
-context = "In Norse mythology, Valhalla is a majestic, enormous hall located in Asgard, ruled over by the god Odin."
-question = "What is Valhalla ?"
-
-get_answer(question, context)
-# output: 'a majestic, enormous hall located in Asgard, ruled over by the god Odin'
-```
-Play with this model [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1a5xpJiUjZybfU9Mi-aDkOp116PZ9-wni?usp=sharing)
-
-> Created by Suraj Patil [![Github icon](https://cdn0.iconfinder.com/data/icons/octicons/1024/mark-github-32.png)](https://github.com/patil-suraj/)
-[![Twitter icon](https://cdn0.iconfinder.com/data/icons/shift-logotypes/32/Twitter-32.png)](https://twitter.com/psuraj28)

notebooks/02-transformers.ipynb

@@ -3,6 +3,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "collapsed": true,
     "pycharm": {
      "is_executing": false,
      "name": "#%% md\n"
@@ -76,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "pycharm": {
      "is_executing": false,
@@ -84,7 +85,77 @@
     },
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: transformers in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (2.5.1)\n",
+      "Requirement already satisfied: filelock in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from transformers) (3.0.12)\n",
+      "Requirement already satisfied: sentencepiece in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from transformers) (0.1.83)\n",
+      "Requirement already satisfied: boto3 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from transformers) (1.12.0)\n",
+      "Requirement already satisfied: requests in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from transformers) (2.22.0)\n",
+      "Requirement already satisfied: numpy in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from transformers) (1.18.1)\n",
+      "Requirement already satisfied: sacremoses in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from transformers) (0.0.35)\n",
+      "Requirement already satisfied: tokenizers==0.5.2 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from transformers) (0.5.2)\n",
+      "Requirement already satisfied: regex!=2019.12.17 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from transformers) (2020.1.8)\n",
+      "Requirement already satisfied: tqdm>=4.27 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from transformers) (4.42.1)\n",
+      "Requirement already satisfied: s3transfer<0.4.0,>=0.3.0 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from boto3->transformers) (0.3.3)\n",
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+      "Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from boto3->transformers) (0.9.4)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from requests->transformers) (2019.11.28)\n",
+      "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from requests->transformers) (2.8)\n",
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+      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from requests->transformers) (3.0.4)\n",
+      "Requirement already satisfied: joblib in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from sacremoses->transformers) (0.14.0)\n",
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+      "Requirement already satisfied: docutils<0.16,>=0.10 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from botocore<1.16.0,>=1.15.0->boto3->transformers) (0.15.2)\n",
+      "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from botocore<1.16.0,>=1.15.0->boto3->transformers) (2.8.1)\n",
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+      "Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorflow==2.1.0) (1.1.0)\n",
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+      "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorflow==2.1.0) (1.0.8)\n",
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+      "Requirement already satisfied: setuptools in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from protobuf>=3.8.0->tensorflow==2.1.0) (45.2.0.post20200210)\n",
+      "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (1.11.2)\n",
+      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (0.4.1)\n",
+      "Requirement already satisfied: markdown>=2.6.8 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (3.1.1)\n",
+      "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (1.0.0)\n",
+      "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (2.22.0)\n",
+      "Requirement already satisfied: h5py in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from keras-applications>=1.0.8->tensorflow==2.1.0) (2.10.0)\n",
+      "Requirement already satisfied: rsa<4.1,>=3.1.4 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (4.0)\n",
+      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (4.0.0)\n",
+      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (0.2.8)\n",
+      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (1.3.0)\n",
+      "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (2.8)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (2019.11.28)\n",
+      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (3.0.4)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (1.25.8)\r\n",
+      "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from rsa<4.1,>=3.1.4->google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (0.4.8)\r\n",
+      "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (3.1.0)\r\n"
+     ]
+    }
+   ],
    "source": [
     "!pip install transformers\n",
     "!pip install tensorflow==2.1.0"
@@ -103,7 +174,7 @@
     {
      "data": {
       "text/plain": [
-       "<torch.autograd.grad_mode.set_grad_enabled at 0x7f10b441e890>"
+       "<torch.autograd.grad_mode.set_grad_enabled at 0x102c0ce10>"
       ]
      },
      "execution_count": 2,
@@ -370,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "pycharm": {
      "is_executing": false
@@ -387,22 +458,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "pycharm": {
      "is_executing": false
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "output differences: 1.6236e-05\n",
-      "pooled differences: -1.3039e-08\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# transformers generates a ready to use dictionary with all the required parameters for the specific framework.\n",
     "input_tf = tokenizer.encode_plus(\"This is a sample input\", return_tensors=\"tf\")\n",
@@ -414,7 +476,7 @@
     "# Models outputs 2 values (The value for each tokens, the pooled representation of the input sentence)\n",
     "# Here we compare the output differences between PyTorch and TensorFlow.\n",
     "for name, o_tf, o_pt in zip([\"output\", \"pooled\"], output_tf, output_pt):\n",
-    "    print(\"{} differences: {:.5}\".format(name, (o_tf.numpy() - o_pt.numpy()).sum()))"
+    "    print(\"{} differences: {}\".format(name, (o_tf.numpy() - o_pt.numpy()).sum()))"
    ]
   },
   {
@@ -442,24 +504,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "pycharm": {
      "is_executing": false
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 232 ms, sys: 0 ns, total: 232 ms\n",
-      "Wall time: 21.1 ms\n",
-      "CPU times: user 511 ms, sys: 0 ns, total: 511 ms\n",
-      "Wall time: 43.9 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from transformers import DistilBertModel\n",
     "\n",
@@ -490,25 +541,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "pycharm": {
      "is_executing": false
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tokens (int)      : [102, 12272, 9355, 5746, 30881, 215, 261, 5945, 4118, 212, 2414, 153, 1942, 232, 3532, 566, 103]\n",
-      "Tokens (str)      : ['[CLS]', 'Hug', '##ging', 'Fac', '##e', 'ist', 'eine', 'französische', 'Firma', 'mit', 'Sitz', 'in', 'New', '-', 'York', '.', '[SEP]']\n",
-      "Tokens (attn_mask): [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-      "\n",
-      "Token wise output: torch.Size([1, 7, 768]), Pooled output: torch.Size([1, 768])\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Let's load German BERT from the Bavarian State Library\n",
     "de_bert = BertModel.from_pretrained(\"dbmdz/bert-base-german-cased\")\n",
@@ -518,14 +557,7 @@
     "    \"Hugging Face ist eine französische Firma mit Sitz in New-York.\",\n",
     "    return_tensors=\"pt\"\n",
     ")\n",
-    "print(\"Tokens (int)      : {}\".format(de_input['input_ids'].tolist()[0]))\n",
-    "print(\"Tokens (str)      : {}\".format([de_tokenizer.convert_ids_to_tokens(s) for s in de_input['input_ids'].tolist()[0]]))\n",
-    "print(\"Tokens (attn_mask): {}\".format(de_input['attention_mask'].tolist()[0]))\n",
-    "print()\n",
-    "\n",
-    "output_de, pooled_de = de_bert(**de_input)\n",
-    "\n",
-    "print(\"Token wise output: {}, Pooled output: {}\".format(outputs.shape, pooled.shape))"
+    "output_de, pooled_de = de_bert(**de_input)"
    ]
   }
  ],
@@ -545,7 +577,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6"
   },
   "pycharm": {
    "stem_cell": {
@@ -558,5 +590,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 1
 }

notebooks/03-pipelines.ipynb

@@ -30,8 +30,7 @@
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@@ -2351,16 +2105,6 @@
     }
   },
   "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/huggingface/transformers/blob/generation_pipeline_docs/notebooks/03-pipelines.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -2426,29 +2170,13 @@
         },
         "id": "4maAknWNrl_N",
         "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 102
-        },
-        "outputId": "467e3cc8-a069-47da-8029-86e4142c7dde"
+        "colab": {}
       },
       "source": [
         "!pip install -q transformers"
       ],
-      "execution_count": 2,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "\u001b[K     |████████████████████████████████| 645kB 4.4MB/s \n",
-            "\u001b[K     |████████████████████████████████| 3.8MB 11.7MB/s \n",
-            "\u001b[K     |████████████████████████████████| 890kB 51.5MB/s \n",
-            "\u001b[K     |████████████████████████████████| 1.0MB 46.0MB/s \n",
-            "\u001b[?25h  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
-          ],
-          "name": "stdout"
-        }
-      ]
+      "execution_count": 0,
+      "outputs": []
     },
     {
       "cell_type": "code",
@@ -2491,7 +2219,6 @@
         },
         "id": "AMRXHQw9rl_d",
         "colab_type": "code",
-        "outputId": "a7a10851-b71e-4553-9afc-04066120410d",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 83,
@@ -2505,13 +2232,14 @@
             "ad84da685cf44abb90d17d9d2e023b48",
             "a246f9eea2d7440cb979e728741d2e32"
           ]
-        }
+        },
+        "outputId": "a7a10851-b71e-4553-9afc-04066120410d"
       },
       "source": [
         "nlp_sentence_classif = pipeline('sentiment-analysis')\n",
         "nlp_sentence_classif('Such a nice weather outside !')"
       ],
-      "execution_count": 0,
+      "execution_count": 3,
       "outputs": [
         {
           "output_type": "display_data",
@@ -2572,7 +2300,6 @@
         },
         "id": "B3BDRX_Krl_n",
         "colab_type": "code",
-        "outputId": "a6b90b11-a272-4ecb-960d-4c682551b399",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 185,
@@ -2586,13 +2313,14 @@
             "405afa5bb8b840d8bc0850e02f593ce4",
             "78c718e3d5fa4cb892217260bea6d540"
           ]
-        }
+        },
+        "outputId": "a6b90b11-a272-4ecb-960d-4c682551b399"
       },
       "source": [
         "nlp_token_class = pipeline('ner')\n",
         "nlp_token_class('Hugging Face is a French company based in New-York.')"
       ],
-      "execution_count": 0,
+      "execution_count": 4,
       "outputs": [
         {
           "output_type": "display_data",
@@ -2656,7 +2384,6 @@
         },
         "id": "ND_8LzQKrl_u",
         "colab_type": "code",
-        "outputId": "c59ae695-c465-4de6-fa6e-181d8f1a3992",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 117,
@@ -2670,13 +2397,14 @@
             "cd64e3f20b23483daa79712bde6622ea",
             "67cbaa1f55d24e62ad6b022af36bca56"
           ]
-        }
+        },
+        "outputId": "c59ae695-c465-4de6-fa6e-181d8f1a3992"
       },
       "source": [
         "nlp_qa = pipeline('question-answering')\n",
         "nlp_qa(context='Hugging Face is a French company based in New-York.', question='Where is based Hugging Face ?')"
       ],
-      "execution_count": 0,
+      "execution_count": 5,
       "outputs": [
         {
           "output_type": "display_data",
@@ -2742,7 +2470,6 @@
         },
         "id": "zpJQ2HXNrl_4",
         "colab_type": "code",
-        "outputId": "3fb62e7a-25a6-4b06-ced8-51eb8aa6bf33",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 321,
@@ -2756,13 +2483,14 @@
             "a35703cc8ff44e93a8c0eb413caddc40",
             "9df7014c99b343f3b178fa020ff56010"
           ]
-        }
+        },
+        "outputId": "3fb62e7a-25a6-4b06-ced8-51eb8aa6bf33"
       },
       "source": [
         "nlp_fill = pipeline('fill-mask')\n",
         "nlp_fill('Hugging Face is a French company based in ' + nlp_fill.tokenizer.mask_token)"
       ],
-      "execution_count": 0,
+      "execution_count": 6,
       "outputs": [
         {
           "output_type": "display_data",
@@ -2832,11 +2560,11 @@
       "metadata": {
         "id": "8BaOgzi1u1Yc",
         "colab_type": "code",
-        "outputId": "2168e437-cfba-4247-a38c-07f02f555c6e",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 88
-        }
+        },
+        "outputId": "2168e437-cfba-4247-a38c-07f02f555c6e"
       },
       "source": [
         "TEXT_TO_SUMMARIZE = \"\"\" \n",
@@ -2862,7 +2590,7 @@
         "summarizer = pipeline('summarization')\n",
         "summarizer(TEXT_TO_SUMMARIZE)"
       ],
-      "execution_count": 0,
+      "execution_count": 7,
       "outputs": [
         {
           "output_type": "stream",
@@ -2903,7 +2631,6 @@
       "metadata": {
         "id": "8FwayP4nwV3Z",
         "colab_type": "code",
-        "outputId": "66956816-c924-4718-fe58-cabef7d51974",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 83,
@@ -2917,14 +2644,15 @@
             "ad78042ee71a41fd989e4b4ce9d2e3c1",
             "40c8d2617f3d4c84b923b140456fa5da"
           ]
-        }
+        },
+        "outputId": "66956816-c924-4718-fe58-cabef7d51974"
       },
       "source": [
         "# English to French\n",
         "translator = pipeline('translation_en_to_fr')\n",
         "translator(\"HuggingFace is a French company that is based in New York City. HuggingFace's mission is to solve NLP one commit at a time\")"
       ],
-      "execution_count": 0,
+      "execution_count": 8,
       "outputs": [
         {
           "output_type": "display_data",
@@ -2968,7 +2696,6 @@
       "metadata": {
         "colab_type": "code",
         "id": "ra0-WfznwoIW",
-        "outputId": "278a3d5f-cc42-40bc-a9db-c92ec5a3a2f0",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 83,
@@ -2982,14 +2709,15 @@
             "4486f8a2efc34b9aab3864eb5ad2ba48",
             "d6228324f3444aa6bd1323d65ae4ff75"
           ]
-        }
+        },
+        "outputId": "278a3d5f-cc42-40bc-a9db-c92ec5a3a2f0"
       },
       "source": [
         "# English to German\n",
         "translator = pipeline('translation_en_to_de')\n",
         "translator(\"The history of natural language processing (NLP) generally started in the 1950s, although work can be found from earlier periods.\")"
       ],
-      "execution_count": 0,
+      "execution_count": 9,
       "outputs": [
         {
           "output_type": "display_data",
@@ -3028,89 +2756,6 @@
         }
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "qPUpg0M8hCtB",
-        "colab_type": "text"
-      },
-      "source": [
-        "## 7. Text Generation\n",
-        "\n",
-        "Text generation is currently supported by GPT-2, OpenAi-GPT, TransfoXL, XLNet, CTRL and Reformer."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "5pKfxTxohXuZ",
-        "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 120,
-          "referenced_widgets": [
-            "3c86415352574190b71e1fe5a15d36f1",
-            "dd2c9dd935754cf2802233053554c21c",
-            "8ae3be32d9c845e59fdb1c47884d48aa",
-            "4dea0031f3554752ad5aad01fe516a60",
-            "1efb96d931a446de92f1930b973ae846",
-            "6a4f5aab5ba949fd860b5a35bba7db9c",
-            "4b02b2e964ad49af9f7ce7023131ceb8",
-            "0ae8a68c3668401da8d8a6d5ec9cac8f"
-          ]
-        },
-        "outputId": "8705f6b4-2413-4ac6-f72d-e5ecce160662"
-      },
-      "source": [
-        "text_generator = pipeline(\"text-generation\")\n",
-        "text_generator(\"Today is a beautiful day and I will\")"
-      ],
-      "execution_count": 5,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "3c86415352574190b71e1fe5a15d36f1",
-              "version_minor": 0,
-              "version_major": 2
-            },
-            "text/plain": [
-              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=230.0, style=ProgressStyle(description_…"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "[{'generated_text': 'Today is a beautiful day and I will celebrate my birthday!\"\\n\\nThe mother told CNN the two had planned their meal together. After dinner, she added that she and I walked down the street and stopped at a diner near her home. \"He'}]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 5
-        }
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -3118,7 +2763,7 @@
         "colab_type": "text"
       },
       "source": [
-        "## 8. Projection - Features Extraction "
+        "## 7. Projection - Features Extraction "
       ]
     },
     {
@@ -3130,7 +2775,6 @@
         },
         "id": "O4SjR1QQrl__",
         "colab_type": "code",
-        "outputId": "2ce966d5-7a89-4488-d48f-626d1c2a8222",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 83,
@@ -3144,7 +2788,8 @@
             "31d97ecf78fa412c99e6659196d82828",
             "c6be5d48ec3c4c799d1445607e5f1ac6"
           ]
-        }
+        },
+        "outputId": "2ce966d5-7a89-4488-d48f-626d1c2a8222"
       },
       "source": [
         "import numpy as np\n",
@@ -3152,7 +2797,7 @@
         "output = nlp_features('Hugging Face is a French company based in Paris')\n",
         "np.array(output).shape   # (Samples, Tokens, Vector Size)\n"
       ],
-      "execution_count": 0,
+      "execution_count": 10,
       "outputs": [
         {
           "output_type": "display_data",
@@ -3216,7 +2861,6 @@
         },
         "id": "yFlBPQHtrmAH",
         "colab_type": "code",
-        "outputId": "03cc3207-a7e8-49fd-904a-63a7a1d0eb7a",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 116,
@@ -3228,7 +2872,8 @@
             "62b10ca525cc4ac68f3a006434eb7416",
             "211109537fbe4e60b89a238c89db1346"
           ]
-        }
+        },
+        "outputId": "03cc3207-a7e8-49fd-904a-63a7a1d0eb7a"
       },
       "source": [
         "task = widgets.Dropdown(\n",
@@ -3261,7 +2906,7 @@
         "input.on_submit(forward)\n",
         "display(task, input)"
       ],
-      "execution_count": 0,
+      "execution_count": 11,
       "outputs": [
         {
           "output_type": "display_data",
@@ -3313,7 +2958,6 @@
         },
         "id": "GCoKbBTYrmAN",
         "colab_type": "code",
-        "outputId": "57c3a647-160a-4b3a-e852-e7a1daf1294a",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 143,
@@ -3325,7 +2969,8 @@
             "d305ba1662e3466c93ab5cca7ebf8f33",
             "879f7a3747ad455d810c7a29918648ee"
           ]
-        }
+        },
+        "outputId": "57c3a647-160a-4b3a-e852-e7a1daf1294a"
       },
       "source": [
         "context = widgets.Textarea(\n",
@@ -3350,7 +2995,7 @@
         "query.on_submit(forward)\n",
         "display(context, query)"
       ],
-      "execution_count": 0,
+      "execution_count": 12,
       "outputs": [
         {
           "output_type": "display_data",

