pin TF to 2.1 (#4297 )

* pin TF to 2.1 * Pin flake8 as well
[ci] Restrict GPU tests to actual code commits
2020-05-11 21:03:30 -04:00 · 2020-05-11 20:40:41 -04:00 · 2020-05-12 00:05:36 +00:00 · 2020-05-11 16:43:57 -04:00 · 2020-05-11 15:24:02 -04:00 · 2020-05-11 15:05:36 -04:00
44 changed files with 873 additions and 355 deletions
@@ -1,9 +1,13 @@
 name: Self-hosted runner (push)

 on: 
-  # push:
-  #   branches:
-  #     - master
+  push:
+    branches:
+      - master
+    paths: 
+      - "src/**"
+      - "tests/**"
+      - ".github/**"
  # pull_request:
  repository_dispatch:

@@ -31,8 +35,8 @@ jobs:
    - name: Install dependencies
      run: |
        source .env/bin/activate
-        pip install .[sklearn,tf,torch,testing]
-        pip uninstall -y tensorflow
+        pip install torch==1.4.0
+        pip install .[sklearn,testing]

    - name: Are GPUs recognized by our DL frameworks
      run: |
@@ -164,8 +164,9 @@ 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. **[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.
+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.

 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).

@@ -67,3 +67,131 @@ 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.
+```
@@ -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_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/transformers/blob/master/transformers/convert_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_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.

 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>`_\ ).

@@ -33,6 +33,26 @@ 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
 ^^^^^^^^^^

@@ -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/ner) | 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/token-classification) | 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.) |

@@ -108,3 +108,4 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
    model_doc/electra
    model_doc/dialogpt
    model_doc/reformer
+    model_doc/marian
@@ -1,5 +1,18 @@
-# Migrating from pytorch-pretrained-bert
+# Migrating from previous packages

+## 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`

@@ -1,6 +1,6 @@
 Bart
 ----------------------------------------------------
-**DISCLAIMER:** This model is still a work in progress, if you see something strange,
+**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

@@ -0,0 +1,43 @@
+MarianMTModel
+----------------------------------------------------
+**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
+These models are for machine translation. The list of supported language pairs can be found `here <https://huggingface.co/Helsinki-NLP>`__.
+
+Opus Project
+~~~~~~~~~~~~
+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.
+
+Implementation Notes
+~~~~~~~~~~~~~~~~~~~~
+- each model is about 298 MB on disk, there are 1,000+ models.
+- Models are named with the following patter 'Helsinki-NLP/opus-mt-{src_langs}-{targ_langs}'. If there are multiple source or target languages they are joined by a '+' symbol.
+- the 80 opus models that require BPE preprocessing are not supported.
+- There is an outstanding issue w.r.t multilingual models and language codes.
+- The modeling code is the same as ``BartModel`` 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``
+
+
+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
@@ -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``                                             | | 12-layer, 1024-hidden, 16-heads, 406M parameters                                                                                    |
+| Bart              | ``bart-large``                                             | | 24-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                                                                        |
@@ -296,6 +296,12 @@ 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-crime-and-punishment``                          | | 6-layer, 256-hidden, 2-heads, 3M parameters                                                                                         |
-|                   |                                                            | | Trained on English text: Crime and Punishment novel by Fyodor Dostoyevsky                                                           |
+| 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>`_)                                                                           |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
@@ -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 type of classes for each models:
+The library is build around three types of classes for each model:

- **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`
+- **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`

 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 in 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 into 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 full API reference for examples for each model class.
+See the full API reference for examples of 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):

@@ -1,7 +1,7 @@
 ## Named Entity Recognition

-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.
+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.
 This example fine-tune Bert Multilingual on GermEval 2014 (German NER).
 Details and results for the fine-tuning provided by @stefan-it.

