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: |

README.md

@@ -164,9 +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. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
-22. 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'2.9.1'
+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

@@ -108,4 +108,3 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
     model_doc/electra
     model_doc/dialogpt
     model_doc/reformer
-    model_doc/marian

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/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                                                                        |
@@ -296,12 +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>`_)                                                                           |
+| 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/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

@@ -166,7 +166,7 @@ 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 ***")
 
         # Loop to handle MNLI double evaluation (matched, mis-matched)
@@ -181,12 +181,11 @@ def main():
             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 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))
 
             results.update(result)
 

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/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/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/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/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

@@ -1,4 +1,4 @@
-# Bert-base Turkish Sentiment Model
+# Details
 
 https://huggingface.co/savasy/bert-base-turkish-sentiment-cased
 
@@ -7,9 +7,8 @@ This model is used for Sentiment Analysis, which is based on BERTurk for Turkish
 
 # Dataset
 
-The dataset is taken from the studies [2] and [3] and merged.
-
-* The study [2] gathered movie and product reviews. The products are book, DVD, electronics, and kitchen.
+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
@@ -19,70 +18,16 @@ 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 the study [1]
-
-* The study[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* 
-
-| *size*   | *data* |
-|--------|----|
-|   8000 |dev.tsv|
-|   8262 |test.tsv|
-|  32000 |train.tsv|
-|  *48290* |*total*|
+is 4.5.
 
 
 The dataset is used by following papers
+
  
 * 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)
-* [3] 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
 
-# Training
-
-```
-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
 
 ```
@@ -104,43 +49,3 @@ print(p)
 print (p[0]['label']=='LABEL_1')
 #False
 ```
-
-# Test your 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
- 
-> ...
-
-
-
-```
-from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
-
-f="/path/to/your/file/yourfile.tsv"
-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)
-
-i,crr=0,0
-for line in open(f):
- 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)
-```
-
-
-
-
-

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",
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+      "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",
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+      "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",
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+      "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",
+      "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorflow==2.1.0) (3.1.0)\n",
+      "Requirement already satisfied: protobuf>=3.8.0 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorflow==2.1.0) (3.11.4)\n",
+      "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorflow==2.1.0) (1.18.1)\n",
+      "Requirement already satisfied: tensorboard<2.2.0,>=2.1.0 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorflow==2.1.0) (2.1.0)\n",
+      "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",
+      "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorflow==2.1.0) (1.11.2)\n",
+      "Requirement already satisfied: six>=1.12.0 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorflow==2.1.0) (1.14.0)\n",
+      "Requirement already satisfied: tensorflow-estimator<2.2.0,>=2.1.0rc0 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorflow==2.1.0) (2.1.0)\n",
+      "Requirement already satisfied: scipy==1.4.1; python_version >= \"3\" in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorflow==2.1.0) (1.4.1)\n",
+      "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorflow==2.1.0) (0.1.8)\n",
+      "Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorflow==2.1.0) (0.34.2)\n",
+      "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorflow==2.1.0) (1.16.1)\n",
+      "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorflow==2.1.0) (0.9.0)\n",
+      "Requirement already satisfied: gast==0.2.2 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorflow==2.1.0) (0.2.2)\n",
+      "Requirement already satisfied: astor>=0.6.0 in /usr/local/Caskroom/miniconda/base/envs/huggingface/lib/python3.7/site-packages (from tensorflow==2.1.0) (0.8.0)\n",
+      "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": {
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@@ -558,5 +590,5 @@
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+ "nbformat_minor": 1
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notebooks/03-pipelines.ipynb

@@ -30,8 +30,7 @@
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@@ -2351,16 +2105,6 @@
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-    {
-      "cell_type": "markdown",
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-        "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",
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         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 83,
@@ -2505,13 +2232,14 @@
             "ad84da685cf44abb90d17d9d2e023b48",
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+        },
+        "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",
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@@ -2586,13 +2313,14 @@
             "405afa5bb8b840d8bc0850e02f593ce4",
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       "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",
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@@ -2670,13 +2397,14 @@
             "cd64e3f20b23483daa79712bde6622ea",
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-        }
+        },
+        "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",
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@@ -2756,13 +2483,14 @@
             "a35703cc8ff44e93a8c0eb413caddc40",
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-        }
+        },
+        "outputId": "3fb62e7a-25a6-4b06-ced8-51eb8aa6bf33"
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         "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",
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           "height": 88
-        }
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+        "outputId": "2168e437-cfba-4247-a38c-07f02f555c6e"
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       "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",
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@@ -2917,14 +2644,15 @@
             "ad78042ee71a41fd989e4b4ce9d2e3c1",
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-        }
+        },
+        "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",
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@@ -2982,14 +2709,15 @@
             "4486f8a2efc34b9aab3864eb5ad2ba48",
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-        }
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+        "outputId": "278a3d5f-cc42-40bc-a9db-c92ec5a3a2f0"
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       "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",
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-          "base_uri": "https://localhost:8080/",
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-        "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",
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-            ]
-          },
-          "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": []
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-          "execution_count": 5
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       "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",
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@@ -3144,7 +2788,8 @@
             "31d97ecf78fa412c99e6659196d82828",
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           ]
-        }
+        },
+        "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",
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@@ -3228,7 +2872,8 @@
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             "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

@@ -4,23 +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) |

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 @ git+git://github.com/microsoft/onnxconverter-common.git@f64ca15989b6dc95a1f3507ff6e4c395ba12dff5#egg=onnxconverter-common",
-    "keras2onnx @ git+git://github.com/onnx/keras-onnx.git@cbdc75cb950b16db7f0a67be96a278f8d2953b48#egg=keras2onnx"
-]
-extras["tf-cpu"] = [
-    "tensorflow-cpu",
-    "onnxconverter-common @ git+git://github.com/microsoft/onnxconverter-common.git@f64ca15989b6dc95a1f3507ff6e4c395ba12dff5#egg=onnxconverter-common",
-    "keras2onnx @ git+git://github.com/onnx/keras-onnx.git@cbdc75cb950b16db7f0a67be96a278f8d2953b48#egg=keras2onnx"
-]
+extras["tf"] = ["tensorflow<=2.1.0"]
+extras["tf-cpu"] = ["tensorflow-cpu<=2.1.0"]
 extras["torch"] = ["torch"]
 
 extras["serving"] = ["pydantic", "uvicorn", "fastapi", "starlette"]
@@ -91,11 +81,11 @@ extras["quality"] = [
     "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="2.9.1",
+    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",

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__ = "2.9.1"
+__version__ = "2.9.0"
 
 # Work around to update TensorFlow's absl.logging threshold which alters the
 # default Python logging output behavior when present.
@@ -248,7 +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,
@@ -359,7 +359,6 @@ if is_tf_available():
     from .modeling_tf_auto import (
         TFAutoModel,
         TFAutoModelForPreTraining,
-        TFAutoModelForMultipleChoice,
         TFAutoModelForSequenceClassification,
         TFAutoModelForQuestionAnswering,
         TFAutoModelWithLMHead,
@@ -494,7 +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

@@ -28,7 +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_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
@@ -74,7 +73,6 @@ CONFIG_MAPPING = OrderedDict(
         ("albert", AlbertConfig,),
         ("camembert", CamembertConfig,),
         ("xlm-roberta", XLMRobertaConfig,),
-        ("marian", MarianConfig,),
         ("bart", BartConfig,),
         ("reformer", ReformerConfig,),
         ("roberta", RobertaConfig,),

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, 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

@@ -114,7 +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):

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/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/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

@@ -39,7 +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,
@@ -99,7 +98,6 @@ from .modeling_flaubert import (
     FlaubertWithLMHeadModel,
 )
 from .modeling_gpt2 import GPT2_PRETRAINED_MODEL_ARCHIVE_MAP, GPT2LMHeadModel, GPT2Model
-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 (
@@ -216,7 +214,6 @@ MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
         (AlbertConfig, AlbertForMaskedLM),
         (CamembertConfig, CamembertForMaskedLM),
         (XLMRobertaConfig, XLMRobertaForMaskedLM),
-        (MarianConfig, MarianMTModel),
         (BartConfig, BartForConditionalGeneration),
         (RobertaConfig, RobertaForMaskedLM),
         (BertConfig, BertForMaskedLM),
@@ -906,7 +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

@@ -19,7 +19,6 @@
 import logging
 import math
 import os
-from functools import partial
 
 import torch
 from torch import nn
@@ -154,7 +153,7 @@ class BertEmbeddings(nn.Module):
         # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
         # any TensorFlow checkpoint file
         self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = partial(nn.functional.dropout, p=config.hidden_dropout_prob)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
     def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
         if input_ids is not None:
@@ -177,7 +176,7 @@ class BertEmbeddings(nn.Module):
 
         embeddings = inputs_embeds + position_embeddings + token_type_embeddings
         embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings, training=self.training)
+        embeddings = self.dropout(embeddings)
         return embeddings
 