notebooks/04-onnx-export.ipynb

@@ -1,492 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "jBasof3bv1LB"
-   },
-   "source": [
-    "<h1><center>How to export 🤗 Transformers Models to ONNX ?<h1><center>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[ONNX](http://onnx.ai/) is open format for machine learning models. It allows to save your neural network's computation graph in a framework agnostic way, which might be particulary helpful when deploying deep learning models.\n",
-    "\n",
-    "Indeed, businesses might have other requirements _(languages, hardware, ...)_ for which the training framework might not be the best suited in inference scenarios. In that context, having a representation of the actual computation graph that can be shared accross various business units and logics across an organization might be a desirable component.\n",
-    "\n",
-    "Along with the serialization format, ONNX also provides a runtime library which allows efficient and hardware specific execution of the ONNX graph. This is done through the [onnxruntime](https://microsoft.github.io/onnxruntime/) project and already includes collaborations with many hardware vendors to seamlessly deploy models on various platforms.\n",
-    "\n",
-    "Through this notebook we'll walk you through the process to convert a PyTorch or TensorFlow transformers model to the [ONNX](http://onnx.ai/) and leverage [onnxruntime](https://microsoft.github.io/onnxruntime/) to run inference tasks on models from  🤗 __transformers__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "yNnbrSg-5e1s"
-   },
-   "source": [
-    "## Exporting 🤗 transformers model to ONNX\n",
-    "\n",
-    "---\n",
-    "\n",
-    "Exporting models _(either PyTorch or TensorFlow)_ is easily achieved through the conversion tool provided as part of 🤗 __transformers__ repository. \n",
-    "\n",
-    "Under the hood the process is sensibly the following: \n",
-    "\n",
-    "1. Allocate the model from transformers (**PyTorch or TensorFlow**)\n",
-    "2. Forward dummy inputs through the model this way **ONNX** can record the set of operations executed\n",
-    "3. Optionally define dynamic axes on input and output tensors\n",
-    "4. Save the graph along with the network parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "!pip install --upgrade git+https://github.com/huggingface/transformers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "PwAaOchY4N2-"
-   },
-   "outputs": [],
-   "source": [
-    "!rm -rf onnx/\n",
-    "from transformers.convert_graph_to_onnx import convert\n",
-    "\n",
-    "# Handles all the above steps for you\n",
-    "convert(framework=\"pt\", model=\"bert-base-cased\", output=\"onnx/bert-base-cased.onnx\", opset=11)\n",
-    "\n",
-    "# Tensorflow \n",
-    "# convert(framework=\"tf\", model=\"bert-base-cased\", output=\"onnx/bert-base-cased.onnx\", opset=11)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## How to leverage runtime for inference over an ONNX graph\n",
-    "\n",
-    "---\n",
-    "\n",
-    "As mentionned in the introduction, **ONNX** is a serialization format and many side projects can load the saved graph and run the actual computations from it. Here, we'll focus on the official [onnxruntime](https://microsoft.github.io/onnxruntime/). The runtime is implemented in C++ for performance reasons and provides API/Bindings for C++, C, C#, Java and Python.\n",
-    "\n",
-    "In the case of this notebook, we will use the Python API to highlight how to load a serialized **ONNX** graph and run inference workload on various backends through **onnxruntime**.\n",
-    "\n",
-    "**onnxruntime** is available on pypi:\n",
-    "\n",
-    "- onnxruntime: ONNX + MLAS (Microsoft Linear Algebra Subprograms)\n",
-    "- onnxruntime-gpu: ONNX + MLAS + CUDA\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "!pip install transformers onnxruntime-gpu onnx psutil matplotlib"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "-gP08tHfBvgY"
-   },
-   "source": [
-    "## Preparing for an Inference Session\n",
-    "\n",
-    "---\n",
-    "\n",
-    "Inference is done using a specific backend definition which turns on hardware specific optimizations of the graph. \n",
-    "\n",
-    "Optimizations are basically of three kinds: \n",
-    "\n",
-    "- **Constant Folding**: Convert static variables to constants in the graph \n",
-    "- **Deadcode Elimination**: Remove nodes never accessed in the graph\n",
-    "- **Operator Fusing**: Merge multiple instruction into one (Linear -> ReLU can be fused to be LinearReLU)\n",
-    "\n",
-    "ONNX Runtime automatically applies most optimizations by setting specific `SessionOptions`.\n",
-    "\n",
-    "Note:Some of the latest optimizations that are not yet integrated into ONNX Runtime are available in [optimization script](https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/python/tools/transformers) that tunes models for the best performance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "# # An optional step unless\n",
-    "# # you want to get a model with mixed precision for perf accelartion on newer GPU\n",
-    "# # or you are working with Tensorflow(tf.keras) models or pytorch models other than bert\n",
-    "\n",
-    "# !pip install onnxruntime-tools\n",
-    "# from onnxruntime_tools import optimizer\n",
-    "\n",
-    "# # Mixed precision conversion for bert-base-cased model converted from Pytorch\n",
-    "# optimized_model = optimizer.optimize_model(\"bert-base-cased.onnx\", model_type='bert', num_heads=12, hidden_size=768)\n",
-    "# optimized_model.convert_model_float32_to_float16()\n",
-    "# optimized_model.save_model_to_file(\"bert-base-cased.onnx\")\n",
-    "\n",
-    "# # optimizations for bert-base-cased model converted from Tensorflow(tf.keras)\n",
-    "# optimized_model = optimizer.optimize_model(\"bert-base-cased.onnx\", model_type='bert_keras', num_heads=12, hidden_size=768)\n",
-    "# optimized_model.save_model_to_file(\"bert-base-cased.onnx\")\n"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from os import environ\n",
-    "from psutil import cpu_count\n",
-    "\n",
-    "# Constants from the performance optimization available in onnxruntime\n",
-    "# It needs to be done before importing onnxruntime\n",
-    "environ[\"OMP_NUM_THREADS\"] = str(cpu_count(logical=True))\n",
-    "environ[\"OMP_WAIT_POLICY\"] = 'ACTIVE'\n",
-    "\n",
-    "from onnxruntime import InferenceSession, SessionOptions, get_all_providers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "2k-jHLfdcTFS"
-   },
-   "outputs": [],
-   "source": [
-    "def create_model_for_provider(model_path: str, provider: str) -> InferenceSession: \n",
-    "  \n",
-    "  assert provider in get_all_providers(), f\"provider {provider} not found, {get_all_providers()}\"\n",
-    "\n",
-    "  # Few properties than might have an impact on performances (provided by MS)\n",
-    "  options = SessionOptions()\n",
-    "  options.intra_op_num_threads = 1\n",
-    "\n",
-    "  # Load the model as a graph and prepare the CPU backend \n",
-    "  return InferenceSession(model_path, options, providers=[provider])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "teJdG3amE-hR"
-   },
-   "source": [
-    "## Forwarding through our optimized ONNX model running on CPU\n",
-    "\n",
-    "---\n",
-    "\n",
-    "When the model is loaded for inference over a specific provider, for instance **CPUExecutionProvider** as above, an optimized graph can be saved. This graph will might include various optimizations, and you might be able to see some **higher-level** operations in the graph _(through [Netron](https://github.com/lutzroeder/Netron) for instance)_ such as:\n",
-    "- **EmbedLayerNormalization**\n",
-    "- **Attention**\n",
-    "- **FastGeLU**\n",
-    "\n",
-    "These operations are an example of the kind of optimization **onnxruntime** is doing, for instance here gathering multiple operations into bigger one _(Operator Fusing)_."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 34
-    },
-    "colab_type": "code",
-    "id": "dmC22kJfVGYe",
-    "outputId": "f3aba5dc-15c0-4f82-b38c-1bbae1bf112e"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sequence output: (1, 6, 768), Pooled output: (1, 768)\n"
-     ]
-    }
-   ],
-   "source": [
-    "from transformers import BertTokenizerFast\n",
-    "\n",
-    "tokenizer = BertTokenizerFast.from_pretrained(\"bert-base-cased\")\n",
-    "cpu_model = create_model_for_provider(\"onnx/bert-base-cased.onnx\", \"CPUExecutionProvider\")\n",
-    "\n",
-    "# Inputs are provided through numpy array\n",
-    "model_inputs = tokenizer.encode_plus(\"My name is Bert\", return_tensors=\"pt\")\n",
-    "inputs_onnx = {k: v.cpu().detach().numpy() for k, v in model_inputs.items()}\n",
-    "\n",
-    "# Run the model (None = get all the outputs)\n",
-    "sequence, pooled = cpu_model.run(None, inputs_onnx)\n",
-    "\n",
-    "# Print information about outputs\n",
-    "\n",
-    "print(f\"Sequence output: {sequence.shape}, Pooled output: {pooled.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Kda1e7TkEqNR"
-   },
-   "source": [
-    "## Benchmarking different CPU & GPU providers\n",
-    "\n",
-    "_**Disclamer: results may vary from the actual hardware used to run the model**_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 170
-    },
-    "colab_type": "code",
-    "id": "WcdFZCvImVig",
-    "outputId": "bfd779a1-0bc7-42db-8587-e52a485ec5e3"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Doing GPU inference on TITAN RTX\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Warming up: 100%|██████████| 10/10 [00:00<00:00, 333.82it/s]\n",
-      "Tracking inference time on CUDAExecutionProvider: 100%|██████████| 100/100 [00:00<00:00, 521.76it/s]\n",
-      "Warming up: 100%|██████████| 10/10 [00:00<00:00, 62.95it/s]\n",
-      "Tracking inference time on CPUExecutionProvider: 100%|██████████| 100/100 [00:01<00:00, 68.65it/s]\n",
-      "Warming up: 100%|██████████| 10/10 [00:00<00:00, 69.72it/s]\n",
-      "Tracking inference time on TensorrtExecutionProvider: 100%|██████████| 100/100 [00:01<00:00, 71.31it/s]\n",
-      "Warming up: 100%|██████████| 10/10 [00:00<00:00, 66.28it/s]\n",
-      "Tracking inference time on DnnlExecutionProvider: 100%|██████████| 100/100 [00:01<00:00, 72.03it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from torch.cuda import get_device_name\n",
-    "from contextlib import contextmanager\n",
-    "from dataclasses import dataclass\n",
-    "from time import time\n",
-    "from tqdm import trange\n",
-    "\n",
-    "print(f\"Doing GPU inference on {get_device_name(0)}\", flush=True)\n",
-    "\n",
-    "@contextmanager\n",
-    "def track_infer_time(buffer: [int]):\n",
-    "    start = time()\n",
-    "    yield\n",
-    "    end = time()\n",
-    "\n",
-    "    buffer.append(end - start)\n",
-    "\n",
-    "\n",
-    "@dataclass\n",
-    "class OnnxInferenceResult:\n",
-    "  model_inference_time: [int]  \n",
-    "  optimized_model_path: str\n",
-    "\n",
-    "\n",
-    "# All the providers we'll be using in the test\n",
-    "results = {}\n",
-    "providers = [\n",
-    "  \"CUDAExecutionProvider\",\n",
-    "  \"CPUExecutionProvider\",            \n",
-    "  \"TensorrtExecutionProvider\",\n",
-    "  \"DnnlExecutionProvider\",          \n",
-    "]\n",
-    "\n",
-    "# Iterate over all the providers\n",
-    "for provider in providers:\n",
-    "\n",
-    "  # Create the model with the specified provider\n",
-    "  model = create_model_for_provider(\"onnx/bert-base-cased.onnx\", provider)\n",
-    "\n",
-    "  # Keep track of the inference time\n",
-    "  time_buffer = []\n",
-    "\n",
-    "  # Warm up the model\n",
-    "  for _ in trange(10, desc=\"Warming up\"):\n",
-    "    model.run(None, inputs_onnx)\n",
-    "\n",
-    "  # Compute \n",
-    "  for _ in trange(100, desc=f\"Tracking inference time on {provider}\"):\n",
-    "    with track_infer_time(time_buffer):\n",
-    "      model.run(None, inputs_onnx)\n",
-    "\n",
-    "  # Store the result\n",
-    "  results[provider] = OnnxInferenceResult(\n",
-    "      time_buffer,\n",
-    "      model.get_session_options().optimized_model_filepath\n",
-    "  )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 51
-    },
-    "colab_type": "code",
-    "id": "PS_49goe197g",
-    "outputId": "0ef0f70c-f5a7-46a0-949a-1a93f231d193"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Warming up: 100%|██████████| 10/10 [00:00<00:00, 18.04it/s]\n",
-      "Tracking inference time on PyTorch: 100%|██████████| 100/100 [00:05<00:00, 18.88it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from transformers import BertModel\n",
-    "\n",
-    "# Add PyTorch to the providers\n",
-    "model_pt = BertModel.from_pretrained(\"bert-base-cased\")\n",
-    "for _ in trange(10, desc=\"Warming up\"):\n",
-    "  model_pt(**model_inputs)\n",
-    "\n",
-    "# Compute \n",
-    "time_buffer = []\n",
-    "for _ in trange(100, desc=f\"Tracking inference time on PyTorch\"):\n",
-    "  with track_infer_time(time_buffer):\n",
-    "    model_pt(**model_inputs)\n",
-    "\n",
-    "# Store the result\n",
-    "results[\"Pytorch\"] = OnnxInferenceResult(\n",
-    "    time_buffer, \n",
-    "    model.get_session_options().optimized_model_filepath\n",
-    ") "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Show the inference performance of each providers \n",
-    "\n",
-    "_Note: PyTorch model benchmark is run on CPU_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 676
-    },
-    "colab_type": "code",
-    "id": "dj-rS8AcqRZQ",
-    "outputId": "b4bf07d1-a7b4-4eff-e6bd-d5d424fd17fb"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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 truncated
-      "text/plain": [
-       "<Figure size 1600x1200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import os\n",
-    "\n",
-    "# Compute average inference time + std\n",
-    "time_results = {k: np.mean(v.model_inference_time) * 1e3 for k, v in results.items()}\n",
-    "time_results_std = np.std([v.model_inference_time for v in results.values()]) * 1000\n",
-    "\n",
-    "plt.rcdefaults()\n",
-    "fig, ax = plt.subplots(figsize=(16, 12))\n",
-    "ax.set_ylabel(\"Avg Inference time (ms)\")\n",
-    "ax.set_title(\"Average inference time (ms) for each provider\")\n",
-    "ax.bar(time_results.keys(), time_results.values(), yerr=time_results_std)\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "collapsed_sections": [],
-   "name": "ONNX Overview",
-   "provenance": [],
-   "toc_visible": true
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}

notebooks/README.md

@@ -4,25 +4,15 @@ You can find here a list of the official notebooks provided by Hugging Face.
 
 Also, we would like to list here interesting content created by the community. 
 If you wrote some notebook(s) leveraging transformers and would like be listed here, please open a 
-Pull Request so it can be included under the Community notebooks. 
+Pull Request and we'll review it so it can be included here. 
 