@@ -0,0 +1,57 @@
+## 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.
@@ -1,3 +1,4 @@
+
 # 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. 
@@ -12,6 +13,7 @@ O

 ## Metrics on evaluation set:
 ### Average over the 40 languages
+Number of documents: 262300
 ```
           precision    recall  f1-score   support

@@ -24,6 +26,7 @@ macro avg       0.86      0.87      0.87    333298
 ```

 ### Afrikaans
+Number of documents: 1000
 ```
           precision    recall  f1-score   support

@@ -36,6 +39,7 @@ macro avg       0.87      0.91      0.89      1469
 ``` 

 ### Arabic
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -48,6 +52,7 @@ macro avg       0.87      0.88      0.88     10754
 ```

 ### Basque
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -60,6 +65,7 @@ macro avg       0.89      0.89      0.89     12954
 ```

 ### Bengali
+Number of documents: 1000
 ```
           precision    recall  f1-score   support

@@ -72,6 +78,7 @@ macro avg       0.91      0.92      0.91      1095
 ```

 ### Bulgarian
+Number of documents: 1000
 ```
           precision    recall  f1-score   support

@@ -84,6 +91,7 @@ macro avg       0.91      0.92      0.91     14116
 ```

 ### Burmese
+Number of documents: 100
 ```
           precision    recall  f1-score   support

@@ -96,6 +104,7 @@ macro avg       0.57      0.65      0.60       103
 ```

 ### Chinese
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -108,6 +117,7 @@ macro avg       0.76      0.78      0.77     11558
 ```

 ### Dutch
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -120,6 +130,7 @@ macro avg       0.91      0.92      0.91     13120
 ```

 ### English
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -132,6 +143,7 @@ macro avg       0.82      0.83      0.83     13973
 ```

 ### Estonian
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -144,6 +156,7 @@ macro avg       0.90      0.91      0.90     13558
 ```

 ### Finnish
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -156,6 +169,7 @@ macro avg       0.89      0.89      0.89     13930
 ```

 ### French
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -168,6 +182,7 @@ macro avg       0.89      0.90      0.90     12933
 ```

 ### Georgian
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -180,6 +195,7 @@ macro avg       0.84      0.86      0.85     12615
 ```

 ### German
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -192,6 +208,7 @@ macro avg       0.86      0.86      0.86     13638
 ```

 ### Greek
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -204,6 +221,7 @@ macro avg       0.88      0.90      0.89     12101
 ```

 ### Hebrew
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -216,6 +234,7 @@ macro avg       0.82      0.83      0.83     12934
 ```

 ### Hindi
+Number of documents: 1000
 ```
           precision    recall  f1-score   support

@@ -228,6 +247,7 @@ macro avg       0.84      0.87      0.85      1211
 ```

 ### Hungarian
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -240,6 +260,7 @@ macro avg       0.91      0.92      0.91     13879
 ```

 ### Indonesian
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -252,6 +273,7 @@ macro avg       0.91      0.92      0.92     11376
 ```

 ### Italian
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -264,6 +286,7 @@ macro avg       0.90      0.90      0.90     13412
 ```

 ### Japanese
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -276,6 +299,7 @@ macro avg       0.69      0.72      0.70     12277
 ```

 ### Javanese
+Number of documents: 100
 ```
           precision    recall  f1-score   support

@@ -288,6 +312,7 @@ macro avg       0.78      0.82      0.80       112
 ```

 ### Kazakh
+Number of documents: 1000
 ```
           precision    recall  f1-score   support

@@ -300,6 +325,7 @@ macro avg       0.81      0.83      0.81      1135
 ```

 ### Korean
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -312,6 +338,7 @@ macro avg       0.83      0.83      0.83     13329
 ```

 ### Malay
+Number of documents: 1000
 ```
           precision    recall  f1-score   support

@@ -324,6 +351,7 @@ macro avg       0.91      0.92      0.91      1088
 ```

 ### Malayalam
+Number of documents: 1000
 ```
           precision    recall  f1-score   support

@@ -336,6 +364,7 @@ macro avg       0.78      0.80      0.79      1155
 ```

 ### Marathi
+Number of documents: 1000
 ```
           precision    recall  f1-score   support