 
@@ -194,26 +193,17 @@ class BertSelfAttention(nn.Module):
         self.num_attention_heads = config.num_attention_heads
         self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
         self.all_head_size = self.num_attention_heads * self.attention_head_size
-        self.scale_factor = 1. / math.sqrt(self.attention_head_size)
 
         self.query = nn.Linear(config.hidden_size, self.all_head_size)
         self.key = nn.Linear(config.hidden_size, self.all_head_size)
         self.value = nn.Linear(config.hidden_size, self.all_head_size)
 
-        self.qkv_weight = nn.Parameter(torch.cat(
-            (self.query.weight, self.key.weight, self.value.weight), dim=0
-        ).t().contiguous())
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
 
-        self.qkv_bias = nn.Parameter(
-            torch.cat((self.query.bias, self.key.bias, self.value.bias), dim=0)
-        )
-
-        self.dropout = partial(nn.functional.dropout, p=config.attention_probs_dropout_prob)
-
-    # def transpose_for_scores(self, x):
-    #     new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-    #     x = x.view(*new_x_shape)
-    #     return x.permute(0, 2, 1, 3)
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
 
     def forward(
         self,
@@ -223,45 +213,49 @@ class BertSelfAttention(nn.Module):
         encoder_hidden_states=None,
         encoder_attention_mask=None,
     ):
+        mixed_query_layer = self.query(hidden_states)
+
         # If this is instantiated as a cross-attention module, the keys
         # and values come from an encoder; the attention mask needs to be
         # such that the encoder's padding tokens are not attended to.
         if encoder_hidden_states is not None:
-            mixed_query_layer = self.query(hidden_states)
             mixed_key_layer = self.key(encoder_hidden_states)
             mixed_value_layer = self.value(encoder_hidden_states)
             attention_mask = encoder_attention_mask
         else:
-            # Compute qkv_bias + (hidden_states @ qkv_weight)
-            qkv = torch.matmul(hidden_states, self.qkv_weight) + self.qkv_bias
-            mixed_query_layer, mixed_key_layer, mixed_value_layer = qkv.chunk(3, dim=2)
+            mixed_key_layer = self.key(hidden_states)
+            mixed_value_layer = self.value(hidden_states)
 
-        attention_scores = torch.bmm(mixed_query_layer, mixed_key_layer.transpose(2, 1))
-        attention_scores = attention_scores * self.scale_factor
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+        key_layer = self.transpose_for_scores(mixed_key_layer)
+        value_layer = self.transpose_for_scores(mixed_value_layer)
 
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
         if attention_mask is not None:
             # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
-            attention_scores = attention_scores + attention_mask.squeeze(2)
+            attention_scores = attention_scores + attention_mask
 
         # Normalize the attention scores to probabilities.
-        attention_probs = torch.softmax(attention_scores, dim=-1)
+        attention_probs = nn.Softmax(dim=-1)(attention_scores)
 
         # This is actually dropping out entire tokens to attend to, which might
         # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs = self.dropout(attention_probs, training=self.training)
+        attention_probs = self.dropout(attention_probs)
 
         # Mask heads if we want to
         if head_mask is not None:
-            bs = attention_probs.size(0)
-
-            # Split attention_probs
-            attention_probs = attention_probs.view(bs, -1, self.num_attention_heads, self.attention_head_size)
             attention_probs = attention_probs * head_mask
-            attention_probs = attention_probs.flatten(2)
 
-        context_layer = torch.bmm(attention_probs, mixed_value_layer)
+        context_layer = torch.matmul(attention_probs, value_layer)
 
-        return (context_layer, attention_probs) if self.output_attentions else (context_layer,)
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(*new_context_layer_shape)
+
+        outputs = (context_layer, attention_probs) if self.output_attentions else (context_layer,)
+        return outputs
 
 
 class BertSelfOutput(nn.Module):
@@ -269,11 +263,11 @@ class BertSelfOutput(nn.Module):
         super().__init__()
         self.dense = nn.Linear(config.hidden_size, config.hidden_size)
         self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = partial(nn.functional.dropout, p=config.hidden_dropout_prob)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
     def forward(self, hidden_states, input_tensor):
         hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states, training=self.training)
+        hidden_states = self.dropout(hidden_states)
         hidden_states = self.LayerNorm(hidden_states + input_tensor)
         return hidden_states
 
@@ -320,7 +314,8 @@ class BertAttention(nn.Module):
             hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask
         )
         attention_output = self.output(self_outputs[0], hidden_states)
-        return (attention_output,) + self_outputs[1:]  # add attentions if we output them
+        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
+        return outputs
 
 
 class BertIntermediate(nn.Module):
@@ -343,11 +338,11 @@ class BertOutput(nn.Module):
         super().__init__()
         self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
         self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = partial(nn.functional.dropout, p=config.hidden_dropout_prob)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
     def forward(self, hidden_states, input_tensor):
         hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states, training=self.training)
+        hidden_states = self.dropout(hidden_states)
         hidden_states = self.LayerNorm(hidden_states + input_tensor)
         return hidden_states
 
@@ -432,13 +427,15 @@ class BertPooler(nn.Module):
     def __init__(self, config):
         super().__init__()
         self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
 
     def forward(self, hidden_states):
         # We "pool" the model by simply taking the hidden state corresponding
         # to the first token.
         first_token_tensor = hidden_states[:, 0]
         pooled_output = self.dense(first_token_tensor)
-        return torch.tanh(pooled_output)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
 
 
 class BertPredictionHeadTransform(nn.Module):
@@ -706,7 +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_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_marian.py

@@ -18,33 +18,18 @@
 from transformers.modeling_bart import BartForConditionalGeneration
 
 
+PRETRAINED_MODEL_ARCHIVE_MAP = {
+    "opus-mt-en-de": "https://cdn.huggingface.co/Helsinki-NLP/opus-mt-en-de/pytorch_model.bin",
+}
+
+
 class MarianMTModel(BartForConditionalGeneration):
-    r"""
-    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>`__
+    """Pytorch version of marian-nmt's transformer.h (c++). Designed for the OPUS-NMT translation checkpoints.
+    Model API is identical to BartForConditionalGeneration"""
 
-    Examples::
+    pretrained_model_archive_map = PRETRAINED_MODEL_ARCHIVE_MAP
 
-        from transformers import MarianTokenizer, MarianMTModel
-        from typing import List
-        src = 'fr'  # source language
-        trg = 'en'  # target language
-        sample_text = "où est l'arrêt de bus ?"
-        mname = f'Helsinki-NLP/opus-mt-{src}-{trg}'
-
-        model = MarianMTModel.from_pretrained(mname)
-        tok = MarianTokenizer.from_pretrained(mname)
-        batch = tok.prepare_translation_batch(src_texts=[sample_text])  # don't need tgt_text for inference
-        gen = model.generate(**batch)  # for forward pass: model(**batch)
-        words: List[str] = tok.batch_decode(gen, skip_special_tokens=True)  # returns "Where is the the bus stop ?"
-
-    """
-
-    pretrained_model_archive_map = {}  # see https://huggingface.co/models?search=Helsinki-NLP
-
-    def prepare_logits_for_generation(self, logits, cur_len, max_length):
-        logits[:, self.config.pad_token_id] = float("-inf")
+    def prepare_scores_for_generation(self, scores, cur_len, max_length):
         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

src/transformers/modeling_reformer.py

@@ -36,8 +36,7 @@ from .modeling_utils import PreTrainedModel, apply_chunking_to_forward
 logger = logging.getLogger(__name__)
 
 REFORMER_PRETRAINED_MODEL_ARCHIVE_MAP = {
-    "google/reformer-crime-and-punishment": "https://cdn.huggingface.co/google/reformer-crime-and-punishment/pytorch_model.bin",
-    "google/reformer-enwik8": "https://cdn.huggingface.co/google/reformer-enwik8/pytorch_model.bin",
+    "google/reformer-crime-and-punishment": "https://cdn.huggingface.co/google/reformer-crime-and-punishment/pytorch_model.bin"
 }
 
 
@@ -562,8 +561,8 @@ class LSHSelfAttention(nn.Module, EfficientAttentionMixin):
 