 
 ## Hugging Face's notebooks :hugs:
 
 | Notebook     |      Description      |   |
-|:----------|:-------------|------:|
+|:----------|:-------------:|------:|
 | [Getting Started Tokenizers](https://github.com/huggingface/transformers/blob/master/notebooks/01-training-tokenizers.ipynb)  | How to train and use your very own tokenizer  |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/transformers/blob/master/notebooks/01-training-tokenizers.ipynb) |
 | [Getting Started Transformers](https://github.com/huggingface/transformers/blob/master/notebooks/02-transformers.ipynb)   | How to easily start using transformers  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/transformers/blob/master/notebooks/02-transformers.ipynb) |
 | [How to use Pipelines](https://github.com/huggingface/transformers/blob/master/notebooks/03-pipelines.ipynb)  | Simple and efficient way to use State-of-the-Art models on downstream tasks through transformers | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/transformers/blob/master/notebooks/03-pipelines.ipynb) |
 | [How to train a language model](https://github.com/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)| Highlight all the steps to effectively train Transformer model on custom data | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)|
 | [How to generate text](https://github.com/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)| How to use different decoding methods for language generation with transformers | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)|
-| [How to export model to ONNX](https://github.com/huggingface/transformers/blob/master/notebooks/04-onnx-export.ipynb) | Highlight how to export and run inference workloads through ONNX |
-
-
-## Community notebooks:
-
-| Notebook     |      Description      |      Author      |      |
-|:----------|:-------------|:-------------|------:|
-| [Train T5 on TPU](https://github.com/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb)  | How to train T5 on SQUAD with Transformers and Nlp | [Suraj Patil](https://github.com/patil-suraj) |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb#scrollTo=QLGiFCDqvuil) |
-| [Fine-tune T5 for Classification and Multiple Choice](https://github.com/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb)  | How to fine-tune T5 for classification and multiple choice tasks using a text-to-text format with PyTorch Lightning |  [Suraj Patil](https://github.com/patil-suraj) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb) |
-| [Fine-tune DialoGPT on New Datasets and Languages](https://github.com/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb)  | How to fine-tune the DialoGPT model on a new dataset for open-dialog conversational chatbots |  [Nathan Cooper](https://github.com/ncoop57) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb)

setup.cfg

@@ -36,5 +36,5 @@ multi_line_output = 3
 use_parentheses = True
 
 [flake8]
-ignore = E203, E501, E741, W503
+ignore = E203, E501, W503
 max-line-length = 119

setup.py

@@ -67,18 +67,8 @@ extras = {}
 
 extras["mecab"] = ["mecab-python3"]
 extras["sklearn"] = ["scikit-learn"]
-
-# keras2onnx and onnxconverter-common version is specific through a commit until 1.7.0 lands on pypi
-extras["tf"] = [
-    "tensorflow",
-    "onnxconverter-common",
-    "keras2onnx"
-]
-extras["tf-cpu"] = [
-    "tensorflow-cpu",
-    "onnxconverter-common",
-    "keras2onnx"
-]
+extras["tf"] = ["tensorflow<=2.1.0"]
+extras["tf-cpu"] = ["tensorflow-cpu<=2.1.0"]
 extras["torch"] = ["torch"]
 
 extras["serving"] = ["pydantic", "uvicorn", "fastapi", "starlette"]
@@ -88,15 +78,15 @@ extras["testing"] = ["pytest", "pytest-xdist", "timeout-decorator"]
 extras["docs"] = ["recommonmark", "sphinx", "sphinx-markdown-tables", "sphinx-rtd-theme"]
 extras["quality"] = [
     "black",
-    "isort",
+    "isort @ git+git://github.com/timothycrosley/isort.git@e63ae06ec7d70b06df9e528357650281a3d3ec22#egg=isort",
     "flake8",
 ]
-extras["dev"] = extras["testing"] + extras["quality"] + ["mecab-python3", "scikit-learn", "tensorflow", "torch"]
+extras["dev"] = extras["testing"] + extras["quality"] + ["mecab-python3", "scikit-learn", "tensorflow<=2.1.0", "torch"]
 
 setup(
     name="transformers",
-    version="2.10.0",
-    author="Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Sam Shleifer, Patrick von Platen, Google AI Language Team Authors, Open AI team Authors, Facebook AI Authors, Carnegie Mellon University Authors",
+    version="2.9.0",
+    author="Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Sam Shleifer, Google AI Language Team Authors, Open AI team Authors, Facebook AI Authors, Carnegie Mellon University Authors",
     author_email="thomas@huggingface.co",
     description="State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch",
     long_description=open("README.md", "r", encoding="utf-8").read(),

src/transformers/__init__.py

@@ -2,7 +2,7 @@
 # There's no way to ignore "F401 '...' imported but unused" warnings in this
 # module, but to preserve other warnings. So, don't check this module at all.
 
-__version__ = "2.10.0"
+__version__ = "2.9.0"
 
 # Work around to update TensorFlow's absl.logging threshold which alters the
 # default Python logging output behavior when present.
@@ -44,7 +44,6 @@ from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, Electr
 from .configuration_encoder_decoder import EncoderDecoderConfig
 from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig
 from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config
-from .configuration_longformer import LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, LongformerConfig
 from .configuration_marian import MarianConfig
 from .configuration_mmbt import MMBTConfig
 from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
@@ -139,7 +138,6 @@ from .tokenization_distilbert import DistilBertTokenizer, DistilBertTokenizerFas
 from .tokenization_electra import ElectraTokenizer, ElectraTokenizerFast
 from .tokenization_flaubert import FlaubertTokenizer
 from .tokenization_gpt2 import GPT2Tokenizer, GPT2TokenizerFast
-from .tokenization_longformer import LongformerTokenizer
 from .tokenization_openai import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
 from .tokenization_reformer import ReformerTokenizer
 from .tokenization_roberta import RobertaTokenizer, RobertaTokenizerFast
@@ -250,7 +248,7 @@ if is_torch_available():
         BART_PRETRAINED_MODEL_ARCHIVE_MAP,
     )
     from .modeling_marian import MarianMTModel
-    from .tokenization_marian import MarianTokenizer
+    from .tokenization_marian import MarianSentencePieceTokenizer
     from .modeling_roberta import (
         RobertaForMaskedLM,
         RobertaModel,
@@ -321,7 +319,6 @@ if is_torch_available():
         ElectraForMaskedLM,
         ElectraForTokenClassification,
         ElectraPreTrainedModel,
-        ElectraForSequenceClassification,
         ElectraModel,
         load_tf_weights_in_electra,
         ELECTRA_PRETRAINED_MODEL_ARCHIVE_MAP,
@@ -335,8 +332,6 @@ if is_torch_available():
         REFORMER_PRETRAINED_MODEL_ARCHIVE_MAP,
     )
 
-    from .modeling_longformer import LONGFORMER_PRETRAINED_MODEL_ARCHIVE_MAP, LongformerModel, LongformerForMaskedLM
-
     # Optimization
     from .optimization import (
         AdamW,
@@ -364,7 +359,6 @@ if is_tf_available():
     from .modeling_tf_auto import (
         TFAutoModel,
         TFAutoModelForPreTraining,
-        TFAutoModelForMultipleChoice,
         TFAutoModelForSequenceClassification,
         TFAutoModelForQuestionAnswering,
         TFAutoModelWithLMHead,
@@ -499,7 +493,6 @@ if is_tf_available():
         TFAlbertModel,
         TFAlbertForPreTraining,
         TFAlbertForMaskedLM,
-        TFAlbertForMultipleChoice,
         TFAlbertForSequenceClassification,
         TFAlbertForQuestionAnswering,
         TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,

src/transformers/activations.py

@@ -26,7 +26,7 @@ def gelu_new(x):
     """ Implementation of the gelu activation function currently in Google Bert repo (identical to OpenAI GPT).
         Also see https://arxiv.org/abs/1606.08415
     """
-    return 0.5 * x * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))
+    return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))
 
 
 if torch.__version__ < "1.4.0":
@@ -36,7 +36,7 @@ else:
 
 
 def gelu_fast(x):
-    return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 + 0.044715 * x * x)))
+    return 0.5 * x * (1 + torch.tanh(x * 0.7978845608 * (1 + 0.044715 * x * x)))
 
 
 ACT2FN = {

src/transformers/commands/convert.py

@@ -62,21 +62,7 @@ class ConvertCommand(BaseTransformersCLICommand):
         self._finetuning_task_name = finetuning_task_name
 
     def run(self):
-        if self._model_type == "albert":
-            try:
-                from transformers.convert_albert_original_tf_checkpoint_to_pytorch import (
-                    convert_tf_checkpoint_to_pytorch,
-                )
-            except ImportError:
-                msg = (
-                    "transformers can only be used from the commandline to convert TensorFlow models in PyTorch, "
-                    "In that case, it requires TensorFlow to be installed. Please see "
-                    "https://www.tensorflow.org/install/ for installation instructions."
-                )
-                raise ImportError(msg)
-
-            convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
-        elif self._model_type == "bert":
+        if self._model_type == "bert":
             try:
                 from transformers.convert_bert_original_tf_checkpoint_to_pytorch import (
                     convert_tf_checkpoint_to_pytorch,

src/transformers/configuration_auto.py

@@ -28,8 +28,6 @@ from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, Electr
 from .configuration_encoder_decoder import EncoderDecoderConfig
 from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig
 from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config
-from .configuration_longformer import LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, LongformerConfig
-from .configuration_marian import MarianConfig
 from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
 from .configuration_reformer import ReformerConfig
 from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig
@@ -63,7 +61,6 @@ ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = dict(
         XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
         FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
-        LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ]
     for key, value, in pretrained_map.items()
 )
@@ -76,10 +73,8 @@ CONFIG_MAPPING = OrderedDict(
         ("albert", AlbertConfig,),
         ("camembert", CamembertConfig,),
         ("xlm-roberta", XLMRobertaConfig,),
-        ("marian", MarianConfig,),
         ("bart", BartConfig,),
         ("reformer", ReformerConfig,),
-        ("longformer", LongformerConfig,),
         ("roberta", RobertaConfig,),
         ("flaubert", FlaubertConfig,),
         ("bert", BertConfig,),
@@ -136,7 +131,6 @@ class AutoConfig:
             - contains `albert`: :class:`~transformers.AlbertConfig` (ALBERT model)
             - contains `camembert`: :class:`~transformers.CamembertConfig` (CamemBERT model)
             - contains `xlm-roberta`: :class:`~transformers.XLMRobertaConfig` (XLM-RoBERTa model)
-            - contains `longformer`: :class:`~transformers.LongformerConfig` (Longformer model)
             - contains `roberta`: :class:`~transformers.RobertaConfig` (RoBERTa model)
             - contains `reformer`: :class:`~transformers.ReformerConfig` (Reformer model)
             - contains `bert`: :class:`~transformers.BertConfig` (Bert model)
@@ -149,6 +143,7 @@ class AutoConfig:
             - contains `flaubert` : :class:`~transformers.FlaubertConfig` (Flaubert model)
             - contains `electra` : :class:`~transformers.ElectraConfig` (ELECTRA model)
 
+
         Args:
             pretrained_model_name_or_path (:obj:`string`):
                 Is either: \

src/transformers/configuration_longformer.py

@@ -1,69 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The Allen Institute for AI team and The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# 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.
-""" Longformer configuration """
-
-import logging
-from typing import List, Union
-
-from .configuration_roberta import RobertaConfig
-
-
-logger = logging.getLogger(__name__)
-
-LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "longformer-base-4096": "https://s3.amazonaws.com/models.huggingface.co/bert/allenai/longformer-base-4096/config.json",
-    "longformer-large-4096": "https://s3.amazonaws.com/models.huggingface.co/bert/allenai/longformer-large-4096/config.json",
-}
-
-
-class LongformerConfig(RobertaConfig):
-    r"""
-        This is the configuration class to store the configuration of an :class:`~transformers.LongformerModel`.
-        It is used to instantiate an Longformer model according to the specified arguments, defining the model
-        architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
-        the RoBERTa `roberta-base <https://huggingface.co/roberta-base>`__ architecture with a sequence length 4,096.
-
-        The :class:`~transformers.LongformerConfig` class directly inherits :class:`~transformers.RobertaConfig`.
-        It reuses the same defaults. Please check the parent class for more information.
-
-        Args:
-            attention_window (:obj:`int` or :obj:`List[int]`, optional, defaults to 512):
-                Size of an attention window around each token. If :obj:`int`, use the same size for all layers.
-                To specify a different window size for each layer, use a :obj:`List[int]` where
-                ``len(attention_window) == num_hidden_layers``.
-
-        Example::
-
-            from transformers import LongformerConfig, LongformerModel
-
-            # Initializing a Longformer configuration
-            configuration = LongformerConfig()
-
-            # Initializing a model from the configuration
-            model = LongformerModel(configuration)
-
-            # Accessing the model configuration
-            configuration = model.config
-
-        Attributes:
-            pretrained_config_archive_map (Dict[str, str]):
-                A dictionary containing all the available pre-trained checkpoints.
-    """
-    pretrained_config_archive_map = LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP
-    model_type = "longformer"
-
-    def __init__(self, attention_window: Union[List[int], int] = 512, **kwargs):
-        super().__init__(**kwargs)
-        self.attention_window = attention_window

src/transformers/configuration_marian.py

@@ -23,5 +23,4 @@ PRETRAINED_CONFIG_ARCHIVE_MAP = {
 
 
 class MarianConfig(BartConfig):
-    model_type = "marian"
     pretrained_config_archive_map = PRETRAINED_CONFIG_ARCHIVE_MAP

src/transformers/configuration_reformer.py

@@ -24,8 +24,7 @@ from .configuration_utils import PretrainedConfig
 logger = logging.getLogger(__name__)
 
 REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "google/reformer-crime-and-punishment": "https://cdn.huggingface.co/google/reformer-crime-and-punishment/config.json",
-    "google/reformer-enwik8": "https://cdn.huggingface.co/google/reformer-enwik8/config.json",
+    "google/reformer-crime-and-punishment": "https://cdn.huggingface.co/google/reformer-crime-and-punishment/config.json"
 }
 
 

src/transformers/configuration_roberta.py

@@ -68,6 +68,6 @@ class RobertaConfig(BertConfig):
     model_type = "roberta"
 
     def __init__(self, pad_token_id=1, bos_token_id=0, eos_token_id=2, **kwargs):
-        """Constructs RobertaConfig.
+        """Constructs FlaubertConfig.
         """
         super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)

src/transformers/configuration_t5.py

@@ -39,10 +39,10 @@ class T5Config(PretrainedConfig):
 
         Arguments:
             vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `T5Model`.
-            d_model: Size of the encoder layers and the pooler layer. `d_model` can also accesed via the property `hidden_size`.
-            num_layers: Number of hidden layers in the Transformer encoder. `num_layers` can also be accessed via the property `num_hidden_layers`.
-            num_heads: Number of attention heads for each attention layer in
-                the Transformer encoder. `num_heads` can also be accessed via the property `num_attention_heads`.
+            hidden_size: Size of the encoder layers and the pooler layer.
+            num_hidden_layers: Number of hidden layers in the Transformer encoder.
+            num_attention_heads: Number of attention heads for each attention layer in
+                the Transformer encoder.
             intermediate_size: The size of the "intermediate" (i.e., feed-forward)
                 layer in the Transformer encoder.
             hidden_act: The non-linear activation function (function or string) in the
@@ -51,9 +51,9 @@ class T5Config(PretrainedConfig):
                 layers in the embeddings, encoder, and pooler.
             attention_probs_dropout_prob: The dropout ratio for the attention
                 probabilities.
-            n_positions: The maximum sequence length that this model might
+            max_position_embeddings: The maximum sequence length that this model might
                 ever be used with. Typically set this to something large just in case
-                (e.g., 512 or 1024 or 2048). `n_positions` can also be accessed via the property `max_position_embeddings'.
+                (e.g., 512 or 1024 or 2048).
             type_vocab_size: The vocabulary size of the `token_type_ids` passed into
                 `T5Model`.
             initializer_factor: A factor for initializing all weight matrices (should be kept to 1.0, used for initialization testing).

src/transformers/convert_graph_to_onnx.py

@@ -1,11 +1,10 @@
-from argparse import ArgumentParser
-from itertools import takewhile
-from os import listdir, makedirs
-from os.path import abspath, dirname, exists
-from typing import Dict, List, Optional, Tuple
+from argparse import ArgumentParser, Namespace
+from os import mkdir, listdir
+from os.path import exists, abspath, dirname
+from typing import Dict, Tuple, List
 
-from transformers import is_tf_available, is_torch_available
-from transformers.pipelines import Pipeline, pipeline
+from transformers import is_torch_available, is_tf_available
+from transformers.pipelines import SUPPORTED_TASKS, pipeline, Pipeline
 from transformers.tokenization_utils import BatchEncoding
 
 
@@ -15,100 +14,70 @@ class OnnxConverterArgumentParser(ArgumentParser):
     """
 
     def __init__(self):
-        super(OnnxConverterArgumentParser, self).__init__("ONNX Converter")
+        super().__init__("ONNX Converter")
 
         self.add_argument("--model", type=str, required=True, help="Model's id or path (ex: bert-base-cased)")
         self.add_argument("--tokenizer", type=str, help="Tokenizer's id or path (ex: bert-base-cased)")
+        self.add_argument("--task", type=str, default=None, choices=list(SUPPORTED_TASKS.keys()), help="Model's task")
         self.add_argument("--framework", type=str, choices=["pt", "tf"], help="Framework for loading the model")
-        self.add_argument("--opset", type=int, default=11, help="ONNX opset to use")
+        self.add_argument("--opset", type=int, default=-1, help="ONNX opset to use (-1 = latest)")
         self.add_argument("--check-loading", action="store_true", help="Check ONNX is able to load the model")
-        self.add_argument("--use-external-format", action="store_true", help="Allow exporting model >= than 2Gb")
         self.add_argument("output")
 