@@ -348,6 +377,7 @@ macro avg       0.85      0.86      0.85      1190
 ```

 ### Persian
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -360,6 +390,7 @@ macro avg       0.92      0.92      0.92     10494
 ```

 ### Portuguese
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -372,6 +403,7 @@ macro avg       0.90      0.91      0.90     12673
 ```

 ### Russian
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -384,6 +416,7 @@ macro avg       0.87      0.88      0.88     12051
 ```

 ### Spanish
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -396,6 +429,7 @@ macro avg       0.90      0.91      0.90     12153
 ```

 ### Swahili
+Number of documents: 1000
 ```
           precision    recall  f1-score   support

@@ -408,6 +442,7 @@ macro avg       0.88      0.89      0.88      1202
 ```

 ### Tagalog
+Number of documents: 1000
 ```
           precision    recall  f1-score   support

@@ -420,6 +455,7 @@ macro avg       0.90      0.92      0.91      1027
 ```

 ### Tamil
+Number of documents: 1000
 ```
           precision    recall  f1-score   support

@@ -432,6 +468,7 @@ macro avg       0.82      0.83      0.82      1183
 ```

 ### Telugu
+Number of documents: 1000
 ```
           precision    recall  f1-score   support

@@ -444,6 +481,7 @@ macro avg       0.73      0.77      0.75      1193
 ```

 ### Thai
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -456,6 +494,7 @@ macro avg       0.68      0.74      0.71     14722
 ```

 ### Turkish
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -468,6 +507,7 @@ macro avg       0.91      0.92      0.91     13360
 ```

 ### Urdu
+Number of documents: 1000
 ```
           precision    recall  f1-score   support

@@ -480,6 +520,7 @@ macro avg       0.92      0.94      0.93      1011
 ```

 ### Vietnamese
+Number of documents: 10000
 ```
           precision    recall  f1-score   support

@@ -492,6 +533,7 @@ macro avg       0.89      0.90      0.90     11107
 ```

 ### Yoruba
+Number of documents: 100
 ```
           precision    recall  f1-score   support

@@ -504,7 +546,7 @@ macro avg       0.63      0.68      0.63       107
 ```

 ## Reproduce the results
-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:
+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:
 ```
 cd examples/ner
 python run_tf_ner.py \
@@ -533,8 +575,9 @@ nlp_ner = pipeline(
    model="jplu/tf-xlm-r-ner-40-lang",
    tokenizer=(
        'jplu/tf-xlm-r-ner-40-lang',  
-        {"use_fast": True}
-))
+        {"use_fast": True}),
+    framework="tf"
+)

 text_fr = "Barack Obama est né à Hawaï."
 text_en = "Barack Obama was born in Hawaii."
@@ -553,4 +596,4 @@ 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'}]

-```
+```
@@ -0,0 +1,60 @@
+---
+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
+
+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")
+  ```
@@ -1,4 +1,4 @@
-# Details
+# Bert-base Turkish Sentiment Model

 https://huggingface.co/savasy/bert-base-turkish-sentiment-cased

@@ -7,8 +7,9 @@ This model is used for Sentiment Analysis, which is based on BERTurk for Turkish

 # Dataset

-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 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.
 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
@@ -18,16 +19,70 @@ 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.
+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*|


 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)
+* 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

 ```
@@ -49,3 +104,43 @@ 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(folder)
+tokenizer = AutoTokenizer.from_pretrained(folder)
+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)
+```
+
+
+
+
+
@@ -67,8 +67,8 @@ extras = {}

 extras["mecab"] = ["mecab-python3"]
 extras["sklearn"] = ["scikit-learn"]
-extras["tf"] = ["tensorflow<=2.1.0"]
-extras["tf-cpu"] = ["tensorflow-cpu<=2.1.0"]
+extras["tf"] = ["tensorflow <= 2.1"]
+extras["tf-cpu"] = ["tensorflow-cpu  <= 2.1"]
 extras["torch"] = ["torch"]