         # get correct mask values depending on precision
         if query_key_dots.dtype == torch.float16:
-            self_mask_value = self.self_mask_value_float16.half()
-            mask_value = self.mask_value_float16.half()
+            self_mask_value = self.self_mask_value_float16
+            mask_value = self.mask_value_float16
         else:
             self_mask_value = self.self_mask_value_float32
             mask_value = self.mask_value_float32
@@ -834,7 +833,7 @@ class LocalSelfAttention(nn.Module, EfficientAttentionMixin):
         if mask is not None:
             # get mask tensor depending on half precision or not
             if query_key_dots.dtype == torch.float16:
-                mask_value = self.mask_value_float16.half()
+                mask_value = self.mask_value_float16
             else:
                 mask_value = self.mask_value_float32
 

src/transformers/modeling_t5.py

@@ -149,12 +149,8 @@ class T5LayerNorm(nn.Module):
         self.variance_epsilon = eps
 
     def forward(self, x):
-        # layer norm should always be calculated in float32
-        variance = x.to(torch.float32).pow(2).mean(-1, keepdim=True)
+        variance = x.pow(2).mean(-1, keepdim=True)
         x = x / torch.sqrt(variance + self.variance_epsilon)
-
-        if self.weight.dtype == torch.float16:
-            x = x.to(torch.float16)
         return self.weight * x
 
 
@@ -695,16 +691,14 @@ class T5Stack(T5PreTrainedModel):
             attention_mask = torch.ones(batch_size, mask_seq_length).to(inputs_embeds.device)
         if self.is_decoder and encoder_attention_mask is None and encoder_hidden_states is not None:
             encoder_seq_length = encoder_hidden_states.shape[1]
-            encoder_attention_mask = torch.ones(
-                batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long
-            )
+            encoder_attention_mask = torch.ones(batch_size, encoder_seq_length).to(inputs_embeds.device)
 
         # initialize past_key_value_states with `None` if past does not exist
         if past_key_value_states is None:
             past_key_value_states = [None] * len(self.block)
 
         # ourselves in which case we just need to make it broadcastable to all heads.
-        extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, inputs_embeds.device)
+        extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, self.device)
 
         if self.is_decoder and encoder_attention_mask is not None:
             encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
@@ -739,7 +733,6 @@ class T5Stack(T5PreTrainedModel):
             # layer_outputs is a tuple with:
             # hidden-states, key-value-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
             hidden_states, present_key_value_state = layer_outputs[:2]
-
             if i == 0:
                 # We share the position biases between the layers - the first layer store them
                 # layer_outputs = hidden-states, key-value-states (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)

src/transformers/modeling_tf_albert.py

@@ -21,7 +21,7 @@ import logging
 import tensorflow as tf
 
 from .configuration_albert import AlbertConfig
-from .file_utils import MULTIPLE_CHOICE_DUMMY_INPUTS, add_start_docstrings, add_start_docstrings_to_callable
+from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
 from .modeling_tf_bert import ACT2FN, TFBertSelfAttention
 from .modeling_tf_utils import TFPreTrainedModel, get_initializer, keras_serializable, shape_list
 from .tokenization_utils import BatchEncoding
@@ -957,127 +957,3 @@ class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel):
         outputs = (start_logits, end_logits,) + outputs[2:]
 
         return outputs  # start_logits, end_logits, (hidden_states), (attentions)
-
-
-@add_start_docstrings(
-    """Albert Model with a multiple choice classification head on top (a linear layer on top of
-    the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
-    ALBERT_START_DOCSTRING,
-)
-class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel):
-    def __init__(self, config, *inputs, **kwargs):
-        super().__init__(config, *inputs, **kwargs)
-
-        self.albert = TFAlbertMainLayer(config, name="albert")
-        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
-        self.classifier = tf.keras.layers.Dense(
-            1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
-        )
-
-    @property
-    def dummy_inputs(self):
-        """ Dummy inputs to build the network.
-
-        Returns:
-            tf.Tensor with dummy inputs
-        """
-        return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}
-
-    @add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING)
-    def call(
-        self,
-        inputs,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        training=False,
-    ):
-        r"""
-    Return:
-        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
-        classification_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices)`:
-            `num_choices` is the size of the second dimension of the input tensors. (see `input_ids` above).
-
-            Classification scores (before SoftMax).
-        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
-            tuple of :obj:`tf.Tensor` (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(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
-            tuple of :obj:`tf.Tensor` (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 tensorflow as tf
-        from transformers import AlbertTokenizer, TFAlbertForMultipleChoice
-
-        tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
-        model = TFAlbertForMultipleChoice.from_pretrained('albert-base-v2')
-
-        example1 = ["This is a context", "Is it a context? Yes"]
-        example2 = ["This is a context", "Is it a context? No"]
-        encoding = tokenizer.batch_encode_plus([example1, example2], return_tensors='tf', truncation_strategy="only_first", pad_to_max_length=True, max_length=128)
-        outputs = model(encoding["input_ids"][None, :])
-        logits = outputs[0]
-
-        """
-        if isinstance(inputs, (tuple, list)):
-            input_ids = inputs[0]
-            attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
-            token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
-            position_ids = inputs[3] if len(inputs) > 3 else position_ids
-            head_mask = inputs[4] if len(inputs) > 4 else head_mask
-            inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
-            assert len(inputs) <= 6, "Too many inputs."
-        elif isinstance(inputs, dict):
-            print("isdict(1)")
-            input_ids = inputs.get("input_ids")
-            print(input_ids)
-
-            attention_mask = inputs.get("attention_mask", attention_mask)
-            token_type_ids = inputs.get("token_type_ids", token_type_ids)
-            position_ids = inputs.get("position_ids", position_ids)
-            head_mask = inputs.get("head_mask", head_mask)
-            inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
-            assert len(inputs) <= 6, "Too many inputs."
-        else:
-            input_ids = inputs
-
-        if input_ids is not None:
-            num_choices = shape_list(input_ids)[1]
-            seq_length = shape_list(input_ids)[2]
-        else:
-            num_choices = shape_list(inputs_embeds)[1]
-            seq_length = shape_list(inputs_embeds)[2]
-
-        flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
-        flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
-        flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
-        flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
-
-        flat_inputs = [
-            flat_input_ids,
-            flat_attention_mask,
-            flat_token_type_ids,
-            flat_position_ids,
-            head_mask,
-            inputs_embeds,
-        ]
-
-        outputs = self.albert(flat_inputs, training=training)
-
-        pooled_output = outputs[1]
-
-        pooled_output = self.dropout(pooled_output, training=training)
-        logits = self.classifier(pooled_output)
-        reshaped_logits = tf.reshape(logits, (-1, num_choices))
-
-        outputs = (reshaped_logits,) + outputs[2:]  # add hidden states and attention if they are here
-
-        return outputs  # reshaped_logits, (hidden_states), (attentions)

src/transformers/modeling_tf_auto.py

@@ -36,7 +36,6 @@ from .configuration_utils import PretrainedConfig
 from .modeling_tf_albert import (
     TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
     TFAlbertForMaskedLM,
-    TFAlbertForMultipleChoice,
     TFAlbertForPreTraining,
     TFAlbertForQuestionAnswering,
     TFAlbertForSequenceClassification,
@@ -45,7 +44,6 @@ from .modeling_tf_albert import (
 from .modeling_tf_bert import (
     TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
     TFBertForMaskedLM,
-    TFBertForMultipleChoice,
     TFBertForPreTraining,
     TFBertForQuestionAnswering,
     TFBertForSequenceClassification,
@@ -174,10 +172,6 @@ TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
     ]
 )
 
-TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING = OrderedDict(
-    [(BertConfig, TFBertForMultipleChoice), (AlbertConfig, TFAlbertForMultipleChoice)]
-)
-
 TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
     [
         (DistilBertConfig, TFDistilBertForQuestionAnswering),
@@ -668,153 +662,6 @@ class TFAutoModelWithLMHead(object):
         )
 