 
-def ensure_valid_input(model, tokens, input_names):
-    """
-    Ensure input are presented in the correct order, without any None
-    Args:
-        model: The model used to forward the input data
-        tokens: BatchEncoding holding the input data
-        input_names: The name of the inputs
-
-    Returns: Tuple
-
-    """
-    model_args_name = model.forward.__code__.co_varnames
-    model_args_pos = [(model_args_name.index(name) - 1, name) for name in input_names]
-    model_args = [None] * (max(map(lambda x: x[0], model_args_pos)) + 1)
-
-    for arg_pos, arg_name in model_args_pos:
-        model_args[arg_pos] = tokens[arg_name]
-
-    model_args = tuple(model_args)  # Need to be ordered
-    return tuple(takewhile(lambda arg: arg is not None, model_args))
-
-
 def infer_shapes(nlp: Pipeline, framework: str) -> Tuple[List[str], List[str], Dict, BatchEncoding]:
     def build_shape_dict(tensor, is_input: bool, seq_len: int):
-        if isinstance(tensor, (tuple, list)):
-            return [build_shape_dict(t, is_input, seq_len) for t in tensor]
-
-        else:
-            # Let's assume batch is the first axis with only 1 element (~~ might not be always true ...)
-            axes = {[axis for axis, numel in enumerate(tensor.shape) if numel == 1][0]: "batch"}
-            if is_input:
-                if len(tensor.shape) == 2:
-                    axes[1] = "sequence"
-                else:
-                    raise ValueError("Unable to infer tensor axes ({})".format(len(tensor.shape)))
+        axes = {0: "batch"}
+        if is_input:
+            if len(tensor.shape) == 2:
+                axes[1] = "sequence"
             else:
-                seq_axes = [dim for dim, shape in enumerate(tensor.shape) if shape == seq_len]
-                axes.update({dim: "sequence" for dim in seq_axes})
+                raise ValueError("Unable to infer tensor axes ({})".format(len(tensor.shape)))
+        else:
+            seq_axes = [dim for dim, shape in enumerate(tensor.shape) if shape == seq_len]
+            axes.update({dim: "sequence" for dim in seq_axes})
 
         return axes
 
     tokens = nlp.tokenizer.encode_plus("This is a sample output", return_tensors=framework)
     seq_len = tokens.input_ids.shape[-1]
-    outputs = nlp.model(**tokens) if framework == "pt" else nlp.model(tokens)
+    outputs = nlp.model(**tokens) if args.framework == "pt" else nlp.model(tokens)
 
     if not isinstance(outputs, (list, tuple)):
         outputs = (outputs,)
 
-    # Generate input names & axes
+    # Generate names
+    output_names = ["output_{}".format(i) for i in range(len(outputs))]
     input_vars = list(tokens.keys())
+
+    # Define dynamic axes
     input_dynamic_axes = {k: build_shape_dict(v, True, seq_len) for k, v in tokens.items()}
-
-    # flatten potentially grouped outputs (past for gpt2, attentions)
-    outputs_flat = []
-    for output in outputs:
-        if isinstance(output, (tuple, list)):
-            outputs_flat.extend(output)
-        else:
-            outputs_flat.append(output)
-
-    # Generate output names & axes
-    output_names = ["output_{}".format(i) for i in range(len(outputs_flat))]
-    output_dynamic_axes = {k: build_shape_dict(v, False, seq_len) for k, v in zip(output_names, outputs_flat)}
-
-    # Create the aggregated axes representation
+    output_dynamic_axes = {k: build_shape_dict(v, False, seq_len) for k, v in zip(output_names, outputs)}
     dynamic_axes = dict(input_dynamic_axes, **output_dynamic_axes)
     return input_vars, output_names, dynamic_axes, tokens
 
 
-def load_graph_from_args(framework: str, model: str, tokenizer: Optional[str] = None) -> Pipeline:
+def load_graph_from_args(args: Namespace) -> Pipeline:
     # If no tokenizer provided
-    if tokenizer is None:
-        tokenizer = model
+    if args.tokenizer is None:
+        args.tokenizer = args.model
 
-    print("Loading pipeline (model: {}, tokenizer: {})".format(model, tokenizer))
+    print("Loading pipeline (task: {}, model: {}, tokenizer: {})".format(args.task, args.model, args.tokenizer))
+
+    if args.opset == -1:
+        from onnx.defs import onnx_opset_version
+
+        print("Setting ONNX opset version to: {}".format(onnx_opset_version()))
+        args.opset = onnx_opset_version()
 
     # Allocate tokenizer and model
-    return pipeline("feature-extraction", model=model, tokenizer=tokenizer, framework=framework)
+    return pipeline(args.task, model=args.model, framework=args.framework)
 
 
-def convert_pytorch(nlp: Pipeline, opset: int, output: str, use_external_format: bool):
+def export_pytorch(nlp: Pipeline, args: Namespace):
     if not is_torch_available():
-        raise Exception("Cannot convert because PyTorch is not installed. Please install torch first.")
+        print("Cannot export {} because PyTorch is not installed. Please install torch first.".format(args.model))
+        exit(1)
 
     import torch
     from torch.onnx import export
@@ -116,105 +85,81 @@ def convert_pytorch(nlp: Pipeline, opset: int, output: str, use_external_format:
     print("PyTorch: {}".format(torch.__version__))
 
     with torch.no_grad():
-        input_names, output_names, dynamic_axes, tokens = infer_shapes(nlp, "pt")
-        model_args = ensure_valid_input(nlp.model, tokens, input_names)
-
+        input_names, output_names, dynamic_axes, tokens = infer_shapes(nlp, args.framework)
+        tokens = tuple(tokens[key] for key in input_names)  # Need to be ordered
         export(
             nlp.model,
-            model_args,
-            f=output,
+            tokens,
+            f=args.output,
             input_names=input_names,
             output_names=output_names,
             dynamic_axes=dynamic_axes,
-            do_constant_folding=True,
-            use_external_data_format=use_external_format,
+            do_constant_folding=False,
+            use_external_data_format=True,
             enable_onnx_checker=True,
-            opset_version=opset,
         )
 
 
-def convert_tensorflow(nlp: Pipeline, opset: int, output: str):
+def export_tensorflow(nlp: Pipeline, args: Namespace):
     if not is_tf_available():
-        raise Exception(
-            "Cannot convert {} because TF is not installed. Please install torch first.".format(args.model)
-        )
+        print("Cannot export {} because TF is not installed. Please install torch first.".format(args.model))
+        exit(1)
 
-    print("/!\\ Please note TensorFlow doesn't support exporting model > 2Gb /!\\")
+    print("Please note TensorFlow doesn't support exporting model > 2Gb")
 
     try:
         import tensorflow as tf
-        from keras2onnx import convert_keras, save_model, __version__ as k2ov
+        from keras2onnx import convert_keras, save_model, build_io_names_tf2onnx, __version__ as k2ov
 
         print("TensorFlow: {}, keras2onnx: {}".format(tf.version.VERSION, k2ov))
 
         # Build
-        input_names, output_names, dynamic_axes, tokens = infer_shapes(nlp, "tf")
+        input_names, output_names, dynamic_axes, tokens = infer_shapes(nlp, args.framework)
 
         # Forward
-        nlp.model.predict(tokens.data)
-        onnx_model = convert_keras(nlp.model, nlp.model.name, target_opset=opset)
-        save_model(onnx_model, output)
+        nlp.model.predict(list(tokens.data.values()))
+        onnx_model = convert_keras(nlp.model, nlp.model.name)
+        save_model(onnx_model, args.output)
 
     except ImportError as e:
-        raise Exception(
-            "Cannot import {} required to convert TF model to ONNX. Please install {} first.".format(e.name, e.name)
-        )
-
-
-def convert(
-    framework: str,
-    model: str,
-    output: str,
-    opset: int,
-    tokenizer: Optional[str] = None,
-    use_external_format: bool = False,
-):
-    print("ONNX opset version set to: {}".format(opset))
-
-    # Load the pipeline
-    nlp = load_graph_from_args(framework, model, tokenizer)
-
-    parent = dirname(output)
-    if not exists(parent):
-        print("Creating folder {}".format(parent))
-        makedirs(parent)
-    elif len(listdir(parent)) > 0:
-        raise Exception("Folder {} is not empty, aborting conversion".format(parent))
-
-    # Export the graph
-    if framework == "pt":
-        convert_pytorch(nlp, opset, output, use_external_format)
-    else:
-        convert_tensorflow(nlp, opset, output)
-
-
-def verify(path: str):
-    from onnxruntime import InferenceSession, SessionOptions
-    from onnxruntime.capi.onnxruntime_pybind11_state import RuntimeException
-
-    print("Checking ONNX model loading from: {}".format(path))
-    try:
-        onnx_options = SessionOptions()
-        _ = InferenceSession(path, onnx_options, providers=["CPUExecutionProvider"])
-        print("Model correctly loaded")
-    except RuntimeException as re:
-        print("Error while loading the model: {}".format(re))
+        print("Cannot import {} required to export TF model to ONNX. Please install {} first.".format(e.name, e.name))
+        exit(1)
 
 
 if __name__ == "__main__":
     parser = OnnxConverterArgumentParser()
     args = parser.parse_args()
 
-    # Make sure output is absolute path
+    # Ensure we have an absolute path for the output
     args.output = abspath(args.output)
 
-    try:
-        # Convert
-        convert(args.framework, args.model, args.output, args.opset, args.tokenizer, args.use_external_format)
+    # Create export folder if needed
+    if exists(dirname(args.output)) and len(listdir(dirname(args.output))) > 0:
+        raise ValueError("Folder {} already exists".format(args.output))
+    elif not exists(dirname(args.output)):
+        print("Creating folder {}".format(dirname(args.output)))
+        mkdir(dirname(args.output))
+    else:
+        print("Folder {} already exists and is empty: {}".format(dirname(args.output), "\u2713"))
 
-        # And verify
-        if args.check_loading:
-            verify(args.output)
-    except Exception as e:
-        print("Error while converting the model: {}".format(e))
-        exit(1)
+    # Load the pipeline
+    nlp = load_graph_from_args(args)
+
+    # Export the graph
+    if args.framework == "pt":
+        export_pytorch(nlp, args)
+    else:
+        export_tensorflow(nlp, args)
+
+    if args.check_loading:
+        from onnxruntime import InferenceSession, SessionOptions, GraphOptimizationLevel
+        from onnxruntime.capi.onnxruntime_pybind11_state import RuntimeException
+
+        print("Checking ONNX model loading from: {}".format(args.output))
+        try:
+            onnx_options = SessionOptions()
+            onnx_options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL
+            session = InferenceSession(args.output, onnx_options, providers=["CPUExecutionProvider"])
+            print("Model correctly loaded")
+        except RuntimeException as re:
+            print("Error while loading the model: {}".format(re))

src/transformers/convert_marian_to_pytorch.py

@@ -11,8 +11,7 @@ import numpy as np
 import torch
 from tqdm import tqdm
 
-from transformers import MarianConfig, MarianMTModel, MarianTokenizer
-from transformers.hf_api import HfApi
+from transformers import MarianConfig, MarianMTModel, MarianSentencePieceTokenizer
 
 
 def remove_prefix(text: str, prefix: str):
@@ -39,19 +38,6 @@ def load_layers_(layer_lst: torch.nn.ModuleList, opus_state: dict, converter, is
         layer.load_state_dict(sd, strict=True)
 
 
-def find_pretrained_model(src_lang: str, tgt_lang: str) -> List[str]:
-    """Find models that can accept src_lang as input and return tgt_lang as output."""
-    prefix = "Helsinki-NLP/opus-mt-"
-    api = HfApi()
-    model_list = api.model_list()
-    model_ids = [x.modelId for x in model_list if x.modelId.startswith("Helsinki-NLP")]
-    src_and_targ = [
-        remove_prefix(m, prefix).lower().split("-") for m in model_ids if "+" not in m
-    ]  # + cant be loaded.
-    matching = [f"{prefix}{a}-{b}" for (a, b) in src_and_targ if src_lang in a and tgt_lang in b]
-    return matching
-
-
 def add_emb_entries(wemb, final_bias, n_special_tokens=1):
     vsize, d_model = wemb.shape
     embs_to_add = np.zeros((n_special_tokens, d_model))
@@ -95,103 +81,7 @@ def find_model_file(dest_dir):  # this one better
     return model_file
 
 
-# Group Names Logic: change long opus model names to something shorter, like opus-mt-en-ROMANCE
-ROM_GROUP = "fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la"
-GROUPS = [
-    ("cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh", "ZH"),
-    (ROM_GROUP, "ROMANCE"),
-    ("de+nl+fy+af+da+fo+is+no+nb+nn+sv", "NORTH_EU"),
-    ("da+fo+is+no+nb+nn+sv", "SCANDINAVIA"),
-    ("se+sma+smj+smn+sms", "SAMI"),
-    ("nb_NO+nb+nn_NO+nn+nog+no_nb+no", "NORWAY"),
-    ("ga+cy+br+gd+kw+gv", "CELTIC"),  # https://en.wikipedia.org/wiki/Insular_Celtic_languages
-]
-GROUP_TO_OPUS_NAME = {
-    "opus-mt-ZH-de": "cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh-de",
-    "opus-mt-ZH-fi": "cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh-fi",
-    "opus-mt-ZH-sv": "cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh-sv",
-    "opus-mt-SCANDINAVIA-SCANDINAVIA": "da+fo+is+no+nb+nn+sv-da+fo+is+no+nb+nn+sv",
-    "opus-mt-NORTH_EU-NORTH_EU": "de+nl+fy+af+da+fo+is+no+nb+nn+sv-de+nl+fy+af+da+fo+is+no+nb+nn+sv",
-    "opus-mt-de-ZH": "de-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh",
-    "opus-mt-en_el_es_fi-en_el_es_fi": "en+el+es+fi-en+el+es+fi",
-    "opus-mt-en-ROMANCE": "en-fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO"
-    "+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR"
-    "+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la",
-    "opus-mt-en-CELTIC": "en-ga+cy+br+gd+kw+gv",
-    "opus-mt-es-NORWAY": "es-nb_NO+nb+nn_NO+nn+nog+no_nb+no",
-    "opus-mt-fi_nb_no_nn_ru_sv_en-SAMI": "fi+nb+no+nn+ru+sv+en-se+sma+smj+smn+sms",
-    "opus-mt-fi-ZH": "fi-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh",
-    "opus-mt-fi-NORWAY": "fi-nb_NO+nb+nn_NO+nn+nog+no_nb+no",
-    "opus-mt-ROMANCE-en": "fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO"
-    "+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR"
-    "+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la-en",
-    "opus-mt-CELTIC-en": "ga+cy+br+gd+kw+gv-en",
-    "opus-mt-sv-ZH": "sv-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh",
-    "opus-mt-sv-NORWAY": "sv-nb_NO+nb+nn_NO+nn+nog+no_nb+no",
-}
-OPUS_GITHUB_URL = "https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/"
-ORG_NAME = "Helsinki-NLP/"
-
-
-def convert_opus_name_to_hf_name(x):
-    for substr, grp_name in GROUPS:
-        x = x.replace(substr, grp_name)
-    return x.replace("+", "_")
-
-
-def convert_hf_name_to_opus_name(hf_model_name):
-    """Relies on the assumption that there are no language codes like pt_br in models that are not in GROUP_TO_OPUS_NAME."""
-    hf_model_name = remove_prefix(hf_model_name, ORG_NAME)
-    if hf_model_name in GROUP_TO_OPUS_NAME:
-        opus_w_prefix = GROUP_TO_OPUS_NAME[hf_model_name]
-    else:
-        opus_w_prefix = hf_model_name.replace("_", "+")
-    return remove_prefix(opus_w_prefix, "opus-mt-")
-
-
-def write_model_card(
-    hf_model_name: str,
-    repo_path="OPUS-MT-train/models/",
-    dry_run=False,
-    model_card_dir=Path("marian_converted/model_cards/Helsinki-NLP/"),
-) -> str:
-    """Copy the most recent model's readme section from opus, and add metadata.
-    upload command: s3cmd sync --recursive model_card_dir s3://models.huggingface.co/bert/Helsinki-NLP/
-    """
-    hf_model_name = remove_prefix(hf_model_name, ORG_NAME)
-    opus_name: str = convert_hf_name_to_opus_name(hf_model_name)
-    opus_src, opus_tgt = [x.split("+") for x in opus_name.split("-")]
-    readme_url = OPUS_GITHUB_URL + f"{opus_name}/README.md"
-    s, t = ",".join(opus_src), ",".join(opus_tgt)
-    extra_markdown = f"### {hf_model_name}\n\n* source languages: {s}\n* target languages: {t}\n*  OPUS readme: [{opus_name}]({readme_url})\n"
-    # combine with opus markdown
-    opus_readme_path = Path(f"{repo_path}{opus_name}/README.md")
-    assert opus_readme_path.exists(), opus_readme_path
-    content = opus_readme_path.open().read()
-    content = content.split("\n# ")[-1]  # Get the lowest level 1 header in the README -- the most recent model.
-    content = "*".join(content.split("*")[1:])
-    content = extra_markdown + "\n* " + content.replace("download", "download original weights")
-    if dry_run:
-        return content
-    # Save string to model_cards/hf_model_name/readme.md
-    model_card_dir.mkdir(exist_ok=True)
-    sub_dir = model_card_dir / hf_model_name
-    sub_dir.mkdir(exist_ok=True)
-    dest = sub_dir / "README.md"
-    dest.open("w").write(content)
-    return content
-
-
-def get_clean_model_id_mapping(multiling_model_ids):
-    return {x: convert_opus_name_to_hf_name(x) for x in multiling_model_ids}
-
-
-def make_registry(repo_path="Opus-MT-train/models"):
-    if not (Path(repo_path) / "fr-en" / "README.md").exists():
-        raise ValueError(
-            f"repo_path:{repo_path} does not exist: "
-            "You must run: git clone git@github.com:Helsinki-NLP/Opus-MT-train.git before calling."
-        )
+def parse_readmes(repo_path):
     results = {}
     for p in Path(repo_path).ls():
         n_dash = p.name.count("-")
@@ -200,48 +90,22 @@ def make_registry(repo_path="Opus-MT-train/models"):
         else:
             lns = list(open(p / "README.md").readlines())
             results[p.name] = _parse_readme(lns)
-    return [(k, v["pre-processing"], v["download"], v["download"][:-4] + ".test.txt") for k, v in results.items()]
+    return results
 