 extras["serving"] = ["pydantic", "uvicorn", "fastapi", "starlette"]
@@ -79,9 +79,9 @@ extras["docs"] = ["recommonmark", "sphinx", "sphinx-markdown-tables", "sphinx-rt
 extras["quality"] = [
    "black",
    "isort @ git+git://github.com/timothycrosley/isort.git@e63ae06ec7d70b06df9e528357650281a3d3ec22#egg=isort",
-    "flake8",
+    "flake8==3.7.9",
 ]
-extras["dev"] = extras["testing"] + extras["quality"] + ["mecab-python3", "scikit-learn", "tensorflow<=2.1.0", "torch"]
+extras["dev"] = extras["testing"] + extras["quality"] + ["mecab-python3", "scikit-learn", "tensorflow <= 2.1", "torch"]

 setup(
    name="transformers",
@@ -248,7 +248,7 @@ if is_torch_available():
        BART_PRETRAINED_MODEL_ARCHIVE_MAP,
    )
    from .modeling_marian import MarianMTModel
-    from .tokenization_marian import MarianSentencePieceTokenizer
+    from .tokenization_marian import MarianTokenizer
    from .modeling_roberta import (
        RobertaForMaskedLM,
        RobertaModel,
@@ -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 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))
+    return 0.5 * x * (1.0 + torch.tanh(math.sqrt(2.0 / 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 + torch.tanh(x * 0.7978845608 * (1 + 0.044715 * x * x)))
+    return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 + 0.044715 * x * x)))


 ACT2FN = {
@@ -62,7 +62,21 @@ class ConvertCommand(BaseTransformersCLICommand):
        self._finetuning_task_name = finetuning_task_name

    def run(self):
-        if self._model_type == "bert":
+        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":
            try:
                from transformers.convert_bert_original_tf_checkpoint_to_pytorch import (
                    convert_tf_checkpoint_to_pytorch,
@@ -28,6 +28,7 @@ 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
@@ -73,6 +74,7 @@ CONFIG_MAPPING = OrderedDict(
        ("albert", AlbertConfig,),
        ("camembert", CamembertConfig,),
        ("xlm-roberta", XLMRobertaConfig,),
+        ("marian", MarianConfig,),
        ("bart", BartConfig,),
        ("reformer", ReformerConfig,),
        ("roberta", RobertaConfig,),
@@ -23,4 +23,5 @@ PRETRAINED_CONFIG_ARCHIVE_MAP = {


 class MarianConfig(BartConfig):
+    model_type = "marian"
    pretrained_config_archive_map = PRETRAINED_CONFIG_ARCHIVE_MAP
@@ -24,7 +24,8 @@ 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-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",
 }


@@ -1,165 +0,0 @@
-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_torch_available, is_tf_available
-from transformers.pipelines import SUPPORTED_TASKS, pipeline, Pipeline
-from transformers.tokenization_utils import BatchEncoding
-
-
-class OnnxConverterArgumentParser(ArgumentParser):
-    """
-    Wraps all the script arguments supported to export transformers models to ONNX IR
-    """
-
-    def __init__(self):
-        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=-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("output")
-
-
-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):
-        axes = {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)))
-        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 args.framework == "pt" else nlp.model(tokens)
-
-    if not isinstance(outputs, (list, tuple)):
-        outputs = (outputs,)
-
-    # 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()}
-    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(args: Namespace) -> Pipeline:
-    # If no tokenizer provided
-    if args.tokenizer is None:
-        args.tokenizer = args.model
-
-    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(args.task, model=args.model, framework=args.framework)
-
-
-def export_pytorch(nlp: Pipeline, args: Namespace):
-    if not is_torch_available():
-        print("Cannot export {} because PyTorch is not installed. Please install torch first.".format(args.model))
-        exit(1)
-
-    import torch
-    from torch.onnx import export
-
-    print("PyTorch: {}".format(torch.__version__))
-
-    with torch.no_grad():
-        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,
-            tokens,
-            f=args.output,
-            input_names=input_names,
-            output_names=output_names,
-            dynamic_axes=dynamic_axes,
-            do_constant_folding=False,
-            use_external_data_format=True,
-            enable_onnx_checker=True,
-        )
-
-
-def export_tensorflow(nlp: Pipeline, args: Namespace):
-    if not is_tf_available():
-        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")
-
-    try:
-        import tensorflow as tf
-        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, args.framework)
-
-        # Forward
-        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:
-        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()
-
-    # Ensure we have an absolute path for the output
-    args.output = abspath(args.output)
-
-    # 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"))
-
-    # 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))
@@ -11,7 +11,8 @@ import numpy as np
 import torch
 from tqdm import tqdm