 
-class TFAutoModelForMultipleChoice:
-    r"""
-        :class:`~transformers.TFAutoModelForMultipleChoice` is a generic model class
-        that will be instantiated as one of the multiple choice model classes of the library
-        when created with the `TFAutoModelForMultipleChoice.from_pretrained(pretrained_model_name_or_path)`
-        class method.
-
-        The `from_pretrained()` method takes care of returning the correct model class instance
-        based on the `model_type` property of the config object, or when it's missing,
-        falling back to using pattern matching on the `pretrained_model_name_or_path` string.
-
-        The model class to instantiate is selected as the first pattern matching
-        in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `albert`: TFAlbertForMultipleChoice (Albert model)
-            - contains `bert`: TFBertForMultipleChoice (Bert model)
-
-        This class cannot be instantiated using `__init__()` (throws an error).
-    """
-
-    def __init__(self):
-        raise EnvironmentError(
-            "TFAutoModelForMultipleChoice is designed to be instantiated "
-            "using the `TFAutoModelForMultipleChoice.from_pretrained(pretrained_model_name_or_path)` or "
-            "`TFAutoModelForMultipleChoice.from_config(config)` methods."
-        )
-
-    @classmethod
-    def from_config(cls, config):
-        r""" Instantiates one of the base model classes of the library
-        from a configuration.
-
-            config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
-                The model class to instantiate is selected based on the configuration class:
-                    - isInstance of `albert` configuration class: AlbertModel (Albert model)
-                    - isInstance of `bert` configuration class: BertModel (Bert model)
-
-        Examples::
-
-            config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
-            model = AutoModelForMulitpleChoice.from_config(config)  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
-        """
-        for config_class, model_class in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.items():
-            if isinstance(config, config_class):
-                return model_class(config)
-        raise ValueError(
-            "Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
-            "Model type should be one of {}.".format(
-                config.__class__,
-                cls.__name__,
-                ", ".join(c.__name__ for c in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys()),
-            )
-        )
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
-        r""" Instantiates one of the multiple choice model classes of the library
-        from a pre-trained model configuration.
-
-        The `from_pretrained()` method takes care of returning the correct model class instance
-        based on the `model_type` property of the config object, or when it's missing,
-        falling back to using pattern matching on the `pretrained_model_name_or_path` string.
-
-        The model class to instantiate is selected as the first pattern matching
-        in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `albert`: TFRobertaForMultiple (Albert model)
-            - contains `bert`: TFBertForMultipleChoice (Bert model)
-
-        The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
-        To train the model, you should first set it back in training mode with `model.train()`
-
-        Params:
-            pretrained_model_name_or_path: either:
-
-                - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
-                - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
-                - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
-                - a path or url to a `PyTorch, TF 1.X or TF 2.0 checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In the case of a PyTorch checkpoint, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument.
-
-            from_pt: (`Optional`) Boolean
-                Set to True if the Checkpoint is a PyTorch checkpoint.
-
-            model_args: (`optional`) Sequence of positional arguments:
-                All remaning positional arguments will be passed to the underlying model's ``__init__`` method
-
-            config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
-                Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
-
-                - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
-                - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
-                - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
-
-            state_dict: (`optional`) dict:
-                an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
-                This option can be used if you want to create a model from a pretrained configuration but load your own weights.
-                In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
-
-            cache_dir: (`optional`) string:
-                Path to a directory in which a downloaded pre-trained model
-                configuration should be cached if the standard cache should not be used.
-
-            force_download: (`optional`) boolean, default False:
-                Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
-
-            resume_download: (`optional`) boolean, default False:
-                Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
-
-            proxies: (`optional`) dict, default None:
-                A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
-                The proxies are used on each request.
-
-            output_loading_info: (`optional`) boolean:
-                Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
-
-            kwargs: (`optional`) Remaining dictionary of keyword arguments:
-                Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
-
-                - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
-                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
-
-        Examples::
-
-            model = TFAutoModelFormultipleChoice.from_pretrained('bert-base-uncased')    # Download model and configuration from S3 and cache.
-            model = TFAutoModelFormultipleChoice.from_pretrained('./test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
-            model = TFAutoModelFormultipleChoice.from_pretrained('bert-base-uncased', output_attention=True)  # Update configuration during loading
-            assert model.config.output_attention == True
-            # Loading from a TF checkpoint file instead of a PyTorch model (slower)
-            config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
-            model = TFAutoModelFormultipleChoice.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=True, config=config)
-
-        """
-        config = kwargs.pop("config", None)
-        if not isinstance(config, PretrainedConfig):
-            config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-
-        for config_class, model_class in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.items():
-            if isinstance(config, config_class):
-                return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
-        raise ValueError(
-            "Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
-            "Model type should be one of {}.".format(
-                config.__class__,
-                cls.__name__,
-                ", ".join(c.__name__ for c in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys()),
-            )
-        )
-
-
 class TFAutoModelForSequenceClassification(object):
     r"""
         :class:`~transformers.TFAutoModelForSequenceClassification` is a generic model class

src/transformers/modeling_tf_t5.py

@@ -537,7 +537,7 @@ class TFT5MainLayer(tf.keras.layers.Layer):
 
     def call(
         self,
-        inputs,
+        input_ids,
         attention_mask=None,
         encoder_hidden_states=None,
         encoder_attention_mask=None,
@@ -548,19 +548,19 @@ class TFT5MainLayer(tf.keras.layers.Layer):
         training=False,
     ):
 
-        if inputs is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both inputs and inputs_embeds at the same time")
-        elif inputs is not None:
-            input_shape = shape_list(inputs)
-            inputs = tf.reshape(inputs, (-1, input_shape[-1]))
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            input_shape = shape_list(input_ids)
+            input_ids = tf.reshape(input_ids, (-1, input_shape[-1]))
         elif inputs_embeds is not None:
             input_shape = shape_list(inputs_embeds)[:-1]
         else:
-            raise ValueError("You have to specify either inputs or inputs_embeds")
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
 
         if inputs_embeds is None:
             assert self.embed_tokens is not None, "You have to intialize the model with valid token embeddings"
-            inputs_embeds = self.embed_tokens(inputs)
+            inputs_embeds = self.embed_tokens(input_ids)
 
         batch_size, seq_length = input_shape
 
@@ -725,11 +725,11 @@ class TFT5PreTrainedModel(TFPreTrainedModel):
 
     @property
     def dummy_inputs(self):
-        inputs = tf.constant(DUMMY_INPUTS)
+        input_ids = tf.constant(DUMMY_INPUTS)
         input_mask = tf.constant(DUMMY_MASK)
         dummy_inputs = {
-            "inputs": inputs,
-            "decoder_input_ids": inputs,
+            "inputs": input_ids,
+            "decoder_input_ids": input_ids,
             "decoder_attention_mask": input_mask,
         }
         return dummy_inputs
@@ -759,11 +759,11 @@ T5_START_DOCSTRING = r"""    The T5 model was proposed in
 
         If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :
 
-        - a single Tensor with inputs only and nothing else: `model(inputs_ids)
+        - a single Tensor with input_ids only and nothing else: `model(inputs_ids)
         - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
-            `model([inputs, attention_mask])` or `model([inputs, attention_mask, token_type_ids])`
+            `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
         - a dictionary with one or several input Tensors associaed to the input names given in the docstring:
-            `model({'inputs': inputs, 'token_type_ids': token_type_ids})`
+            `model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
 
     Parameters:
         config (:class:`~transformers.T5Config`): Model configuration class with all the parameters of the model.
@@ -780,7 +780,7 @@ T5_INPUTS_DOCSTRING = r"""
             T5 is a model with relative position embeddings so you should be able to pad the inputs on
             the right or the left.
             Indices can be obtained using :class:`transformers.T5Tokenizer`.
-            To know more on how to prepare :obj:`inputs` for pre-training take a look at
+            To know more on how to prepare :obj:`input_ids` for pre-training take a look at
             `T5 Training <./t5.html#training>`_ .
             See :func:`transformers.PreTrainedTokenizer.encode` and
             :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
@@ -805,8 +805,8 @@ T5_INPUTS_DOCSTRING = r"""
         use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
             If `use_cache` is True, `decoder_past_key_value_states` are returned and can be used to speed up decoding (see `decoder_past_key_value_states`).
         inputs_embeds (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
-            Optionally, instead of passing :obj:`inputs` you can choose to directly pass an embedded representation.
-            This is useful if you want more control over how to convert `inputs` indices into associated vectors
+            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.
         decoder_inputs_embeds (:obj:`tf.Tensor` of shape :obj:`(batch_size, target_sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
             Optionally, instead of passing :obj:`decoder_input_ids` you can choose to directly pass an embedded representation.
@@ -885,8 +885,8 @@ class TFT5Model(TFT5PreTrainedModel):
 
         tokenizer = T5Tokenizer.from_pretrained('t5-small')
         model = TFT5Model.from_pretrained('t5-small')
-        inputs = tokenizer.encode("Hello, my dog is cute", return_tensors="tf")  # Batch size 1
-        outputs = model(inputs, decoder_input_ids=inputs)
+        input_ids = tokenizer.encode("Hello, my dog is cute", return_tensors="tf")  # Batch size 1
+        outputs = model(input_ids, decoder_input_ids=input_ids)
         last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
 
         """
@@ -897,7 +897,7 @@ class TFT5Model(TFT5PreTrainedModel):
             kwargs["inputs"] = inputs
 