 
-def convert_all_sentencepiece_models(model_list=None, repo_path=None):
+def download_all_sentencepiece_models(repo_path="Opus-MT-train/models"):
     """Requires 300GB"""
     save_dir = Path("marian_ckpt")
-    dest_dir = Path("marian_converted")
-    dest_dir.mkdir(exist_ok=True)
-    if model_list is None:
-        model_list: list = make_registry(repo_path=repo_path)
-    for k, prepro, download, test_set_url in tqdm(model_list):
-        if "SentencePiece" not in prepro:  # dont convert BPE models.
+    if not Path(repo_path).exists():
+        raise ValueError("You must run: git clone git@github.com:Helsinki-NLP/Opus-MT-train.git")
+    results: dict = parse_readmes(repo_path)
+    for k, v in tqdm(list(results.items())):
+        if os.path.exists(save_dir / k):
+            print(f"already have path {k}")
             continue
-        if not os.path.exists(save_dir / k / "pytorch_model.bin"):
-            download_and_unzip(download, save_dir / k)
-        pair_name = convert_opus_name_to_hf_name(k)
-        convert(save_dir / k, dest_dir / f"opus-mt-{pair_name}")
-
-
-def lmap(f, x) -> List:
-    return list(map(f, x))
-
-
-def fetch_test_set(test_set_url):
-    import wget
-
-    fname = wget.download(test_set_url, "opus_test.txt")
-    lns = Path(fname).open().readlines()
-    src = lmap(str.strip, lns[::4])
-    gold = lmap(str.strip, lns[1::4])
-    mar_model = lmap(str.strip, lns[2::4])
-    assert len(gold) == len(mar_model) == len(src)
-    os.remove(fname)
-    return src, mar_model, gold
-
-
-def convert_whole_dir(path=Path("marian_ckpt/")):
-    for subdir in tqdm(list(path.ls())):
-        dest_dir = f"marian_converted/{subdir.name}"
-        if (dest_dir / "pytorch_model.bin").exists():
+        if "SentencePiece" not in v["pre-processing"]:
             continue
-        convert(source_dir, dest_dir)
+        download_and_unzip(v["download"], save_dir / k)
 
 
 def _parse_readme(lns):
@@ -267,7 +131,7 @@ def _parse_readme(lns):
     return subres
 
 
-def save_tokenizer_config(dest_dir: Path):
+def write_metadata(dest_dir: Path):
     dname = dest_dir.name.split("-")
     dct = dict(target_lang=dname[-1], source_lang="-".join(dname[:-1]))
     save_json(dct, dest_dir / "tokenizer_config.json")
@@ -284,17 +148,13 @@ def add_to_vocab_(vocab: Dict[str, int], special_tokens: List[str]):
     return added
 
 
-def find_vocab_file(model_dir):
-    return list(model_dir.glob("*vocab.yml"))[0]
-
-
 def add_special_tokens_to_vocab(model_dir: Path) -> None:
-    vocab = load_yaml(find_vocab_file(model_dir))
+    vocab = load_yaml(model_dir / "opus.spm32k-spm32k.vocab.yml")
     vocab = {k: int(v) for k, v in vocab.items()}
     num_added = add_to_vocab_(vocab, ["<pad>"])
     print(f"added {num_added} tokens to vocab")
     save_json(vocab, model_dir / "vocab.json")
-    save_tokenizer_config(model_dir)
+    write_metadata(model_dir)
 
 
 def save_tokenizer(self, save_directory):
@@ -391,6 +251,7 @@ class OpusState:
 
         # Process decoder.yml
         decoder_yml = cast_marian_config(load_yaml(source_dir / "decoder.yml"))
+        # TODO: what are normalize and word-penalty?
         check_marian_cfg_assumptions(cfg)
         self.hf_config = MarianConfig(
             vocab_size=cfg["vocab_size"],
@@ -412,9 +273,6 @@ class OpusState:
             dropout=0.1,  # see opus-mt-train repo/transformer-dropout param.
             # default: add_final_layer_norm=False,
             num_beams=decoder_yml["beam-size"],
-            decoder_start_token_id=self.pad_token_id,
-            bad_words_ids=[[self.pad_token_id]],
-            max_length=512,
         )
 
     def _check_layer_entries(self):
@@ -491,12 +349,12 @@ def download_and_unzip(url, dest_dir):
     os.remove(filename)
 
 
-def convert(source_dir: Path, dest_dir):
+def main(source_dir, dest_dir):
     dest_dir = Path(dest_dir)
     dest_dir.mkdir(exist_ok=True)
 
     add_special_tokens_to_vocab(source_dir)
-    tokenizer = MarianTokenizer.from_pretrained(str(source_dir))
+    tokenizer = MarianSentencePieceTokenizer.from_pretrained(str(source_dir))
     save_tokenizer(tokenizer, dest_dir)
 
     opus_state = OpusState(source_dir)
@@ -519,7 +377,7 @@ if __name__ == "__main__":
     source_dir = Path(args.src)
     assert source_dir.exists()
     dest_dir = f"converted-{source_dir.name}" if args.dest is None else args.dest
-    convert(source_dir, dest_dir)
+    main(source_dir, dest_dir)
 
 
 def load_yaml(path):

src/transformers/convert_reformer_trax_checkpoint_to_pytorch.py

@@ -93,7 +93,7 @@ def set_block_weights_in_torch(weights, torch_block, hidden_size):
         set_layer_weights_in_torch_local(attn_weights, torch_block.attention, hidden_size)
 
     # intermediate weighs
-    intermediate_weights = weights[2][0][1][2]
+    intermediate_weights = weights[2][0][2][2]
 
     # Chunked Feed Forward
     if len(intermediate_weights) == 4:
@@ -145,16 +145,19 @@ def set_model_weights_in_torch(weights, torch_model, hidden_size):
             position_embeddings.weights[emb_idx] = torch.nn.Parameter(torch.tensor(emb_weights))
 
     trax_layer_weights = weights[5]
-    assert len(torch_model_reformer.encoder.layers) * 4 == len(
+    assert len(torch_model_reformer.encoder.layers) * 4 + 1 == len(
         trax_layer_weights
     ), "HF and trax model do not have the same number of layers"
     for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers):
         block_weights = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
         set_block_weights_in_torch(block_weights, layer, hidden_size)
 
+    # output weights
+    out_weights = weights[6]
+
     # output layer norm
-    layer_norm_out_weight = np.asarray(weights[7][0])
-    layer_norm_out_bias = np.asarray(weights[7][1])
+    layer_norm_out_weight = np.asarray(out_weights[0][0])
+    layer_norm_out_bias = np.asarray(out_weights[0][1])
     set_param(
         torch_model_reformer.encoder.layer_norm,
         torch.tensor(layer_norm_out_weight),
@@ -162,8 +165,8 @@ def set_model_weights_in_torch(weights, torch_model, hidden_size):
     )
 
     # output embeddings
-    output_embed_weights = np.asarray(weights[9][0])
-    output_embed_bias = np.asarray(weights[9][1])
+    output_embed_weights = np.asarray(out_weights[2][0])
+    output_embed_bias = np.asarray(out_weights[2][1])
     set_param(
         torch_model.lm_head.decoder,
         torch.tensor(output_embed_weights).transpose(0, 1).contiguous(),

src/transformers/data/datasets/glue.py

@@ -2,8 +2,7 @@ import logging
 import os
 import time
 from dataclasses import dataclass, field
-from enum import Enum
-from typing import List, Optional, Union
+from typing import List, Optional
 
 import torch
 from filelock import FileLock
@@ -48,12 +47,6 @@ class GlueDataTrainingArguments:
         self.task_name = self.task_name.lower()
 
 
-class Split(Enum):
-    train = "train"
-    dev = "dev"
-    test = "test"
-
-
 class GlueDataset(Dataset):
     """
     This will be superseded by a framework-agnostic approach
@@ -69,21 +62,16 @@ class GlueDataset(Dataset):
         args: GlueDataTrainingArguments,
         tokenizer: PreTrainedTokenizer,
         limit_length: Optional[int] = None,
-        mode: Union[str, Split] = Split.train,
+        evaluate=False,
     ):
         self.args = args
-        self.processor = glue_processors[args.task_name]()
+        processor = glue_processors[args.task_name]()
         self.output_mode = glue_output_modes[args.task_name]
-        if isinstance(mode, str):
-            try:
-                mode = Split[mode]
-            except KeyError:
-                raise KeyError("mode is not a valid split name")
         # Load data features from cache or dataset file
         cached_features_file = os.path.join(
             args.data_dir,
             "cached_{}_{}_{}_{}".format(
-                mode.value, tokenizer.__class__.__name__, str(args.max_seq_length), args.task_name,
+                "dev" if evaluate else "train", tokenizer.__class__.__name__, str(args.max_seq_length), args.task_name,
             ),
         )
 
@@ -100,7 +88,7 @@ class GlueDataset(Dataset):
                 )
             else:
                 logger.info(f"Creating features from dataset file at {args.data_dir}")
-                label_list = self.processor.get_labels()
+                label_list = processor.get_labels()
                 if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__ in (
                     RobertaTokenizer,
                     RobertaTokenizerFast,
@@ -108,12 +96,11 @@ class GlueDataset(Dataset):
                 ):
                     # HACK(label indices are swapped in RoBERTa pretrained model)
                     label_list[1], label_list[2] = label_list[2], label_list[1]
-                if mode == Split.dev:
-                    examples = self.processor.get_dev_examples(args.data_dir)
-                elif mode == Split.test:
-                    examples = self.processor.get_test_examples(args.data_dir)
-                else:
-                    examples = self.processor.get_train_examples(args.data_dir)
+                examples = (
+                    processor.get_dev_examples(args.data_dir)
+                    if evaluate
+                    else processor.get_train_examples(args.data_dir)
+                )
                 if limit_length is not None:
                     examples = examples[:limit_length]
                 self.features = glue_convert_examples_to_features(
@@ -127,7 +114,7 @@ class GlueDataset(Dataset):
                 torch.save(self.features, cached_features_file)
                 # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                 logger.info(
-                    "Saving features into cached file %s [took %.3f s]", cached_features_file, time.time() - start
+                    f"Saving features into cached file %s [took %.3f s]", cached_features_file, time.time() - start
                 )
 
     def __len__(self):
@@ -135,6 +122,3 @@ class GlueDataset(Dataset):
 
     def __getitem__(self, i) -> InputFeatures:
         return self.features[i]
-
-    def get_labels(self):
-        return self.processor.get_labels()

src/transformers/data/datasets/language_modeling.py

@@ -4,10 +4,10 @@ import pickle
 import time
 
 import torch
-from filelock import FileLock
 from torch.utils.data.dataset import Dataset
 
 from ...tokenization_utils import PreTrainedTokenizer
+from ...trainer import torch_distributed_zero_first
 
 
 logger = logging.getLogger(__name__)
@@ -20,7 +20,7 @@ class TextDataset(Dataset):
     """
 
     def __init__(
-        self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int, overwrite_cache=False,
+        self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int, overwrite_cache=False, local_rank=-1,
     ):
         assert os.path.isfile(file_path)
 
@@ -31,10 +31,9 @@ class TextDataset(Dataset):
             directory, "cached_lm_{}_{}_{}".format(tokenizer.__class__.__name__, str(block_size), filename,),
         )
 
-        # Make sure only the first process in distributed training processes the dataset,
-        # and the others will use the cache.
-        lock_path = cached_features_file + ".lock"
-        with FileLock(lock_path):
+        with torch_distributed_zero_first(local_rank):
+            # Make sure only the first process in distributed training processes the dataset,
+            # and the others will use the cache.
 
             if os.path.exists(cached_features_file) and not overwrite_cache:
                 start = time.time()
@@ -65,7 +64,7 @@ class TextDataset(Dataset):
                 with open(cached_features_file, "wb") as handle:
                     pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)
                 logger.info(
-                    "Saving features into cached file %s [took %.3f s]", cached_features_file, time.time() - start
+                    f"Saving features into cached file %s [took %.3f s]", cached_features_file, time.time() - start
                 )
 
     def __len__(self):
@@ -81,7 +80,7 @@ class LineByLineTextDataset(Dataset):
     soon.
     """
 
-    def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int):
+    def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int, local_rank=-1):
         assert os.path.isfile(file_path)
         # Here, we do not cache the features, operating under the assumption
         # that we will soon use fast multithreaded tokenizers from the

src/transformers/data/processors/glue.py

@@ -126,9 +126,7 @@ def _glue_convert_examples_to_features(
 
     label_map = {label: i for i, label in enumerate(label_list)}
 
-    def label_from_example(example: InputExample) -> Union[int, float, None]:
-        if example.label is None:
-            return None
+    def label_from_example(example: InputExample) -> Union[int, float]:
         if output_mode == "classification":
             return label_map[example.label]
         elif output_mode == "regression":
@@ -182,16 +180,12 @@ class MrpcProcessor(DataProcessor):
         """See base class."""
         return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
 
-    def get_test_examples(self, data_dir):
-        """See base class."""
-        return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
-
     def get_labels(self):
         """See base class."""
         return ["0", "1"]
 
     def _create_examples(self, lines, set_type):
-        """Creates examples for the training, dev and test sets."""
+        """Creates examples for the training and dev sets."""
         examples = []
         for (i, line) in enumerate(lines):
             if i == 0:
@@ -199,7 +193,7 @@ class MrpcProcessor(DataProcessor):
             guid = "%s-%s" % (set_type, i)
             text_a = line[3]
             text_b = line[4]
-            label = None if set_type == "test" else line[0]
+            label = line[0]
             examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
         return examples
 
@@ -224,16 +218,12 @@ class MnliProcessor(DataProcessor):
         """See base class."""
         return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")), "dev_matched")
 
-    def get_test_examples(self, data_dir):
-        """See base class."""
-        return self._create_examples(self._read_tsv(os.path.join(data_dir, "test_matched.tsv")), "test_matched")
-
     def get_labels(self):
         """See base class."""
         return ["contradiction", "entailment", "neutral"]
 
     def _create_examples(self, lines, set_type):
-        """Creates examples for the training, dev and test sets."""
+        """Creates examples for the training and dev sets."""
         examples = []
         for (i, line) in enumerate(lines):
             if i == 0:
@@ -241,7 +231,7 @@ class MnliProcessor(DataProcessor):
             guid = "%s-%s" % (set_type, line[0])
             text_a = line[8]
             text_b = line[9]
-            label = None if set_type.startswith("test") else line[-1]
+            label = line[-1]
             examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
         return examples
 
@@ -251,11 +241,7 @@ class MnliMismatchedProcessor(MnliProcessor):
 
     def get_dev_examples(self, data_dir):
         """See base class."""
-        return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev_mismatched.tsv")), "dev_mismatched")
-
-    def get_test_examples(self, data_dir):
-        """See base class."""
-        return self._create_examples(self._read_tsv(os.path.join(data_dir, "test_mismatched.tsv")), "test_mismatched")
+        return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev_mismatched.tsv")), "dev_matched")
 
 
 class ColaProcessor(DataProcessor):
@@ -278,25 +264,17 @@ class ColaProcessor(DataProcessor):
         """See base class."""
         return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
 
-    def get_test_examples(self, data_dir):
-        """See base class."""
-        return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
-
     def get_labels(self):
         """See base class."""
         return ["0", "1"]
 
     def _create_examples(self, lines, set_type):
-        """Creates examples for the training, dev and test sets."""
-        test_mode = set_type == "test"
-        if test_mode:
-            lines = lines[1:]
-        text_index = 1 if test_mode else 3
+        """Creates examples for the training and dev sets."""
         examples = []
         for (i, line) in enumerate(lines):
             guid = "%s-%s" % (set_type, i)
-            text_a = line[text_index]
-            label = None if test_mode else line[1]
+            text_a = line[3]
+            label = line[1]
             examples.append(InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
         return examples
 
@@ -321,23 +299,19 @@ class Sst2Processor(DataProcessor):
         """See base class."""
         return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
 
-    def get_test_examples(self, data_dir):
-        """See base class."""
-        return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
-
     def get_labels(self):
         """See base class."""
         return ["0", "1"]
 
     def _create_examples(self, lines, set_type):
-        """Creates examples for the training, dev and test sets."""
+        """Creates examples for the training and dev sets."""
         examples = []
         for (i, line) in enumerate(lines):
             if i == 0:
                 continue
             guid = "%s-%s" % (set_type, i)
             text_a = line[0]
-            label = None if set_type == "test" else line[1]
+            label = line[1]
             examples.append(InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
         return examples
 
@@ -362,16 +336,12 @@ class StsbProcessor(DataProcessor):
         """See base class."""
         return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
 