-from transformers import MarianConfig, MarianMTModel, MarianSentencePieceTokenizer
+from transformers import MarianConfig, MarianMTModel, MarianTokenizer
+from transformers.hf_api import HfApi


 def remove_prefix(text: str, prefix: str):
@@ -38,6 +39,19 @@ 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))
@@ -81,7 +95,12 @@ def find_model_file(dest_dir):  # this one better
    return model_file


-def parse_readmes(repo_path):
+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."
+        )
    results = {}
    for p in Path(repo_path).ls():
        n_dash = p.name.count("-")
@@ -90,22 +109,53 @@ def parse_readmes(repo_path):
        else:
            lns = list(open(p / "README.md").readlines())
            results[p.name] = _parse_readme(lns)
-    return results
+    return [(k, v["pre-processing"], v["download"]) for k, v in results.items()]


-def download_all_sentencepiece_models(repo_path="Opus-MT-train/models"):
+CH_GROUP = "cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh"
+
+
+def convert_all_sentencepiece_models(model_list=None, repo_path=None):
    """Requires 300GB"""
    save_dir = Path("marian_ckpt")
-    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}")
+    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 in tqdm(model_list):
+        if "SentencePiece" not in prepro:  # dont convert BPE models.
            continue
-        if "SentencePiece" not in v["pre-processing"]:
+        if not os.path.exists(save_dir / k / "pytorch_model.bin"):
+            download_and_unzip(download, save_dir / k)
+        pair_name = k.replace(CH_GROUP, "ch_group")
+        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(readmes_raw, pair):
+    import wget
+
+    download_url = readmes_raw[pair]["download"]
+    test_set_url = download_url[:-4] + ".test.txt"
+    fname = wget.download(test_set_url, f"opus_test_{pair}.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():
            continue
-        download_and_unzip(v["download"], save_dir / k)
+        convert(source_dir, dest_dir)


 def _parse_readme(lns):
@@ -131,7 +181,7 @@ def _parse_readme(lns):
    return subres


-def write_metadata(dest_dir: Path):
+def save_tokenizer_config(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")
@@ -148,13 +198,17 @@ 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(model_dir / "opus.spm32k-spm32k.vocab.yml")
+    vocab = load_yaml(find_vocab_file(model_dir))
    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")
-    write_metadata(model_dir)
+    save_tokenizer_config(model_dir)


 def save_tokenizer(self, save_directory):
@@ -251,7 +305,6 @@ 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"],
@@ -273,6 +326,9 @@ 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):
@@ -349,12 +405,12 @@ def download_and_unzip(url, dest_dir):
    os.remove(filename)


-def main(source_dir, dest_dir):
+def convert(source_dir: Path, dest_dir):
    dest_dir = Path(dest_dir)
    dest_dir.mkdir(exist_ok=True)

    add_special_tokens_to_vocab(source_dir)
-    tokenizer = MarianSentencePieceTokenizer.from_pretrained(str(source_dir))
+    tokenizer = MarianTokenizer.from_pretrained(str(source_dir))
    save_tokenizer(tokenizer, dest_dir)

    opus_state = OpusState(source_dir)
@@ -377,7 +433,7 @@ 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
-    main(source_dir, dest_dir)
+    convert(source_dir, dest_dir)


 def load_yaml(path):
@@ -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][2][2]
+    intermediate_weights = weights[2][0][1][2]