         # retrieve arguments
-        inputs = kwargs.get("inputs", None)
+        input_ids = kwargs.get("inputs", None)
         inputs_embeds = kwargs.get("inputs_embeds", None)
         attention_mask = kwargs.get("attention_mask", None)
         encoder_outputs = kwargs.get("encoder_outputs", None)
@@ -911,7 +911,7 @@ class TFT5Model(TFT5PreTrainedModel):
         # Encode if needed (training, first prediction pass)
         if encoder_outputs is None:
             encoder_outputs = self.encoder(
-                inputs, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask,
+                input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask,
             )
 
         hidden_states = encoder_outputs[0]
@@ -1006,14 +1006,14 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel):
 
         tokenizer = T5Tokenizer.from_pretrained('t5-small')
         model = TFT5ForConditionalGeneration.from_pretrained('t5-small')
-        inputs = tokenizer.encode("Hello, my dog is cute", return_tensors="tf")  # Batch size 1
-        outputs = model(inputs, decoder_input_ids=inputs)
+        input_ids = tokenizer.encode("Hello, my dog is cute", return_tensors="tf")  # Batch size 1
+        outputs = model(input_ids, decoder_input_ids=input_ids)
         prediction_scores = outputs[0]
 
         tokenizer = T5Tokenizer.from_pretrained('t5-small')
         model = TFT5ForConditionalGeneration.from_pretrained('t5-small')
-        inputs = tokenizer.encode("summarize: Hello, my dog is cute", return_tensors="tf")  # Batch size 1
-        model.generate(inputs)
+        input_ids = tokenizer.encode("summarize: Hello, my dog is cute", return_tensors="tf")  # Batch size 1
+        model.generate(input_ids)
 
         """
 
@@ -1023,7 +1023,7 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel):
             kwargs["inputs"] = inputs
 
         # retrieve arguments
-        inputs = kwargs.get("inputs", None)
+        input_ids = kwargs.get("inputs", None)
         decoder_input_ids = kwargs.get("decoder_input_ids", None)
         attention_mask = kwargs.get("attention_mask", None)
         encoder_outputs = kwargs.get("encoder_outputs", None)
@@ -1038,7 +1038,7 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel):
         if encoder_outputs is None:
             # Convert encoder inputs in embeddings if needed
             encoder_outputs = self.encoder(
-                inputs, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask,
+                input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask,
             )
 
         hidden_states = encoder_outputs[0]
@@ -1076,7 +1076,7 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel):
 
         return decoder_outputs + encoder_outputs
 
-    def prepare_inputs_for_generation(self, inputs, past, attention_mask, use_cache, **kwargs):
+    def prepare_inputs_for_generation(self, input_ids, past, attention_mask, use_cache, **kwargs):
         assert past is not None, "past has to be defined for encoder_outputs"
 
         # first step
@@ -1087,7 +1087,7 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel):
 
         return {
             "inputs": None,  # inputs don't have to be defined, but still need to be passed to make Keras.layer.__call__ happy
-            "decoder_input_ids": inputs,  # inputs are the decoder_input_ids
+            "decoder_input_ids": input_ids,  # input_ids are the decoder_input_ids
             "decoder_past_key_value_states": decoder_past_key_value_states,
             "encoder_outputs": encoder_outputs,
             "attention_mask": attention_mask,

src/transformers/modeling_tf_utils.py

@@ -929,9 +929,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
             else:
                 tokens_to_add = next_token
 
-            # add token and increase length by one
             input_ids = tf.concat([input_ids, tf.expand_dims(tokens_to_add, -1)], 1)
-            cur_len = cur_len + 1
 
             if eos_token_id is not None:
                 eos_in_sents = tokens_to_add == eos_token_id
@@ -957,6 +955,8 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
                     [attention_mask, tf.ones((shape_list(attention_mask)[0], 1), dtype=tf.int32)], axis=-1
                 )
 
+            cur_len = cur_len + 1
+
         # if there are different sentences lengths in the batch, some batches have to be padded
         min_sent_length = tf.math.reduce_min(sent_lengths)
         max_sent_length = tf.math.reduce_max(sent_lengths)
@@ -970,7 +970,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
                 tf.expand_dims(sent_lengths, -1), [batch_size, max_sent_length]
             )
             broad_casted_range = tf.transpose(
-                tf.broadcast_to(tf.expand_dims(tf.range(max_sent_length), -1), [max_sent_length, batch_size])
+                tf.broadcast_to(tf.expand_dims(tf.range(max_length), -1), [max_length, batch_size])
             )
 
             decoded = tf.where(broad_casted_range < broad_casted_sent_lengths, input_ids, padding)
@@ -1205,11 +1205,9 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
             beam_tokens = tf.convert_to_tensor([x[1] for x in next_batch_beam], dtype=tf.int32)
             beam_idx = tf.convert_to_tensor([x[2] for x in next_batch_beam], dtype=tf.int32)
 
-            # re-order batch and update current length
+            # re-order batch
             input_ids = tf.stack([tf.identity(input_ids[x, :]) for x in beam_idx])
             input_ids = tf.concat([input_ids, tf.expand_dims(beam_tokens, 1)], axis=-1)
-            cur_len = cur_len + 1
-
             # re-order internal states
             if past is not None:
                 past = self._reorder_cache(past, beam_idx)
@@ -1220,6 +1218,9 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
                     [attention_mask, tf.ones((shape_list(attention_mask)[0], 1), dtype=tf.int32)], axis=-1
                 )
 
+            # update current length
+            cur_len = cur_len + 1
+
         # finalize all open beam hypotheses and end to generated hypotheses
         for batch_idx in range(batch_size):
             # Add all open beam hypothesis to generated_hyps

src/transformers/modeling_utils.py

@@ -17,7 +17,7 @@
 import inspect
 import logging
 import os
-from typing import Callable, List, Tuple
+from typing import Callable, Tuple
 
 import torch
 from torch import Tensor, device, dtype, nn
@@ -110,33 +110,11 @@ class ModuleUtilsMixin:
 
     @property
     def device(self) -> device:
-        try:
-            return next(self.parameters()).device
-        except StopIteration:
-            # For nn.DataParallel compatibility in PyTorch 1.5
-
-            def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
-                tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
-                return tuples
-
-            gen = self._named_members(get_members_fn=find_tensor_attributes)
-            first_tuple = next(gen)
-            return first_tuple[1].device
+        return next(self.parameters()).device
 
     @property
     def dtype(self) -> dtype:
-        try:
-            return next(self.parameters()).dtype
-        except StopIteration:
-            # For nn.DataParallel compatibility in PyTorch 1.5
-
-            def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
-                tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
-                return tuples
-
-            gen = self._named_members(get_members_fn=find_tensor_attributes)
-            first_tuple = next(gen)
-            return first_tuple[1].dtype
+        return next(self.parameters()).dtype
 
     def invert_attention_mask(self, encoder_attention_mask: Tensor) -> Tensor:
         """type: torch.Tensor -> torch.Tensor"""
@@ -150,18 +128,7 @@ class ModuleUtilsMixin:
         # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
         # encoder_extended_attention_mask.transpose(-1, -2))
         encoder_extended_attention_mask = encoder_extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
-
-        if self.dtype == torch.float16:
-            encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e4
-        elif self.dtype == torch.float32:
-            encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e9
-        else:
-            raise ValueError(
-                "{} not recognized. `dtype` should be set to either `torch.float32` or `torch.float16`".format(
-                    self.dtype
-                )
-            )
-
+        encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e9
         return encoder_extended_attention_mask
 
     def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: tuple, device: device):
@@ -777,8 +744,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
     def prepare_inputs_for_generation(self, input_ids, **kwargs):
         return {"input_ids": input_ids}
 
-    def prepare_logits_for_generation(self, logits, **kwargs):
-        return logits
+    def prepare_scores_for_generation(self, scores, **kwargs):
+        return scores
 
     def _use_cache(self, outputs, use_cache):
         """During generation, decide whether to pass the `past` variable to the next forward pass."""
@@ -890,7 +857,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
                 ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
                 Defaults to `None`.
 