-    def get_test_examples(self, data_dir):
-        """See base class."""
-        return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
-
     def get_labels(self):
         """See base class."""
         return [None]
 
     def _create_examples(self, lines, set_type):
-        """Creates examples for the training, dev and test sets."""
+        """Creates examples for the training and dev sets."""
         examples = []
         for (i, line) in enumerate(lines):
             if i == 0:
@@ -379,7 +349,7 @@ class StsbProcessor(DataProcessor):
             guid = "%s-%s" % (set_type, line[0])
             text_a = line[7]
             text_b = line[8]
-            label = None if set_type == "test" else line[-1]
+            label = line[-1]
             examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
         return examples
 
@@ -404,28 +374,21 @@ class QqpProcessor(DataProcessor):
         """See base class."""
         return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
 
-    def get_test_examples(self, data_dir):
-        """See base class."""
-        return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
-
     def get_labels(self):
         """See base class."""
         return ["0", "1"]
 
     def _create_examples(self, lines, set_type):
-        """Creates examples for the training, dev and test sets."""
-        test_mode = set_type == "test"
-        q1_index = 1 if test_mode else 3
-        q2_index = 2 if test_mode else 4
+        """Creates examples for the training and dev sets."""
         examples = []
         for (i, line) in enumerate(lines):
             if i == 0:
                 continue
             guid = "%s-%s" % (set_type, line[0])
             try:
-                text_a = line[q1_index]
-                text_b = line[q2_index]
-                label = None if test_mode else line[5]
+                text_a = line[3]
+                text_b = line[4]
+                label = line[5]
             except IndexError:
                 continue
             examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
@@ -450,18 +413,14 @@ class QnliProcessor(DataProcessor):
 
     def get_dev_examples(self, data_dir):
         """See base class."""
-        return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
-
-    def get_test_examples(self, data_dir):
-        """See base class."""
-        return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
+        return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev_matched")
 
     def get_labels(self):
         """See base class."""
         return ["entailment", "not_entailment"]
 
     def _create_examples(self, lines, set_type):
-        """Creates examples for the training, dev and test sets."""
+        """Creates examples for the training and dev sets."""
         examples = []
         for (i, line) in enumerate(lines):
             if i == 0:
@@ -469,7 +428,7 @@ class QnliProcessor(DataProcessor):
             guid = "%s-%s" % (set_type, line[0])
             text_a = line[1]
             text_b = line[2]
-            label = None if set_type == "test" else line[-1]
+            label = line[-1]
             examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
         return examples
 
@@ -494,16 +453,12 @@ class RteProcessor(DataProcessor):
         """See base class."""
         return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
 
-    def get_test_examples(self, data_dir):
-        """See base class."""
-        return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
-
     def get_labels(self):
         """See base class."""
         return ["entailment", "not_entailment"]
 
     def _create_examples(self, lines, set_type):
-        """Creates examples for the training, dev and test sets."""
+        """Creates examples for the training and dev sets."""
         examples = []
         for (i, line) in enumerate(lines):
             if i == 0:
@@ -511,7 +466,7 @@ class RteProcessor(DataProcessor):
             guid = "%s-%s" % (set_type, line[0])
             text_a = line[1]
             text_b = line[2]
-            label = None if set_type == "test" else line[-1]
+            label = line[-1]
             examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
         return examples
 
@@ -536,16 +491,12 @@ class WnliProcessor(DataProcessor):
         """See base class."""
         return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
 
-    def get_test_examples(self, data_dir):
-        """See base class."""
-        return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
-
     def get_labels(self):
         """See base class."""
         return ["0", "1"]
 
     def _create_examples(self, lines, set_type):
-        """Creates examples for the training, dev and test sets."""
+        """Creates examples for the training and dev sets."""
         examples = []
         for (i, line) in enumerate(lines):
             if i == 0:
@@ -553,7 +504,7 @@ class WnliProcessor(DataProcessor):
             guid = "%s-%s" % (set_type, line[0])
             text_a = line[1]
             text_b = line[2]
-            label = None if set_type == "test" else line[-1]
+            label = line[-1]
             examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
         return examples
 

src/transformers/data/processors/squad.py

@@ -195,22 +195,18 @@ def squad_convert_example_to_features(example, max_seq_length, doc_stride, max_q
         cls_index = span["input_ids"].index(tokenizer.cls_token_id)
 
         # p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
-        # Original TF implem also keep the classification token (set to 0)
-        p_mask = np.ones_like(span["token_type_ids"])
+        # Original TF implem also keep the classification token (set to 0) (not sure why...)
+        p_mask = np.array(span["token_type_ids"])
+
+        p_mask = np.minimum(p_mask, 1)
+
         if tokenizer.padding_side == "right":
-            p_mask[len(truncated_query) + sequence_added_tokens :] = 0
-        else:
-            p_mask[-len(span["tokens"]) : -(len(truncated_query) + sequence_added_tokens)] = 0
+            # Limit positive values to one
+            p_mask = 1 - p_mask
 
-        pad_token_indices = np.where(span["input_ids"] == tokenizer.pad_token_id)
-        special_token_indices = np.asarray(
-            tokenizer.get_special_tokens_mask(span["input_ids"], already_has_special_tokens=True)
-        ).nonzero()
+        p_mask[np.where(np.array(span["input_ids"]) == tokenizer.sep_token_id)[0]] = 1
 
-        p_mask[pad_token_indices] = 1
-        p_mask[special_token_indices] = 1
-
-        # Set the cls index to 0: the CLS index can be used for impossible answers
+        # Set the CLS index to '0'
         p_mask[cls_index] = 0
 
         span_is_impossible = example.is_impossible

src/transformers/data/processors/utils.py

@@ -98,10 +98,6 @@ class DataProcessor:
         """Gets a collection of `InputExample`s for the dev set."""
         raise NotImplementedError()
 
-    def get_test_examples(self, data_dir):
-        """Gets a collection of `InputExample`s for the test set."""
-        raise NotImplementedError()
-
     def get_labels(self):
         """Gets the list of labels for this data set."""
         raise NotImplementedError()

src/transformers/file_utils.py

@@ -15,7 +15,6 @@ import tempfile
 from contextlib import contextmanager
 from functools import partial, wraps
 from hashlib import sha256
-from pathlib import Path
 from typing import Optional
 from urllib.parse import urlparse
 from zipfile import ZipFile, is_zipfile
@@ -69,10 +68,19 @@ except ImportError:
     )
 default_cache_path = os.path.join(torch_cache_home, "transformers")
 
+try:
+    from pathlib import Path
 
-PYTORCH_PRETRAINED_BERT_CACHE = os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
-PYTORCH_TRANSFORMERS_CACHE = os.getenv("PYTORCH_TRANSFORMERS_CACHE", PYTORCH_PRETRAINED_BERT_CACHE)
-TRANSFORMERS_CACHE = os.getenv("TRANSFORMERS_CACHE", PYTORCH_TRANSFORMERS_CACHE)
+    PYTORCH_PRETRAINED_BERT_CACHE = Path(
+        os.getenv("PYTORCH_TRANSFORMERS_CACHE", os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path))
+    )
+except (AttributeError, ImportError):
+    PYTORCH_PRETRAINED_BERT_CACHE = os.getenv(
+        "PYTORCH_TRANSFORMERS_CACHE", os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
+    )
+
+PYTORCH_TRANSFORMERS_CACHE = PYTORCH_PRETRAINED_BERT_CACHE  # Kept for backward compatibility
+TRANSFORMERS_CACHE = PYTORCH_PRETRAINED_BERT_CACHE  # Kept for backward compatibility
 
 WEIGHTS_NAME = "pytorch_model.bin"
 TF2_WEIGHTS_NAME = "tf_model.h5"

src/transformers/modeling_albert.py

@@ -175,7 +175,7 @@ class AlbertEmbeddings(BertEmbeddings):
     def __init__(self, config):
         super().__init__(config)
 
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=0)
         self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.embedding_size)
         self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size)
         self.LayerNorm = torch.nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps)
@@ -550,7 +550,7 @@ class AlbertModel(AlbertPreTrainedModel):
             token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
 
         extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
-        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
+        extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype)  # fp16 compatibility
         extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
         head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
 

src/transformers/modeling_auto.py

@@ -30,7 +30,6 @@ from .configuration_auto import (
     EncoderDecoderConfig,
     FlaubertConfig,
     GPT2Config,
-    LongformerConfig,
     OpenAIGPTConfig,
     ReformerConfig,
     RobertaConfig,
@@ -40,7 +39,6 @@ from .configuration_auto import (
     XLMRobertaConfig,
     XLNetConfig,
 )
-from .configuration_marian import MarianConfig
 from .configuration_utils import PretrainedConfig
 from .modeling_albert import (
     ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
@@ -88,7 +86,6 @@ from .modeling_electra import (
     ELECTRA_PRETRAINED_MODEL_ARCHIVE_MAP,
     ElectraForMaskedLM,
     ElectraForPreTraining,
-    ElectraForSequenceClassification,
     ElectraForTokenClassification,
     ElectraModel,
 )
@@ -101,8 +98,6 @@ from .modeling_flaubert import (
     FlaubertWithLMHeadModel,
 )
 from .modeling_gpt2 import GPT2_PRETRAINED_MODEL_ARCHIVE_MAP, GPT2LMHeadModel, GPT2Model
-from .modeling_longformer import LONGFORMER_PRETRAINED_MODEL_ARCHIVE_MAP, LongformerForMaskedLM, LongformerModel
-from .modeling_marian import MarianMTModel
 from .modeling_openai import OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP, OpenAIGPTLMHeadModel, OpenAIGPTModel
 from .modeling_reformer import ReformerModel, ReformerModelWithLMHead
 from .modeling_roberta import (
@@ -165,7 +160,6 @@ ALL_PRETRAINED_MODEL_ARCHIVE_MAP = dict(
         FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
         XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
         ELECTRA_PRETRAINED_MODEL_ARCHIVE_MAP,
-        LONGFORMER_PRETRAINED_MODEL_ARCHIVE_MAP,
     ]
     for key, value, in pretrained_map.items()
 )
@@ -178,7 +172,6 @@ MODEL_MAPPING = OrderedDict(
         (CamembertConfig, CamembertModel),
         (XLMRobertaConfig, XLMRobertaModel),
         (BartConfig, BartModel),
-        (LongformerConfig, LongformerModel),
         (RobertaConfig, RobertaModel),
         (BertConfig, BertModel),
         (OpenAIGPTConfig, OpenAIGPTModel),
@@ -201,7 +194,6 @@ MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
         (CamembertConfig, CamembertForMaskedLM),
         (XLMRobertaConfig, XLMRobertaForMaskedLM),
         (BartConfig, BartForConditionalGeneration),
-        (LongformerConfig, LongformerForMaskedLM),
         (RobertaConfig, RobertaForMaskedLM),
         (BertConfig, BertForPreTraining),
         (OpenAIGPTConfig, OpenAIGPTLMHeadModel),
@@ -222,9 +214,7 @@ MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
         (AlbertConfig, AlbertForMaskedLM),
         (CamembertConfig, CamembertForMaskedLM),
         (XLMRobertaConfig, XLMRobertaForMaskedLM),
-        (MarianConfig, MarianMTModel),
         (BartConfig, BartForConditionalGeneration),
-        (LongformerConfig, LongformerForMaskedLM),
         (RobertaConfig, RobertaForMaskedLM),
         (BertConfig, BertForMaskedLM),
         (OpenAIGPTConfig, OpenAIGPTLMHeadModel),
@@ -252,7 +242,6 @@ MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
         (XLNetConfig, XLNetForSequenceClassification),
         (FlaubertConfig, FlaubertForSequenceClassification),
         (XLMConfig, XLMForSequenceClassification),
-        (ElectraConfig, ElectraForSequenceClassification),
     ]
 )
 
@@ -321,7 +310,6 @@ class AutoModel:
                 The model class to instantiate is selected based on the configuration class:
 
                 - isInstance of `distilbert` configuration class: :class:`~transformers.DistilBertModel` (DistilBERT model)
-                - isInstance of `longformer` configuration class: :class:`~transformers.LongformerModel` (Longformer model)
                 - isInstance of `roberta` configuration class: :class:`~transformers.RobertaModel` (RoBERTa model)
                 - isInstance of `bert` configuration class: :class:`~transformers.BertModel` (Bert model)
                 - isInstance of `openai-gpt` configuration class: :class:`~transformers.OpenAIGPTModel` (OpenAI GPT model)
@@ -364,7 +352,6 @@ class AutoModel:
             - contains `albert`: :class:`~transformers.AlbertModel` (ALBERT model)
             - contains `camembert`: :class:`~transformers.CamembertModel` (CamemBERT model)
             - contains `xlm-roberta`: :class:`~transformers.XLMRobertaModel` (XLM-RoBERTa model)
-            - contains `longformer` :class:`~transformers.LongformerModel` (Longformer model)
             - contains `roberta`: :class:`~transformers.RobertaModel` (RoBERTa model)
             - contains `bert`: :class:`~transformers.BertModel` (Bert model)
             - contains `openai-gpt`: :class:`~transformers.OpenAIGPTModel` (OpenAI GPT model)
@@ -473,7 +460,6 @@ class AutoModelForPreTraining:
                 The model class to instantiate is selected based on the configuration class:
 
                 - isInstance of `distilbert` configuration class: :class:`~transformers.DistilBertForMaskedLM` (DistilBERT model)
-                - isInstance of `longformer` configuration class: :class:`~transformers.LongformerForMaskedLM` (Longformer model)
                 - isInstance of `roberta` configuration class: :class:`~transformers.RobertaForMaskedLM` (RoBERTa model)
                 - isInstance of `bert` configuration class: :class:`~transformers.BertForPreTraining` (Bert model)
                 - isInstance of `openai-gpt` configuration class: :class:`~transformers.OpenAIGPTLMHeadModel` (OpenAI GPT model)
@@ -515,7 +501,6 @@ class AutoModelForPreTraining:
             - contains `albert`: :class:`~transformers.AlbertForMaskedLM` (ALBERT model)
             - contains `camembert`: :class:`~transformers.CamembertForMaskedLM` (CamemBERT model)
             - contains `xlm-roberta`: :class:`~transformers.XLMRobertaForMaskedLM` (XLM-RoBERTa model)
-            - contains `longformer`: :class:`~transformers.LongformerForMaskedLM` (Longformer model)
             - contains `roberta`: :class:`~transformers.RobertaForMaskedLM` (RoBERTa model)
             - contains `bert`: :class:`~transformers.BertForPreTraining` (Bert model)
             - contains `openai-gpt`: :class:`~transformers.OpenAIGPTLMHeadModel` (OpenAI GPT model)
@@ -618,7 +603,6 @@ class AutoModelWithLMHead:
                 The model class to instantiate is selected based on the configuration class:
 
                 - isInstance of `distilbert` configuration class: :class:`~transformers.DistilBertForMaskedLM` (DistilBERT model)
-                - isInstance of `longformer` configuration class: :class:`~transformers.LongformerForMaskedLM` (Longformer model)
                 - isInstance of `roberta` configuration class: :class:`~transformers.RobertaForMaskedLM` (RoBERTa model)
                 - isInstance of `bert` configuration class: :class:`~transformers.BertForMaskedLM` (Bert model)
                 - isInstance of `openai-gpt` configuration class: :class:`~transformers.OpenAIGPTLMHeadModel` (OpenAI GPT model)
@@ -661,7 +645,6 @@ class AutoModelWithLMHead:
             - contains `albert`: :class:`~transformers.AlbertForMaskedLM` (ALBERT model)
             - contains `camembert`: :class:`~transformers.CamembertForMaskedLM` (CamemBERT model)
             - contains `xlm-roberta`: :class:`~transformers.XLMRobertaForMaskedLM` (XLM-RoBERTa model)
-            - contains `longformer`: :class:`~transformers.LongformerForMaskedLM` (Longformer model)
             - contains `roberta`: :class:`~transformers.RobertaForMaskedLM` (RoBERTa model)
             - contains `bert`: :class:`~transformers.BertForMaskedLM` (Bert model)
             - contains `openai-gpt`: :class:`~transformers.OpenAIGPTLMHeadModel` (OpenAI GPT model)
@@ -920,7 +903,7 @@ class AutoModelForQuestionAnswering:
         Examples::
 
             config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
-            model = AutoModelForQuestionAnswering.from_config(config)  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
+            model = AutoModelForSequenceClassification.from_config(config)  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
         """
         for config_class, model_class in MODEL_FOR_QUESTION_ANSWERING_MAPPING.items():
             if isinstance(config, config_class):

src/transformers/modeling_bart.py

@@ -886,7 +886,7 @@ class BartForConditionalGeneration(PretrainedBartModel):
         if new_num_tokens <= old_num_tokens:
             new_bias = self.final_logits_bias[:, :new_num_tokens]
         else:
-            extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
+            extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens))
             new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
         self.register_buffer("final_logits_bias", new_bias)
 
@@ -980,12 +980,12 @@ class BartForConditionalGeneration(PretrainedBartModel):
             "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
         }
 
-    def prepare_logits_for_generation(self, logits, cur_len, max_length):
+    def prepare_scores_for_generation(self, scores, cur_len, max_length):
         if cur_len == 1:
-            self._force_token_ids_generation(logits, self.config.bos_token_id)
+            self._force_token_ids_generation(scores, self.config.bos_token_id)
         if cur_len == max_length - 1 and self.config.eos_token_id is not None:
-            self._force_token_ids_generation(logits, self.config.eos_token_id)
-        return logits
+            self._force_token_ids_generation(scores, self.config.eos_token_id)
+        return scores
 
     def _force_token_ids_generation(self, scores, token_ids) -> None:
         """force one of token_ids to be generated by setting prob of all other tokens to 0"""

src/transformers/modeling_bert.py

@@ -703,7 +703,9 @@ class BertModel(BertPreTrainedModel):
 
         # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
         # ourselves in which case we just need to make it broadcastable to all heads.
-        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
+            attention_mask, input_shape, self.device
+        )
 
         # If a 2D ou 3D attention mask is provided for the cross-attention
         # we need to make broadcastabe to [batch_size, num_heads, seq_length, seq_length]

src/transformers/modeling_distilbert.py

@@ -61,7 +61,7 @@ def create_sinusoidal_embeddings(n_pos, dim, out):
 class Embeddings(nn.Module):
     def __init__(self, config):
         super().__init__()
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.dim, padding_idx=config.pad_token_id)
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.dim, padding_idx=0)
         self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.dim)
         if config.sinusoidal_pos_embds:
             create_sinusoidal_embeddings(

src/transformers/modeling_electra.py

@@ -3,7 +3,6 @@ import os
 
 import torch
 import torch.nn as nn
-from torch.nn import CrossEntropyLoss, MSELoss
 
 from .activations import get_activation
 from .configuration_electra import ElectraConfig
@@ -331,112 +330,6 @@ class ElectraModel(ElectraPreTrainedModel):
         return hidden_states
 
 
-class ElectraClassificationHead(nn.Module):
-    """Head for sentence-level classification tasks."""
-
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
-
-    def forward(self, features, **kwargs):
-        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
-        x = self.dropout(x)
-        x = self.dense(x)
-        x = get_activation("gelu")(x)  # although BERT uses tanh here, it seems Electra authors used gelu here
-        x = self.dropout(x)
-        x = self.out_proj(x)
-        return x
-
-
-@add_start_docstrings(
-    """ELECTRA Model transformer with a sequence classification/regression head on top (a linear layer on top of
-    the pooled output) e.g. for GLUE tasks. """,
-    ELECTRA_START_DOCSTRING,
-)
-class ElectraForSequenceClassification(ElectraPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.electra = ElectraModel(config)
-        self.classifier = ElectraClassificationHead(config)
-
-        self.init_weights()
-
-    @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        labels=None,
-    ):
-        r"""
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
-            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),
-            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-
-    Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
-        loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
-            Classification (or regression if config.num_labels==1) loss.
-        logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
-            Classification (or regression if config.num_labels==1) scores (before SoftMax).
-        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
-            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
-            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
-            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
-            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-
-    Examples::
-
-        from transformers import BertTokenizer, BertForSequenceClassification
-        import torch
-
-        tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-        model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
-
-        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
-        labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
-        outputs = model(input_ids, labels=labels)
-
-        loss, logits = outputs[:2]
-
-        """
-        discriminator_hidden_states = self.electra(
-            input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds
-        )
-
-        sequence_output = discriminator_hidden_states[0]
-        logits = self.classifier(sequence_output)
-
-        outputs = (logits,) + discriminator_hidden_states[2:]  # add hidden states and attention if they are here
-
-        if labels is not None:
-            if self.num_labels == 1:
-                #  We are doing regression
-                loss_fct = MSELoss()
-                loss = loss_fct(logits.view(-1), labels.view(-1))
-            else:
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-            outputs = (loss,) + outputs
-
-        return outputs  # (loss), logits, (hidden_states), (attentions)
-
-
 @add_start_docstrings(
     """
     Electra model with a binary classification head on top as used during pre-training for identifying generated

src/transformers/modeling_gpt2.py

@@ -142,10 +142,10 @@ class Attention(nn.Module):
     def _attn(self, q, k, v, attention_mask=None, head_mask=None):
         w = torch.matmul(q, k)
         if self.scale:
-            w = w / (float(v.size(-1)) ** 0.5)
+            w = w / (v.size(-1) ** 0.5)
         nd, ns = w.size(-2), w.size(-1)
         mask = self.bias[:, :, ns - nd : ns, :ns]
-        w = torch.where(mask.bool(), w, self.masked_bias.to(w.dtype))
+        w = torch.where(mask, w, self.masked_bias.to(w.dtype))
 