    # Chunked Feed Forward
    if len(intermediate_weights) == 4:
@@ -145,19 +145,16 @@ 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 + 1 == len(
+    assert len(torch_model_reformer.encoder.layers) * 4 == 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(out_weights[0][0])
-    layer_norm_out_bias = np.asarray(out_weights[0][1])
+    layer_norm_out_weight = np.asarray(weights[7][0])
+    layer_norm_out_bias = np.asarray(weights[7][1])
    set_param(
        torch_model_reformer.encoder.layer_norm,
        torch.tensor(layer_norm_out_weight),
@@ -165,8 +162,8 @@ def set_model_weights_in_torch(weights, torch_model, hidden_size):
    )

    # output embeddings
-    output_embed_weights = np.asarray(out_weights[2][0])
-    output_embed_bias = np.asarray(out_weights[2][1])
+    output_embed_weights = np.asarray(weights[9][0])
+    output_embed_bias = np.asarray(weights[9][1])
    set_param(
        torch_model.lm_head.decoder,
        torch.tensor(output_embed_weights).transpose(0, 1).contiguous(),
@@ -15,6 +15,7 @@ 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
@@ -68,19 +69,10 @@ except ImportError:
    )
 default_cache_path = os.path.join(torch_cache_home, "transformers")

-try:
-    from pathlib import Path

-    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
+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)

 WEIGHTS_NAME = "pytorch_model.bin"
 TF2_WEIGHTS_NAME = "tf_model.h5"
@@ -39,6 +39,7 @@ 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,
@@ -98,6 +99,7 @@ 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 (
@@ -214,6 +216,7 @@ MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
        (AlbertConfig, AlbertForMaskedLM),
        (CamembertConfig, CamembertForMaskedLM),
        (XLMRobertaConfig, XLMRobertaForMaskedLM),
+        (MarianConfig, MarianMTModel),
        (BartConfig, BartForConditionalGeneration),
        (RobertaConfig, RobertaForMaskedLM),
        (BertConfig, BertForMaskedLM),
@@ -903,7 +906,7 @@ class AutoModelForQuestionAnswering:
        Examples::

            config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
-            model = AutoModelForSequenceClassification.from_config(config)  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
+            model = AutoModelForQuestionAnswering.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):
@@ -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 / (v.size(-1) ** 0.5)
+            w = w / (float(v.size(-1)) ** 0.5)
        nd, ns = w.size(-2), w.size(-1)
        mask = self.bias[:, :, ns - nd : ns, :ns]
-        w = torch.where(mask, w, self.masked_bias.to(w.dtype))
+        w = torch.where(mask.bool(), w, self.masked_bias.to(w.dtype))

        if attention_mask is not None:
            # Apply the attention mask
@@ -18,16 +18,30 @@
 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):
-    """Pytorch version of marian-nmt's transformer.h (c++). Designed for the OPUS-NMT translation checkpoints.
-    Model API is identical to 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>`__

-    pretrained_model_archive_map = PRETRAINED_MODEL_ARCHIVE_MAP
+    Examples::
+
+        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 List`__
+
+        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_scores_for_generation(self, scores, cur_len, max_length):
        if cur_len == max_length - 1 and self.config.eos_token_id is not None:
@@ -36,7 +36,8 @@ 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-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",
 }


@@ -561,8 +562,8 @@ 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
-            mask_value = self.mask_value_float16
+            self_mask_value = self.self_mask_value_float16.half()
+            mask_value = self.mask_value_float16.half()
        else:
            self_mask_value = self.self_mask_value_float32
            mask_value = self.mask_value_float32
@@ -833,7 +834,7 @@ 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
+                mask_value = self.mask_value_float16.half()
            else:
                mask_value = self.mask_value_float32