-                `What are attention masks? <../glossary.html#attention-mask>`__
+            `What are attention masks? <../glossary.html#attention-mask>`__
 
             decoder_start_token_id=None: (`optional`) int
                 If an encoder-decoder model starts decoding with a different token than BOS.
@@ -1269,15 +1236,13 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
             else:
                 tokens_to_add = next_token
 
-            # add token and increase length by one
             input_ids = torch.cat([input_ids, tokens_to_add.unsqueeze(-1)], dim=-1)
-            cur_len = cur_len + 1
 
             if eos_token_id is not None:
                 eos_in_sents = tokens_to_add == eos_token_id
                 # if sentence is unfinished and the token to add is eos, sent_lengths is filled with current length
                 is_sents_unfinished_and_token_to_add_is_eos = unfinished_sents.mul(eos_in_sents.long()).bool()
-                sent_lengths.masked_fill_(is_sents_unfinished_and_token_to_add_is_eos, cur_len)
+                sent_lengths.masked_fill_(is_sents_unfinished_and_token_to_add_is_eos, cur_len + 1)
                 # unfinished_sents is set to zero if eos in sentence
                 unfinished_sents.mul_((~eos_in_sents).long())
 
@@ -1291,6 +1256,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
                     [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
                 )
 
+            cur_len = cur_len + 1
+
         # if there are different sentences lengths in the batch, some batches have to be padded
         if sent_lengths.min().item() != sent_lengths.max().item():
             assert pad_token_id is not None, "`Pad_token_id` has to be defined if batches have different lengths"
@@ -1375,13 +1342,10 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
             if temperature != 1.0:
                 next_token_logits = next_token_logits / temperature
 
-            if self.config.is_encoder_decoder and do_sample is False:
-                # TODO (PVP) still a bit hacky here - there might be a better solution
-                next_token_logits = self.prepare_logits_for_generation(
-                    next_token_logits, cur_len=cur_len, max_length=max_length
-                )
-
             scores = F.log_softmax(next_token_logits, dim=-1)  # (batch_size * num_beams, vocab_size)
+            if self.config.is_encoder_decoder and do_sample is False:
+                # TODO (PVP) still a bit hacky here - there might be a better solutino
+                scores = self.prepare_scores_for_generation(scores, cur_len=cur_len, max_length=max_length)
 
             # set eos token prob to zero if min_length is not reached
             if eos_token_id is not None and cur_len < min_length:
@@ -1506,11 +1470,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
             beam_tokens = input_ids.new([x[1] for x in next_batch_beam])
             beam_idx = input_ids.new([x[2] for x in next_batch_beam])
 
-            # re-order batch and update current length
+            # re-order batch
             input_ids = input_ids[beam_idx, :]
             input_ids = torch.cat([input_ids, beam_tokens.unsqueeze(1)], dim=-1)
-            cur_len = cur_len + 1
-
             # re-order internal states
             if past is not None:
                 past = self._reorder_cache(past, beam_idx)
@@ -1521,6 +1483,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
                     [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
                 )
 
+            # update current length
+            cur_len = cur_len + 1
+
         # finalize all open beam hypotheses and end to generated hypotheses
         for batch_idx in range(batch_size):
             if done[batch_idx]:

src/transformers/modeling_xlnet.py

@@ -623,7 +623,7 @@ class XLNetModel(XLNetPreTrainedModel):
             mask_lo = torch.tril(attn_mask, diagonal=-1)
             ret = torch.cat([ret[:, :qlen] + mask_lo, ret[:, qlen:]], dim=1)
 
-        ret = ret.to(self.device)
+        ret = ret.to(next(self.parameters()))
         return ret
 
     def cache_mem(self, curr_out, prev_mem):
@@ -685,7 +685,7 @@ class XLNetModel(XLNetPreTrainedModel):
                 fwd_pos_seq = fwd_pos_seq.clamp(-self.clamp_len, self.clamp_len)
             pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz)
 
-        pos_emb = pos_emb.to(self.device)
+        pos_emb = pos_emb.to(next(self.parameters()))
         return pos_emb
 
     @add_start_docstrings_to_callable(XLNET_INPUTS_DOCSTRING)
@@ -761,8 +761,8 @@ class XLNetModel(XLNetPreTrainedModel):
         mlen = mems[0].shape[0] if mems is not None and mems[0] is not None else 0
         klen = mlen + qlen
 
-        dtype_float = self.dtype
-        device = self.device
+        dtype_float = next(self.parameters()).dtype
+        device = next(self.parameters()).device
 
         # Attention mask
         # causal attention mask

src/transformers/optimization.py

@@ -152,8 +152,8 @@ class AdamW(Optimizer):
 
                 # Decay the first and second moment running average coefficient
                 # In-place operations to update the averages at the same time
-                exp_avg.mul_(beta1).add_(grad, alpha=1.0 - beta1)
-                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2)
+                exp_avg.mul_(beta1).add_(1.0 - beta1, grad)
+                exp_avg_sq.mul_(beta2).addcmul_(1.0 - beta2, grad, grad)
                 denom = exp_avg_sq.sqrt().add_(group["eps"])
 
                 step_size = group["lr"]
@@ -173,6 +173,6 @@ class AdamW(Optimizer):
                 # of the weights to the loss with plain (non-momentum) SGD.
                 # Add weight decay at the end (fixed version)
                 if group["weight_decay"] > 0.0:
-                    p.data.add_(p.data, alpha=-group["lr"] * group["weight_decay"])
+                    p.data.add_(-group["lr"] * group["weight_decay"], p.data)
 
         return loss

src/transformers/optimization_tf.py

@@ -204,10 +204,7 @@ class GradientAccumulator(object):
         """Number of accumulated steps."""
         if self._accum_steps is None:
             self._accum_steps = tf.Variable(
-                tf.constant(0, dtype=tf.int64),
-                trainable=False,
-                synchronization=tf.VariableSynchronization.ON_READ,
-                aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,
+                tf.constant(0, dtype=tf.int64), trainable=False, synchronization=tf.VariableSynchronization.ON_READ,
             )
 
         return self._accum_steps.value()
@@ -217,7 +214,7 @@ class GradientAccumulator(object):
         """The accumulated gradients on the current replica."""
         if not self._gradients:
             raise ValueError("The accumulator should be called first to initialize the gradients")
-        return list(gradient.value() if gradient is not None else gradient for gradient in self._gradients)
+        return list(gradient.value() for gradient in self._gradients)
 
     def __call__(self, gradients):
         """Accumulates :obj:`gradients` on the current replica."""
@@ -226,13 +223,8 @@ class GradientAccumulator(object):
             self._gradients.extend(
                 [
                     tf.Variable(
-                        tf.zeros_like(gradient),
-                        trainable=False,
-                        synchronization=tf.VariableSynchronization.ON_READ,
-                        aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,
+                        tf.zeros_like(gradient), trainable=False, synchronization=tf.VariableSynchronization.ON_READ,
                     )
-                    if gradient is not None
-                    else gradient
                     for gradient in gradients
                 ]
             )
@@ -240,8 +232,7 @@ class GradientAccumulator(object):
             raise ValueError("Expected %s gradients, but got %d" % (len(self._gradients), len(gradients)))
 
         for accum_gradient, gradient in zip(self._gradients, gradients):
-            if accum_gradient is not None and gradient is not None:
-                accum_gradient.assign_add(gradient)
+            accum_gradient.assign_add(gradient)
 
         self._accum_steps.assign_add(1)
 
@@ -251,5 +242,4 @@ class GradientAccumulator(object):
             return
         self._accum_steps.assign(0)
         for gradient in self._gradients:
-            if gradient is not None:
-                gradient.assign(tf.zeros_like(gradient))
+            gradient.assign(tf.zeros_like(gradient))

src/transformers/pipelines.py

@@ -24,7 +24,7 @@ from abc import ABC, abstractmethod
 from contextlib import contextmanager
 from itertools import chain
 from os.path import abspath, exists
-from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Sequence, Tuple, Union
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Union
 
 import numpy as np
 
@@ -58,10 +58,6 @@ if is_torch_available():
         AutoModelWithLMHead,
     )
 
-if TYPE_CHECKING:
-    from .modeling_utils import PreTrainedModel
-    from .modeling_tf_utils import TFPreTrainedModel
-
 
 logger = logging.getLogger(__name__)
 
@@ -868,7 +864,6 @@ class NerPipeline(Pipeline):
         binary_output: bool = False,
         ignore_labels=["O"],
         task: str = "",
-        grouped_entities: bool = False,
     ):
         super().__init__(
             model=model,
@@ -883,7 +878,6 @@ class NerPipeline(Pipeline):
 
         self._basic_tokenizer = BasicTokenizer(do_lower_case=False)
         self.ignore_labels = ignore_labels
-        self.grouped_entities = grouped_entities
 
     def __call__(self, *args, **kwargs):
         inputs = self._args_parser(*args, **kwargs)
@@ -913,74 +907,23 @@ class NerPipeline(Pipeline):
             score = np.exp(entities) / np.exp(entities).sum(-1, keepdims=True)
             labels_idx = score.argmax(axis=-1)
 