         if attention_mask is not None:
             # Apply the attention mask

src/transformers/modeling_longformer.py

@@ -1,709 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The Allen Institute for AI team and The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# 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.
-"""PyTorch Longformer model. """
-
-import logging
-import math
-
-import torch
-import torch.nn as nn
-from torch.nn import CrossEntropyLoss
-from torch.nn import functional as F
-
-from .configuration_longformer import LongformerConfig
-from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
-from .modeling_bert import BertPreTrainedModel
-from .modeling_roberta import RobertaLMHead, RobertaModel
-
-
-logger = logging.getLogger(__name__)
-
-LONGFORMER_PRETRAINED_MODEL_ARCHIVE_MAP = {
-    "longformer-base-4096": "https://s3.amazonaws.com/models.huggingface.co/bert/allenai/longformer-base-4096/pytorch_model.bin",
-    "longformer-large-4096": "https://s3.amazonaws.com/models.huggingface.co/bert/allenai/longformer-large-4096/pytorch_model.bin",
-}
-
-
-class LongformerSelfAttention(nn.Module):
-    def __init__(self, config, layer_id):
-        super().__init__()
-        if config.hidden_size % config.num_attention_heads != 0:
-            raise ValueError(
-                "The hidden size (%d) is not a multiple of the number of attention "
-                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
-            )
-        self.output_attentions = config.output_attentions
-        self.num_heads = config.num_attention_heads
-        self.head_dim = int(config.hidden_size / config.num_attention_heads)
-        self.embed_dim = config.hidden_size
-
-        self.query = nn.Linear(config.hidden_size, self.embed_dim)
-        self.key = nn.Linear(config.hidden_size, self.embed_dim)
-        self.value = nn.Linear(config.hidden_size, self.embed_dim)
-
-        # separate projection layers for tokens with global attention
-        self.query_global = nn.Linear(config.hidden_size, self.embed_dim)
-        self.key_global = nn.Linear(config.hidden_size, self.embed_dim)
-        self.value_global = nn.Linear(config.hidden_size, self.embed_dim)
-
-        self.dropout = config.attention_probs_dropout_prob
-
-        self.layer_id = layer_id
-        attention_window = config.attention_window[self.layer_id]
-        assert (
-            attention_window % 2 == 0
-        ), f"`attention_window` for layer {self.layer_id} has to be an even value. Given {attention_window}"
-        assert (
-            attention_window > 0
-        ), f"`attention_window` for layer {self.layer_id} has to be positive. Given {attention_window}"
-
-        self.one_sided_attention_window_size = attention_window // 2
-
-    @staticmethod
-    def _skew(x, direction):
-        """Convert diagonals into columns (or columns into diagonals depending on `direction`"""
-        x_padded = F.pad(x, direction)  # padding value is not important because it will be overwritten
-        x_padded = x_padded.view(*x_padded.size()[:-2], x_padded.size(-1), x_padded.size(-2))
-        return x_padded
-
-    @staticmethod
-    def _skew2(x):
-        """shift every row 1 step to right converting columns into diagonals"""
-        # X = B x C x M x L
-        B, C, M, L = x.size()
-        x = F.pad(x, (0, M + 1))  # B x C x M x (L+M+1). Padding value is not important because it'll be overwritten
-        x = x.view(B, C, -1)  # B x C x ML+MM+M
-        x = x[:, :, :-M]  # B x C x ML+MM
-        x = x.view(B, C, M, M + L)  # B x C, M x L+M
-        x = x[:, :, :, :-1]
-        return x
-
-    @staticmethod
-    def _chunk(x, w):
-        """convert into overlapping chunkings. Chunk size = 2w, overlap size = w"""
-
-        # non-overlapping chunks of size = 2w
-        x = x.view(x.size(0), x.size(1) // (w * 2), w * 2, x.size(2))
-
-        # use `as_strided` to make the chunks overlap with an overlap size = w
-        chunk_size = list(x.size())
-        chunk_size[1] = chunk_size[1] * 2 - 1
-
-        chunk_stride = list(x.stride())
-        chunk_stride[1] = chunk_stride[1] // 2
-        return x.as_strided(size=chunk_size, stride=chunk_stride)
-
-    def _mask_invalid_locations(self, input_tensor, w) -> torch.Tensor:
-        affected_seqlen = w
-        beginning_mask_2d = input_tensor.new_ones(w, w + 1).tril().flip(dims=[0])
-        beginning_mask = beginning_mask_2d[None, :, None, :]
-        ending_mask = beginning_mask.flip(dims=(1, 3))
-        seqlen = input_tensor.size(1)
-        beginning_input = input_tensor[:, :affected_seqlen, :, : w + 1]
-        beginning_mask = beginning_mask[:, :seqlen].expand(beginning_input.size())
-        beginning_input.masked_fill_(beginning_mask == 1, -float("inf"))  # `== 1` converts to bool or uint8
-        ending_input = input_tensor[:, -affected_seqlen:, :, -(w + 1) :]
-        ending_mask = ending_mask[:, -seqlen:].expand(ending_input.size())
-        ending_input.masked_fill_(ending_mask == 1, -float("inf"))  # `== 1` converts to bool or uint8
-
-    def _sliding_chunks_matmul_qk(self, q: torch.Tensor, k: torch.Tensor, w: int):
-        """Matrix multiplicatio of query x key tensors using with a sliding window attention pattern.
-        This implementation splits the input into overlapping chunks of size 2w (e.g. 512 for pretrained Longformer)
-        with an overlap of size w"""
-        batch_size, seqlen, num_heads, head_dim = q.size()
-        assert seqlen % (w * 2) == 0, f"Sequence length should be multiple of {w * 2}. Given {seqlen}"
-        assert q.size() == k.size()
-
-        chunks_count = seqlen // w - 1
-
-        # group batch_size and num_heads dimensions into one, then chunk seqlen into chunks of size w * 2
-        q = q.transpose(1, 2).reshape(batch_size * num_heads, seqlen, head_dim)
-        k = k.transpose(1, 2).reshape(batch_size * num_heads, seqlen, head_dim)
-
-        chunk_q = self._chunk(q, w)
-        chunk_k = self._chunk(k, w)
-
-        # matrix multipication
-        # bcxd: batch_size * num_heads x chunks x 2w x head_dim
-        # bcyd: batch_size * num_heads x chunks x 2w x head_dim
-        # bcxy: batch_size * num_heads x chunks x 2w x 2w
-        chunk_attn = torch.einsum("bcxd,bcyd->bcxy", (chunk_q, chunk_k))  # multiply
-
-        # convert diagonals into columns
-        diagonal_chunk_attn = self._skew(chunk_attn, direction=(0, 0, 0, 1))
-
-        # allocate space for the overall attention matrix where the chunks are compined. The last dimension
-        # has (w * 2 + 1) columns. The first (w) columns are the w lower triangles (attention from a word to
-        # w previous words). The following column is attention score from each word to itself, then
-        # followed by w columns for the upper triangle.
-
-        diagonal_attn = diagonal_chunk_attn.new_empty((batch_size * num_heads, chunks_count + 1, w, w * 2 + 1))
-
-        # copy parts from diagonal_chunk_attn into the compined matrix of attentions
-        # - copying the main diagonal and the upper triangle
-        diagonal_attn[:, :-1, :, w:] = diagonal_chunk_attn[:, :, :w, : w + 1]
-        diagonal_attn[:, -1, :, w:] = diagonal_chunk_attn[:, -1, w:, : w + 1]
-        # - copying the lower triangle
-        diagonal_attn[:, 1:, :, :w] = diagonal_chunk_attn[:, :, -(w + 1) : -1, w + 1 :]
-        diagonal_attn[:, 0, 1:w, 1:w] = diagonal_chunk_attn[:, 0, : w - 1, 1 - w :]
-
-        # separate batch_size and num_heads dimensions again
-        diagonal_attn = diagonal_attn.view(batch_size, num_heads, seqlen, 2 * w + 1).transpose(2, 1)
-
-        self._mask_invalid_locations(diagonal_attn, w)
-        return diagonal_attn
-
-    def _sliding_chunks_matmul_pv(self, prob: torch.Tensor, v: torch.Tensor, w: int):
-        """Same as _sliding_chunks_matmul_qk but for prob and value tensors. It is expecting the same output
-        format from _sliding_chunks_matmul_qk"""
-        batch_size, seqlen, num_heads, head_dim = v.size()
-        assert seqlen % (w * 2) == 0
-        assert prob.size()[:3] == v.size()[:3]
-        assert prob.size(3) == 2 * w + 1
-        chunks_count = seqlen // w - 1
-        # group batch_size and num_heads dimensions into one, then chunk seqlen into chunks of size 2w
-        chunk_prob = prob.transpose(1, 2).reshape(batch_size * num_heads, seqlen // w, w, 2 * w + 1)
-
-        # group batch_size and num_heads dimensions into one
-        v = v.transpose(1, 2).reshape(batch_size * num_heads, seqlen, head_dim)
-
-        # pad seqlen with w at the beginning of the sequence and another w at the end
-        padded_v = F.pad(v, (0, 0, w, w), value=-1)
-
-        # chunk padded_v into chunks of size 3w and an overlap of size w
-        chunk_v_size = (batch_size * num_heads, chunks_count + 1, 3 * w, head_dim)
-        chunk_v_stride = padded_v.stride()
-        chunk_v_stride = chunk_v_stride[0], w * chunk_v_stride[1], chunk_v_stride[1], chunk_v_stride[2]
-        chunk_v = padded_v.as_strided(size=chunk_v_size, stride=chunk_v_stride)
-
-        skewed_prob = self._skew2(chunk_prob)
-
-        context = torch.einsum("bcwd,bcdh->bcwh", (skewed_prob, chunk_v))
-        return context.view(batch_size, num_heads, seqlen, head_dim).transpose(1, 2)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-    ):
-        """
-        LongformerSelfAttention expects `len(hidden_states)` to be multiple of `attention_window`.
-        Padding to `attention_window` happens in LongformerModel.forward to avoid redoing the padding on each layer.
-
-        The `attention_mask` is changed in `BertModel.forward` from 0, 1, 2 to
-            -ve: no attention
-              0: local attention
-            +ve: global attention
-
-        `encoder_hidden_states` and `encoder_attention_mask` are not supported and should be None
-        """
-        # TODO: add support for `encoder_hidden_states` and `encoder_attention_mask`
-        assert encoder_hidden_states is None, "`encoder_hidden_states` is not supported and should be None"
-        assert encoder_attention_mask is None, "`encoder_attention_mask` is not supported and shiould be None"
-
-        if attention_mask is not None:
-            attention_mask = attention_mask.squeeze(dim=2).squeeze(dim=1)
-            key_padding_mask = attention_mask < 0
-            extra_attention_mask = attention_mask > 0
-            remove_from_windowed_attention_mask = attention_mask != 0
-
-            num_extra_indices_per_batch = extra_attention_mask.long().sum(dim=1)
-            max_num_extra_indices_per_batch = num_extra_indices_per_batch.max()
-            if max_num_extra_indices_per_batch <= 0:
-                extra_attention_mask = None
-            else:
-                # To support the case of variable number of global attention in the rows of a batch,
-                # we use the following three selection masks to select global attention embeddings
-                # in a 3d tensor and pad it to `max_num_extra_indices_per_batch`
-                # 1) selecting embeddings that correspond to global attention
-                extra_attention_mask_nonzeros = extra_attention_mask.nonzero(as_tuple=True)
-                zero_to_max_range = torch.arange(
-                    0, max_num_extra_indices_per_batch, device=num_extra_indices_per_batch.device
-                )
-                # mask indicating which values are actually going to be padding
-                selection_padding_mask = zero_to_max_range < num_extra_indices_per_batch.unsqueeze(dim=-1)
-                # 2) location of the non-padding values in the selected global attention
-                selection_padding_mask_nonzeros = selection_padding_mask.nonzero(as_tuple=True)
-                # 3) location of the padding values in the selected global attention
-                selection_padding_mask_zeros = (selection_padding_mask == 0).nonzero(as_tuple=True)
-        else:
-            remove_from_windowed_attention_mask = None
-            extra_attention_mask = None
-            key_padding_mask = None
-
-        hidden_states = hidden_states.transpose(0, 1)
-        seqlen, batch_size, embed_dim = hidden_states.size()
-        assert embed_dim == self.embed_dim
-        q = self.query(hidden_states)
-        k = self.key(hidden_states)
-        v = self.value(hidden_states)
-        q /= math.sqrt(self.head_dim)
-
-        q = q.view(seqlen, batch_size, self.num_heads, self.head_dim).transpose(0, 1)
-        k = k.view(seqlen, batch_size, self.num_heads, self.head_dim).transpose(0, 1)
-        # attn_weights = (batch_size, seqlen, num_heads, window*2+1)
-        attn_weights = self._sliding_chunks_matmul_qk(q, k, self.one_sided_attention_window_size)
-        self._mask_invalid_locations(attn_weights, self.one_sided_attention_window_size)
-        if remove_from_windowed_attention_mask is not None:
-            # This implementation is fast and takes very little memory because num_heads x hidden_size = 1
-            # from (batch_size x seqlen) to (batch_size x seqlen x num_heads x hidden_size)
-            remove_from_windowed_attention_mask = remove_from_windowed_attention_mask.unsqueeze(dim=-1).unsqueeze(
-                dim=-1
-            )
-            # cast to fp32/fp16 then replace 1's with -inf
-            float_mask = remove_from_windowed_attention_mask.type_as(q).masked_fill(
-                remove_from_windowed_attention_mask, -10000.0
-            )
-            ones = float_mask.new_ones(size=float_mask.size())  # tensor of ones
-            # diagonal mask with zeros everywhere and -inf inplace of padding
-            d_mask = self._sliding_chunks_matmul_qk(ones, float_mask, self.one_sided_attention_window_size)
-            attn_weights += d_mask
-        assert list(attn_weights.size()) == [
-            batch_size,
-            seqlen,
-            self.num_heads,
-            self.one_sided_attention_window_size * 2 + 1,
-        ]
-
-        # the extra attention
-        if extra_attention_mask is not None:
-            selected_k = k.new_zeros(batch_size, max_num_extra_indices_per_batch, self.num_heads, self.head_dim)
-            selected_k[selection_padding_mask_nonzeros] = k[extra_attention_mask_nonzeros]
-            # (batch_size, seqlen, num_heads, max_num_extra_indices_per_batch)
-            selected_attn_weights = torch.einsum("blhd,bshd->blhs", (q, selected_k))
-            selected_attn_weights[selection_padding_mask_zeros[0], :, :, selection_padding_mask_zeros[1]] = -10000
-            # concat to attn_weights
-            # (batch_size, seqlen, num_heads, extra attention count + 2*window+1)
-            attn_weights = torch.cat((selected_attn_weights, attn_weights), dim=-1)
-
-        attn_weights_fp32 = F.softmax(attn_weights, dim=-1, dtype=torch.float32)  # use fp32 for numerical stability
-        attn_weights = attn_weights_fp32.type_as(attn_weights)
-
-        if key_padding_mask is not None:
-            # softmax sometimes inserts NaN if all positions are masked, replace them with 0
-            attn_weights = torch.masked_fill(attn_weights, key_padding_mask.unsqueeze(-1).unsqueeze(-1), 0.0)
-
-        attn_probs = F.dropout(attn_weights, p=self.dropout, training=self.training)
-        v = v.view(seqlen, batch_size, self.num_heads, self.head_dim).transpose(0, 1)
-        attn = None
-        if extra_attention_mask is not None:
-            selected_attn_probs = attn_probs.narrow(-1, 0, max_num_extra_indices_per_batch)
-            selected_v = v.new_zeros(batch_size, max_num_extra_indices_per_batch, self.num_heads, self.head_dim)
-            selected_v[selection_padding_mask_nonzeros] = v[extra_attention_mask_nonzeros]
-            # use `matmul` because `einsum` crashes sometimes with fp16
-            # attn = torch.einsum('blhs,bshd->blhd', (selected_attn_probs, selected_v))
-            attn = torch.matmul(
-                selected_attn_probs.transpose(1, 2), selected_v.transpose(1, 2).type_as(selected_attn_probs)
-            ).transpose(1, 2)
-            attn_probs = attn_probs.narrow(
-                -1, max_num_extra_indices_per_batch, attn_probs.size(-1) - max_num_extra_indices_per_batch
-            ).contiguous()
-        if attn is None:
-            attn = self._sliding_chunks_matmul_pv(attn_probs, v, self.one_sided_attention_window_size)
-        else:
-            attn += self._sliding_chunks_matmul_pv(attn_probs, v, self.one_sided_attention_window_size)
-
-        assert attn.size() == (batch_size, seqlen, self.num_heads, self.head_dim), "Unexpected size"
-        attn = attn.transpose(0, 1).reshape(seqlen, batch_size, embed_dim).contiguous()
-
-        # For this case, we'll just recompute the attention for these indices
-        # and overwrite the attn tensor.
-        # TODO: remove the redundant computation
-        if extra_attention_mask is not None:
-            selected_hidden_states = hidden_states.new_zeros(max_num_extra_indices_per_batch, batch_size, embed_dim)
-            selected_hidden_states[selection_padding_mask_nonzeros[::-1]] = hidden_states[
-                extra_attention_mask_nonzeros[::-1]
-            ]
-
-            q = self.query_global(selected_hidden_states)
-            k = self.key_global(hidden_states)
-            v = self.value_global(hidden_states)
-            q /= math.sqrt(self.head_dim)
-
-            q = (
-                q.contiguous()
-                .view(max_num_extra_indices_per_batch, batch_size * self.num_heads, self.head_dim)
-                .transpose(0, 1)
-            )  # (batch_size * self.num_heads, max_num_extra_indices_per_batch, head_dim)
-            k = (
-                k.contiguous().view(-1, batch_size * self.num_heads, self.head_dim).transpose(0, 1)
-            )  # batch_size * self.num_heads, seqlen, head_dim)
-            v = (
-                v.contiguous().view(-1, batch_size * self.num_heads, self.head_dim).transpose(0, 1)
-            )  # batch_size * self.num_heads, seqlen, head_dim)
-            attn_weights = torch.bmm(q, k.transpose(1, 2))
-            assert list(attn_weights.size()) == [batch_size * self.num_heads, max_num_extra_indices_per_batch, seqlen]
-
-            attn_weights = attn_weights.view(batch_size, self.num_heads, max_num_extra_indices_per_batch, seqlen)
-            attn_weights[selection_padding_mask_zeros[0], :, selection_padding_mask_zeros[1], :] = -10000.0
-            if key_padding_mask is not None:
-                attn_weights = attn_weights.masked_fill(key_padding_mask.unsqueeze(1).unsqueeze(2), -10000.0,)
-            attn_weights = attn_weights.view(batch_size * self.num_heads, max_num_extra_indices_per_batch, seqlen)
-            attn_weights_float = F.softmax(
-                attn_weights, dim=-1, dtype=torch.float32
-            )  # use fp32 for numerical stability
-            attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training)
-            selected_attn = torch.bmm(attn_probs, v)
-            assert list(selected_attn.size()) == [
-                batch_size * self.num_heads,
-                max_num_extra_indices_per_batch,
-                self.head_dim,
-            ]
-
-            selected_attn_4d = selected_attn.view(
-                batch_size, self.num_heads, max_num_extra_indices_per_batch, self.head_dim
-            )
-            nonzero_selected_attn = selected_attn_4d[
-                selection_padding_mask_nonzeros[0], :, selection_padding_mask_nonzeros[1]
-            ]
-            attn[extra_attention_mask_nonzeros[::-1]] = nonzero_selected_attn.view(
-                len(selection_padding_mask_nonzeros[0]), -1
-            ).type_as(hidden_states)
-
-        context_layer = attn.transpose(0, 1)
-        if self.output_attentions:
-            if extra_attention_mask is not None:
-                # With global attention, return global attention probabilities only
-                # batch_size x num_heads x max_num_global_attention_tokens x sequence_length
-                # which is the attention weights from tokens with global attention to all tokens
-                # It doesn't not return local attention
-                # In case of variable number of global attantion in the rows of a batch,
-                # attn_weights are padded with -10000.0 attention scores
-                attn_weights = attn_weights.view(batch_size, self.num_heads, max_num_extra_indices_per_batch, seqlen)
-            else:
-                # without global attention, return local attention probabilities
-                # batch_size x num_heads x sequence_length x window_size
-                # which is the attention weights of every token attending to its neighbours
-                attn_weights = attn_weights.permute(0, 2, 1, 3)
-        outputs = (context_layer, attn_weights) if self.output_attentions else (context_layer,)
-        return outputs
-
-
-LONGFORMER_START_DOCSTRING = r"""
-
-    This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
-    usage and behavior.
-
-    Parameters:
-        config (:class:`~transformers.LongformerConfig`): Model configuration class with all the parameters of the
-            model. Initializing with a config file does not load the weights associated with the model, only the configuration.
-            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
-"""
-
-LONGFORMER_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary.
-
-            Indices can be obtained using :class:`transformers.LonmgformerTokenizer`.
-            See :func:`transformers.PreTrainedTokenizer.encode` and
-            :func:`transformers.PreTrainedTokenizer.encode_plus` 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`):
-            Mask to decide the attention given on each token, local attention, global attenion, or no attention (for padding tokens).
-            Tokens with global attention attends to all other tokens, and all other tokens attend to them. This is important for
-            task-specific finetuning because it makes the model more flexible at representing the task. For example,
-            for classification, the <s> token should be given global attention. For QA, all question tokens should also have
-            global attention. Please refer to the Longformer paper https://arxiv.org/abs/2004.05150 for more details.
-            Mask values selected in ``[0, 1, 2]``:
-            ``0`` for no attention (padding tokens),
-            ``1`` for local attention (a sliding window attention),
-            ``2`` for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
-
-            `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`):
-            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`):
-            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>`_
-        inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
-            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.
-"""
-
-
-@add_start_docstrings(
-    "The bare Longformer Model outputting raw hidden-states without any specific head on top.",
-    LONGFORMER_START_DOCSTRING,
-)
-class LongformerModel(RobertaModel):
-    """
-    This class overrides :class:`~transformers.RobertaModel` to provide the ability to process
-    long sequences following the selfattention approach described in `Longformer: the Long-Document Transformer`_by
-    Iz Beltagy, Matthew E. Peters, and Arman Cohan. Longformer selfattention combines a local (sliding window)
-    and global attention to extend to long documents without the O(n^2) increase in memory and compute.
-
-    The selfattention module `LongformerSelfAttention` implemented here supports the combination of local and
-    global attention but it lacks support for autoregressive attention and dilated attention. Autoregressive
-    and dilated attention are more relevant for autoregressive language modeling than finetuning on downstream
-    tasks. Future release will add support for autoregressive attention, but the support for dilated attention
-    requires a custom CUDA kernel to be memory and compute efficient.
-
-    .. _`Longformer: the Long-Document Transformer`:
-        https://arxiv.org/abs/2004.05150
-
-    """
-
-    config_class = LongformerConfig
-    pretrained_model_archive_map = LONGFORMER_PRETRAINED_MODEL_ARCHIVE_MAP
-    base_model_prefix = "longformer"
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        if isinstance(config.attention_window, int):
-            assert config.attention_window % 2 == 0, "`config.attention_window` has to be an even value"
-            assert config.attention_window > 0, "`config.attention_window` has to be positive"
-            config.attention_window = [config.attention_window] * config.num_hidden_layers  # one value per layer
-        else:
-            assert len(config.attention_window) == config.num_hidden_layers, (
-                "`len(config.attention_window)` should equal `config.num_hidden_layers`. "
-                f"Expected {config.num_hidden_layers}, given {len(config.attention_window)}"
-            )
-
-        for i, layer in enumerate(self.encoder.layer):
-            # replace the `modeling_bert.BertSelfAttention` object with `LongformerSelfAttention`
-            layer.attention.self = LongformerSelfAttention(config, layer_id=i)
-
-        self.init_weights()
-
-    def _pad_to_window_size(
-        self,
-        input_ids: torch.Tensor,
-        attention_mask: torch.Tensor,
-        token_type_ids: torch.Tensor,
-        position_ids: torch.Tensor,
-        inputs_embeds: torch.Tensor,
-        attention_window: int,
-        pad_token_id: int,
-    ):
-        """A helper function to pad tokens and mask to work with implementation of Longformer selfattention."""
-
-        assert attention_window % 2 == 0, f"`attention_window` should be an even value. Given {attention_window}"
-        input_shape = input_ids.shape if input_ids is not None else inputs_embeds.shape
-        batch_size, seqlen = input_shape[:2]
-
-        padding_len = (attention_window - seqlen % attention_window) % attention_window
-        if padding_len > 0:
-            logger.info(
-                "Input ids are automatically padded from {} to {} to be a multiple of `config.attention_window`: {}".format(
-                    seqlen, seqlen + padding_len, attention_window
-                )
-            )
-            if input_ids is not None:
-                input_ids = F.pad(input_ids, (0, padding_len), value=pad_token_id)
-            if attention_mask is not None:
-                attention_mask = F.pad(
-                    attention_mask, (0, padding_len), value=False
-                )  # no attention on the padding tokens
-            if token_type_ids is not None:
-                token_type_ids = F.pad(token_type_ids, (0, padding_len), value=0)  # pad with token_type_id = 0
-            if position_ids is not None:
-                # pad with position_id = pad_token_id as in modeling_roberta.RobertaEmbeddings
-                position_ids = F.pad(position_ids, (0, padding_len), value=pad_token_id)
-            if inputs_embeds is not None:
-                input_ids_padding = inputs_embeds.new_full(
-                    (batch_size, padding_len), self.config.pad_token_id, dtype=torch.long,
-                )
-                inputs_embeds_padding = self.embeddings(input_ids_padding)
-                inputs_embeds = torch.cat([inputs_embeds, inputs_embeds_padding], dim=-2)
-
-        return padding_len, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds
-
-    @add_start_docstrings_to_callable(LONGFORMER_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        inputs_embeds=None,
-        masked_lm_labels=None,
-    ):
-        r"""
-
-    Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
-        masked_lm_loss (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
-            Masked language modeling loss.
-        prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
-            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
-            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
-            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
-            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
-            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-
-    Examples::
-
-        import torch
-        from transformers import LongformerModel, LongformerTokenizer
-
-        model = LongformerModel.from_pretrained('longformer-base-4096')
-        tokenizer = LongformerTokenizer.from_pretrained('longformer-base-4096')
-
-        SAMPLE_TEXT = ' '.join(['Hello world! '] * 1000)  # long input document
-        input_ids = torch.tensor(tokenizer.encode(SAMPLE_TEXT)).unsqueeze(0)  # batch of size 1
-
-        # Attention mask values -- 0: no attention, 1: local attention, 2: global attention
-        attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=input_ids.device) # initialize to local attention
-        attention_mask[:, [1, 4, 21,]] = 2  # Set global attention based on the task. For example,
-                                            # classification: the <s> token
-                                            # QA: question tokens
-                                            # LM: potentially on the beginning of sentences and paragraphs
-        sequence_output, pooled_output = model(input_ids, attention_mask=attention_mask)
-        """
-
-        # padding
-        attention_window = (
-            self.config.attention_window
-            if isinstance(self.config.attention_window, int)
-            else max(self.config.attention_window)
-        )
-        padding_len, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds = self._pad_to_window_size(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            attention_window=attention_window,
-            pad_token_id=self.config.pad_token_id,
-        )
-
-        # embed
-        output = super().forward(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=None,
-            inputs_embeds=inputs_embeds,
-            encoder_hidden_states=None,
-            encoder_attention_mask=None,
-        )
-
-        # undo padding
-        if padding_len > 0:
-            # `output` has the following tensors: sequence_output, pooled_output, (hidden_states), (attentions)
-            # `sequence_output`: unpad because the calling function is expecting a length == input_ids.size(1)
-            # `pooled_output`: independent of the sequence length
-            # `hidden_states`: mainly used for debugging and analysis, so keep the padding
-            # `attentions`: mainly used for debugging and analysis, so keep the padding
-            output = output[0][:, :-padding_len], *output[1:]
-
-        return output
-
-
-@add_start_docstrings("""Longformer Model with a `language modeling` head on top. """, LONGFORMER_START_DOCSTRING)
-class LongformerForMaskedLM(BertPreTrainedModel):
-    config_class = LongformerConfig
-    pretrained_model_archive_map = LONGFORMER_PRETRAINED_MODEL_ARCHIVE_MAP
-    base_model_prefix = "longformer"
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.longformer = LongformerModel(config)
-        self.lm_head = RobertaLMHead(config)
-
-        self.init_weights()
-
-    @add_start_docstrings_to_callable(LONGFORMER_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        inputs_embeds=None,
-        masked_lm_labels=None,
-    ):
-        r"""
-        masked_lm_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
-            Labels for computing the masked language modeling loss.
-            Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
-            Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
-            in ``[0, ..., config.vocab_size]``
-
-    Returns:
-        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
-        masked_lm_loss (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
-            Masked language modeling loss.
-        prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
-            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
-            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
-            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
-            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
-            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-
-    Examples::
-
-        import torch
-        from transformers import LongformerForMaskedLM, LongformerTokenizer
-
-        model = LongformerForMaskedLM.from_pretrained('longformer-base-4096')
-        tokenizer = LongformerTokenizer.from_pretrained('longformer-base-4096')
-
-        SAMPLE_TEXT = ' '.join(['Hello world! '] * 1000)  # long input document
-        input_ids = torch.tensor(tokenizer.encode(SAMPLE_TEXT)).unsqueeze(0)  # batch of size 1
-
-        attention_mask = None  # default is local attention everywhere, which is a good choice for MaskedLM
-                               # check ``LongformerModel.forward`` for more details how to set `attention_mask`
-        loss, prediction_scores = model(input_ids, attention_mask=attention_mask, masked_lm_labels=input_ids)
-        """
-
-        outputs = self.longformer(
-            input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-        )
-        sequence_output = outputs[0]
-        prediction_scores = self.lm_head(sequence_output)
-
-        outputs = (prediction_scores,) + outputs[2:]  # Add hidden states and attention if they are here
-
-        if masked_lm_labels is not None:
-            loss_fct = CrossEntropyLoss()
-            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1))
-            outputs = (masked_lm_loss,) + outputs
-
-        return outputs  # (masked_lm_loss), prediction_scores, (hidden_states), (attentions)