@@ -204,7 +204,10 @@ 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,
+                tf.constant(0, dtype=tf.int64),
+                trainable=False,
+                synchronization=tf.VariableSynchronization.ON_READ,
+                aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,
            )

        return self._accum_steps.value()
@@ -223,7 +226,10 @@ class GradientAccumulator(object):
            self._gradients.extend(
                [
                    tf.Variable(
-                        tf.zeros_like(gradient), trainable=False, synchronization=tf.VariableSynchronization.ON_READ,
+                        tf.zeros_like(gradient),
+                        trainable=False,
+                        synchronization=tf.VariableSynchronization.ON_READ,
+                        aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,
                    )
                    for gradient in gradients
                ]
@@ -1531,7 +1531,7 @@ SUPPORTED_TASKS = {
                "tf": "distilbert-base-uncased-finetuned-sst-2-english",
            },
            "config": "distilbert-base-uncased-finetuned-sst-2-english",
-            "tokenizer": "distilbert-base-cased",
+            "tokenizer": "distilbert-base-uncased",
        },
    },
    "ner": {
@@ -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]: 0 for a special token, 1 for a sequence token.
+            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
        """

        if already_has_special_tokens:
@@ -38,6 +38,7 @@ 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
@@ -49,6 +50,7 @@ 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
@@ -69,6 +71,7 @@ TOKENIZER_MAPPING = OrderedDict(
        (AlbertConfig, (AlbertTokenizer, None)),
        (CamembertConfig, (CamembertTokenizer, None)),
        (XLMRobertaConfig, (XLMRobertaTokenizer, None)),
+        (MarianConfig, (MarianTokenizer, None)),
        (BartConfig, (BartTokenizer, None)),
        (RobertaConfig, (RobertaTokenizer, RobertaTokenizerFast)),
        (ReformerConfig, (ReformerTokenizer, None)),
@@ -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]: 0 for a special token, 1 for a sequence token.
+            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
        """

        if already_has_special_tokens:
@@ -102,6 +102,7 @@ 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,
@@ -181,7 +182,7 @@ 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]: 0 for a special token, 1 for a sequence token.
+            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
        """
        if already_has_special_tokens:
            if token_ids_1 is not None:
@@ -200,14 +201,7 @@ class CamembertTokenizer(PreTrainedTokenizer):
    ) -> List[int]:
        """
        Creates a mask from the two sequences passed to be used in a sequence-pair classification task.
-        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).
+        CamemBERT, like RoBERTa, does not make use of token type ids, therefore a list of zeros is returned.

        Args:
            token_ids_0 (:obj:`List[int]`):
@@ -216,15 +210,15 @@ class CamembertTokenizer(PreTrainedTokenizer):
                Optional second list of IDs for sequence pairs.

        Returns:
-            :obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given
-            sequence(s).
+            :obj:`List[int]`: List of zeros.
+
        """
        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) * [0] + len(token_ids_1 + sep) * [1]
+        return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]

    @property
    def vocab_size(self):
@@ -22,7 +22,21 @@ PRETRAINED_VOCAB_FILES_MAP = {
 # Example URL https://s3.amazonaws.com/models.huggingface.co/bert/Helsinki-NLP/opus-mt-en-de/vocab.json


-class MarianSentencePieceTokenizer(PreTrainedTokenizer):
+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
+    """
+
    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}
@@ -49,6 +63,8 @@ class MarianSentencePieceTokenizer(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()}

@@ -64,8 +80,11 @@ class MarianSentencePieceTokenizer(PreTrainedTokenizer):
        self.spm_target = sentencepiece.SentencePieceProcessor()
        self.spm_target.Load(target_spm)

-        # Note(SS): splitter would require lots of book-keeping.
-        # self.sentence_splitter = MosesSentenceSplitter(source_lang)
+        # 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("<<")]
+        self.tgt_lang_id = None  # will not be used unless it is set through prepare_translation_batch
+
+        # Note(SS): sentence_splitter would require lots of book-keeping.
        try:
            from mosestokenizer import MosesPunctuationNormalizer

@@ -75,11 +94,10 @@ class MarianSentencePieceTokenizer(PreTrainedTokenizer):
            self.punc_normalizer = lambda x: x

    def _convert_token_to_id(self, token):
-        return self.encoder[token]
+        return self.encoder.get(token, self.encoder[self.unk_token])