-            entities = []
-            entity_groups = []
-            entity_group_disagg = []
-            # Filter to labels not in `self.ignore_labels`
-            filtered_labels_idx = [
-                (idx, label_idx)
-                for idx, label_idx in enumerate(labels_idx)
-                if self.model.config.id2label[label_idx] not in self.ignore_labels
-            ]
-
-            for idx, label_idx in filtered_labels_idx:
-
-                entity = {
-                    "word": self.tokenizer.convert_ids_to_tokens(int(input_ids[idx])),
-                    "score": score[idx][label_idx].item(),
-                    "entity": self.model.config.id2label[label_idx],
-                    "index": idx,
-                }
-                last_idx, _ = filtered_labels_idx[-1]
-                if self.grouped_entities:
-                    if not entity_group_disagg:
-                        entity_group_disagg += [entity]
-                        if idx == last_idx:
-                            entity_groups += [self.group_entities(entity_group_disagg)]
-                        continue
-
-                    # If the current entity is similar and adjacent to the previous entity, append it to the disaggregated entity group
-                    if (
-                        entity["entity"] == entity_group_disagg[-1]["entity"]
-                        and entity["index"] == entity_group_disagg[-1]["index"] + 1
-                    ):
-                        entity_group_disagg += [entity]
-                        # Group the entities at the last entity
-                        if idx == last_idx:
-                            entity_groups += [self.group_entities(entity_group_disagg)]
-                    # If the current entity is different from the previous entity, aggregate the disaggregated entity group
-                    else:
-                        entity_groups += [self.group_entities(entity_group_disagg)]
-                        entity_group_disagg = [entity]
-
-                entities += [entity]
+            answer = []
+            for idx, label_idx in enumerate(labels_idx):
+                if self.model.config.id2label[label_idx] not in self.ignore_labels:
+                    answer += [
+                        {
+                            "word": self.tokenizer.convert_ids_to_tokens(int(input_ids[idx])),
+                            "score": score[idx][label_idx].item(),
+                            "entity": self.model.config.id2label[label_idx],
+                        }
+                    ]
 
             # Append
-            if self.grouped_entities:
-                answers += [entity_groups]
-            else:
-                answers += [entities]
-
+            answers += [answer]
         if len(answers) == 1:
             return answers[0]
         return answers
 
-    def group_entities(self, entities):
-        """
-        Returns grouped entities
-        """
-        # Get the last entity in the entity group
-        entity = entities[-1]["entity"]
-        scores = np.mean([entity["score"] for entity in entities])
-        tokens = [entity["word"] for entity in entities]
-
-        entity_group = {
-            "entity_group": entity,
-            "score": np.mean(scores),
-            "word": self.tokenizer.convert_tokens_to_string(tokens),
-        }
-        return entity_group
-
 
 TokenClassificationPipeline = NerPipeline
 
@@ -1566,7 +1509,7 @@ class TranslationPipeline(Pipeline):
             return results
 
 
-# Register all the supported tasks here
+# Register all the supported task here
 SUPPORTED_TASKS = {
     "feature-extraction": {
         "impl": FeatureExtractionPipeline,
@@ -1588,7 +1531,7 @@ SUPPORTED_TASKS = {
                 "tf": "distilbert-base-uncased-finetuned-sst-2-english",
             },
             "config": "distilbert-base-uncased-finetuned-sst-2-english",
-            "tokenizer": "distilbert-base-uncased",
+            "tokenizer": "distilbert-base-cased",
         },
     },
     "ner": {
@@ -1629,9 +1572,9 @@ SUPPORTED_TASKS = {
         "tf": TFAutoModelWithLMHead if is_tf_available() else None,
         "pt": AutoModelWithLMHead if is_torch_available() else None,
         "default": {
-            "model": {"pt": "bart-large-cnn", "tf": "t5-small"},
+            "model": {"pt": "bart-large-cnn", "tf": None},
             "config": None,
-            "tokenizer": {"pt": ("bart-large-cnn", {"use_fast": False}), "tf": "t5-small"},
+            "tokenizer": ("bart-large-cnn", {"use_fast": False}),
         },
     },
     "translation_en_to_fr": {

src/transformers/tokenization_albert.py

@@ -274,7 +274,7 @@ class AlbertTokenizer(PreTrainedTokenizer):
                 Set to True if the token list is already formatted with special tokens for the model
 
         Returns:
-            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+            :obj:`List[int]`: A list of integers in the range [0, 1]: 0 for a special token, 1 for a sequence token.
         """
 
         if already_has_special_tokens:

src/transformers/tokenization_auto.py

@@ -38,7 +38,6 @@ from .configuration_auto import (
     XLMRobertaConfig,
     XLNetConfig,
 )
-from .configuration_marian import MarianConfig
 from .configuration_utils import PretrainedConfig
 from .tokenization_albert import AlbertTokenizer
 from .tokenization_bart import BartTokenizer
@@ -50,7 +49,6 @@ 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_marian import MarianTokenizer
 from .tokenization_openai import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
 from .tokenization_reformer import ReformerTokenizer
 from .tokenization_roberta import RobertaTokenizer, RobertaTokenizerFast
@@ -71,7 +69,6 @@ TOKENIZER_MAPPING = OrderedDict(
         (AlbertConfig, (AlbertTokenizer, None)),
         (CamembertConfig, (CamembertTokenizer, None)),
         (XLMRobertaConfig, (XLMRobertaTokenizer, None)),
-        (MarianConfig, (MarianTokenizer, None)),
         (BartConfig, (BartTokenizer, None)),
         (RobertaConfig, (RobertaTokenizer, RobertaTokenizerFast)),
         (ReformerConfig, (ReformerTokenizer, None)),

src/transformers/tokenization_bart.py

@@ -27,6 +27,8 @@ vocab_url = "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-v
 merges_url = "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-merges.txt"
 _all_bart_models = ["bart-large", "bart-large-mnli", "bart-large-cnn", "bart-large-xsum"]
 
+VOCAB_FILES_NAMES = {"vocab_file": "sentence.bpe.model"}
+
 
 class BartTokenizer(RobertaTokenizer):
     # merges and vocab same as Roberta
@@ -42,6 +44,6 @@ SPM_URL = "https://s3.amazonaws.com/models.huggingface.co/bert/facebook/mbart-la
 
 
 class MBartTokenizer(XLMRobertaTokenizer):
-    vocab_files_names = {"vocab_file": "sentencepiece.bpe.model"}
+    vocab_files_names = VOCAB_FILES_NAMES
     max_model_input_sizes = {m: 1024 for m in _all_mbart_models}
     pretrained_vocab_files_map = {"vocab_file": {m: SPM_URL for m in _all_mbart_models}}

src/transformers/tokenization_bert.py

@@ -269,7 +269,7 @@ class BertTokenizer(PreTrainedTokenizer):
                 Set to True if the token list is already formatted with special tokens for the model
 
         Returns:
-            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+            :obj:`List[int]`: A list of integers in the range [0, 1]: 0 for a special token, 1 for a sequence token.
         """
 
         if already_has_special_tokens:

src/transformers/tokenization_camembert.py

@@ -102,7 +102,6 @@ class CamembertTokenizer(PreTrainedTokenizer):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
 
     def __init__(
         self,
@@ -182,7 +181,7 @@ class CamembertTokenizer(PreTrainedTokenizer):
                 Set to True if the token list is already formatted with special tokens for the model
 
         Returns:
-            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+            :obj:`List[int]`: A list of integers in the range [0, 1]: 0 for a special token, 1 for a sequence token.
         """
         if already_has_special_tokens:
             if token_ids_1 is not None:
@@ -201,7 +200,14 @@ class CamembertTokenizer(PreTrainedTokenizer):
     ) -> List[int]:
         """
         Creates a mask from the two sequences passed to be used in a sequence-pair classification task.
-        CamemBERT, like RoBERTa, does not make use of token type ids, therefore a list of zeros is returned.
+        A CamemBERT sequence pair mask has the following format:
+
+        ::
+
+            0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+            | first sequence  | | second sequence |
+
+        if token_ids_1 is None, only returns the first portion of the mask (0s).
 
         Args:
             token_ids_0 (:obj:`List[int]`):
@@ -210,15 +216,15 @@ class CamembertTokenizer(PreTrainedTokenizer):
                 Optional second list of IDs for sequence pairs.
 
         Returns:
-            :obj:`List[int]`: List of zeros.
-
+            :obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given
+            sequence(s).
         """
         sep = [self.sep_token_id]
         cls = [self.cls_token_id]
 
         if token_ids_1 is None:
             return len(cls + token_ids_0 + sep) * [0]
-        return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+        return len(cls + token_ids_0 + sep + sep) * [0] + len(token_ids_1 + sep) * [1]
 
     @property
     def vocab_size(self):

src/transformers/tokenization_marian.py

@@ -1,5 +1,4 @@
 import json
-import re
 import warnings
 from typing import Dict, List, Optional, Union
 
@@ -15,7 +14,7 @@ vocab_files_names = {
     "vocab": "vocab.json",
     "tokenizer_config_file": "tokenizer_config.json",
 }
-MODEL_NAMES = ("opus-mt-en-de",)  # TODO(SS): the only required constant is vocab_files_names
+MODEL_NAMES = ("opus-mt-en-de",)
 PRETRAINED_VOCAB_FILES_MAP = {
     k: {m: f"{S3_BUCKET_PREFIX}/Helsinki-NLP/{m}/{fname}" for m in MODEL_NAMES}
     for k, fname in vocab_files_names.items()
@@ -23,26 +22,11 @@ PRETRAINED_VOCAB_FILES_MAP = {
 # Example URL https://s3.amazonaws.com/models.huggingface.co/bert/Helsinki-NLP/opus-mt-en-de/vocab.json
 
 
-class MarianTokenizer(PreTrainedTokenizer):
-    """Sentencepiece tokenizer for marian. Source and target languages have different SPM models.
-    The logic is use the relevant source_spm or target_spm to encode txt as pieces, then look up each piece in a vocab dictionary.
-
-    Examples::
-
-        from transformers import MarianTokenizer
-        tok = MarianTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-de')
-        src_texts = [ "I am a small frog.", "Tom asked his teacher for advice."]
-        tgt_texts = ["Ich bin ein kleiner Frosch.", "Tom bat seinen Lehrer um Rat."]  # optional
-        batch_enc: BatchEncoding = tok.prepare_translation_batch(src_texts, tgt_texts=tgt_texts)
-        # keys  [input_ids, attention_mask, decoder_input_ids,  decoder_attention_mask].
-        # model(**batch) should work
-    """
-
+class MarianSentencePieceTokenizer(PreTrainedTokenizer):
     vocab_files_names = vocab_files_names
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = {m: 512 for m in MODEL_NAMES}
     model_input_names = ["attention_mask"]  # actually attention_mask, decoder_attention_mask
-    language_code_re = re.compile(">>.+<<")  # type: re.Pattern
 
     def __init__(
         self,
@@ -65,8 +49,6 @@ class MarianTokenizer(PreTrainedTokenizer):
             pad_token=pad_token,
         )
         self.encoder = load_json(vocab)
-        if self.unk_token not in self.encoder:
-            raise KeyError("<unk> token must be in vocab")
         assert self.pad_token in self.encoder
         self.decoder = {v: k for k, v in self.encoder.items()}
 
@@ -74,15 +56,16 @@ class MarianTokenizer(PreTrainedTokenizer):
         self.target_lang = target_lang
 
         # load SentencePiece model for pre-processing
+        self.paths = {}
+
         self.spm_source = sentencepiece.SentencePieceProcessor()
         self.spm_source.Load(source_spm)
 
         self.spm_target = sentencepiece.SentencePieceProcessor()
         self.spm_target.Load(target_spm)
 
-        # Multilingual target side: default to using first supported language code.
-        self.supported_language_codes: list = [k for k in self.encoder if k.startswith(">>") and k.endswith("<<")]
-
+        # Note(SS): splitter would require lots of book-keeping.
+        # self.sentence_splitter = MosesSentenceSplitter(source_lang)
         try:
             from mosestokenizer import MosesPunctuationNormalizer
 
@@ -91,23 +74,12 @@ class MarianTokenizer(PreTrainedTokenizer):
             warnings.warn("Recommended: pip install mosestokenizer")
             self.punc_normalizer = lambda x: x
 
-    def normalize(self, x: str) -> str:
-        """Cover moses empty string edge case. They return empty list for '' input!"""
-        return self.punc_normalizer(x) if x else ""
-
     def _convert_token_to_id(self, token):
-        return self.encoder.get(token, self.encoder[self.unk_token])
+        return self.encoder[token]
 
-    def remove_language_code(self, text: str):
-        """Remove language codes like <<fr>> before sentencepiece"""
-        match = self.language_code_re.match(text)
-        code: list = [match.group(0)] if match else []
-        return code, self.language_code_re.sub("", text)
-
-    def _tokenize(self, text: str) -> List[str]:
-        code, text = self.remove_language_code(text)
-        pieces = self.current_spm.EncodeAsPieces(text)
-        return code + pieces
+    def _tokenize(self, text: str, src=True) -> List[str]:
+        spm = self.spm_source if src else self.spm_target
+        return spm.EncodeAsPieces(text)
 
     def _convert_id_to_token(self, index: int) -> str:
         """Converts an index (integer) in a token (str) using the encoder."""
@@ -117,6 +89,10 @@ class MarianTokenizer(PreTrainedTokenizer):
         """Uses target language sentencepiece model"""
         return self.spm_target.DecodePieces(tokens)
 
+    def _append_special_tokens_and_truncate(self, tokens: str, max_length: int,) -> List[int]:
+        ids: list = self.convert_tokens_to_ids(tokens)[:max_length]
+        return ids + [self.eos_token_id]
+
     def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]:
         """Build model inputs from a sequence by appending eos_token_id."""
         if token_ids_1 is None:
@@ -124,6 +100,9 @@ class MarianTokenizer(PreTrainedTokenizer):
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_0 + token_ids_1 + [self.eos_token_id]
 
+    def decode_batch(self, token_ids, **kwargs) -> List[str]:
+        return [self.decode(ids, **kwargs) for ids in token_ids]
+
     def prepare_translation_batch(
         self,
         src_texts: List[str],
@@ -132,44 +111,43 @@ class MarianTokenizer(PreTrainedTokenizer):
         pad_to_max_length: bool = True,
         return_tensors: str = "pt",
     ) -> BatchEncoding:
-        """Prepare model inputs for translation. For best performance, translate one sentence at a time.
+        """
         Arguments:
             src_texts: list of src language texts
+            src_lang: default en_XX (english)
             tgt_texts: list of tgt language texts
+            tgt_lang: default ro_RO (romanian)
             max_length: (None) defer to config (1024 for mbart-large-en-ro)
             pad_to_max_length: (bool)
-            return_tensors: (str) default "pt" returns pytorch tensors, pass None to return lists.
 
         Returns:
             BatchEncoding: with keys [input_ids, attention_mask, decoder_input_ids,  decoder_attention_mask]
-            all shaped bs, seq_len. (BatchEncoding is a dict of string -> tensor or lists).
-            If no tgt_text is specified, the only keys will be input_ids and attention_mask.
+            all shaped bs, seq_len. (BatchEncoding is a dict of string -> tensor or lists)
+
+        Examples:
+            from transformers import MarianS
         """
-        if "" in src_texts:
-            raise ValueError(f"found empty string in src_texts: {src_texts}")
-        self.current_spm = self.spm_source
-        src_texts = [self.normalize(t) for t in src_texts]  # this does not appear to do much
         model_inputs: BatchEncoding = self.batch_encode_plus(
             src_texts,
             add_special_tokens=True,
             return_tensors=return_tensors,
             max_length=max_length,
             pad_to_max_length=pad_to_max_length,
+            src=True,
         )
         if tgt_texts is None:
             return model_inputs
 
-        self.current_spm = self.spm_target
         decoder_inputs: BatchEncoding = self.batch_encode_plus(
             tgt_texts,
             add_special_tokens=True,
             return_tensors=return_tensors,
             max_length=max_length,
             pad_to_max_length=pad_to_max_length,
+            src=False,
         )
         for k, v in decoder_inputs.items():
             model_inputs[f"decoder_{k}"] = v
-        self.current_spm = self.spm_source
         return model_inputs
 
     @property

src/transformers/tokenization_roberta.py

@@ -193,7 +193,7 @@ class RobertaTokenizer(GPT2Tokenizer):
                 Set to True if the token list is already formatted with special tokens for the model
 
         Returns:
-            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+            :obj:`List[int]`: A list of integers in the range [0, 1]: 0 for a special token, 1 for a sequence token.
         """
         if already_has_special_tokens:
             if token_ids_1 is not None:

src/transformers/tokenization_utils.py

@@ -173,11 +173,7 @@ class BatchEncoding(UserDict):
 
     """
 
-    def __init__(
-        self,
-        data: Optional[Dict[str, Any]] = None,
-        encoding: Optional[Union[EncodingFast, Sequence[EncodingFast]]] = None,
-    ):
+    def __init__(self, data: Dict[str, Any], encoding: Optional[Union[EncodingFast, Sequence[EncodingFast]]] = None):
         super().__init__(data)
 
         if isinstance(encoding, EncodingFast):
@@ -2183,9 +2179,6 @@ class PreTrainedTokenizer(SpecialTokensMixin):
         else:
             return text
 
-    def batch_decode(self, sequences: List[List[int]], **kwargs) -> List[str]:
-        return [self.decode(seq, **kwargs) for seq in sequences]
-
     @staticmethod
     def clean_up_tokenization(out_string: str) -> str:
         """ Clean up a list of simple English tokenization artifacts like spaces before punctuations and abreviated forms.
@@ -2198,6 +2191,7 @@ class PreTrainedTokenizer(SpecialTokensMixin):
             .replace(" ' ", "'")
             .replace(" n't", "n't")
             .replace(" 'm", "'m")
+            .replace(" do not", " don't")
             .replace(" 's", "'s")
             .replace(" 've", "'ve")
             .replace(" 're", "'re")