-    def _tokenize(self, text: str, src=True) -> List[str]:
-        spm = self.spm_source if src else self.spm_target
-        return spm.EncodeAsPieces(text)
+    def _tokenize(self, text: str) -> List[str]:
+        return self.current_spm.EncodeAsPieces(text)

    def _convert_id_to_token(self, index: int) -> str:
        """Converts an index (integer) in a token (str) using the encoder."""
@@ -89,10 +107,6 @@ class MarianSentencePieceTokenizer(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:
@@ -100,7 +114,7 @@ class MarianSentencePieceTokenizer(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]:
+    def batch_decode(self, token_ids, **kwargs) -> List[str]:
        return [self.decode(ids, **kwargs) for ids in token_ids]

    def prepare_translation_batch(
@@ -114,40 +128,38 @@ class MarianSentencePieceTokenizer(PreTrainedTokenizer):
        """
        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)
-
-        Examples:
-            from transformers import MarianS
+            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.
        """
+        self.current_spm = self.spm_source
        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
@@ -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]: 0 for a special token, 1 for a sequence token.
+            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
        """
        if already_has_special_tokens:
            if token_ids_1 is not None:
@@ -893,7 +893,7 @@ class XLMTokenizer(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]: 0 for a special token, 1 for a sequence token.
+            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
        """

        if already_has_special_tokens:
@@ -217,7 +217,7 @@ class XLMRobertaTokenizer(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]: 0 for a special token, 1 for a sequence token.
+            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
        """

        if already_has_special_tokens:
@@ -278,7 +278,7 @@ class XLNetTokenizer(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]: 0 for a special token, 1 for a sequence token.
+            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
        """

        if already_has_special_tokens:
@@ -56,9 +56,11 @@ class TFTrainingArguments(TrainingArguments):
                strategy = tf.distribute.experimental.TPUStrategy(tpu)
            elif len(gpus) == 0:
                strategy = tf.distribute.OneDeviceStrategy(device="/cpu:0")
+            elif len(gpus) == 1:
+                strategy = tf.distribute.OneDeviceStrategy(device="/gpu:0")
            elif len(gpus) > 1:
                # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
-                strategy = tf.distribute.MirroredStrategy(gpus)
+                strategy = tf.distribute.MirroredStrategy()
            else:
                raise ValueError("Cannot find the proper strategy please check your environment properties.")

@@ -18,35 +18,94 @@ import unittest

 from transformers import is_torch_available
 from transformers.file_utils import cached_property
+from transformers.hf_api import HfApi

 from .utils import require_torch, slow, torch_device


 if is_torch_available():
    import torch
-    from transformers import MarianMTModel, MarianSentencePieceTokenizer
+    from transformers import (
+        AutoTokenizer,
+        MarianConfig,
+        AutoConfig,
+        AutoModelWithLMHead,
+        MarianTokenizer,
+        MarianMTModel,
+    )
+
+
+class ModelManagementTests(unittest.TestCase):
+    @slow
+    def test_model_count(self):
+        model_list = HfApi().model_list()
+        expected_num_models = 1011
+        actual_num_models = len([x for x in model_list if x.modelId.startswith("Helsinki-NLP")])
+        self.assertEqual(expected_num_models, actual_num_models)


@require_torch
-class IntegrationTests(unittest.TestCase):
+class MarianIntegrationTest(unittest.TestCase):
+    src = "en"
+    tgt = "de"
+    src_text = [
+        "I am a small frog.",
+        "Now I can forget the 100 words of german that I know.",
+        "Tom asked his teacher for advice.",
+        "That's how I would do it.",
+        "Tom really admired Mary's courage.",
+        "Turn around and close your eyes.",
+    ]
+    expected_text = [
+        "Ich bin ein kleiner Frosch.",
+        "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
+        "Tom bat seinen Lehrer um Rat.",
+        "So würde ich das machen.",
+        "Tom bewunderte Marias Mut wirklich.",
+        "Drehen Sie sich um und schließen Sie die Augen.",
+    ]
+    # ^^ actual C++ output differs slightly: (1) des Deutschen removed, (2) ""-> "O", (3) tun -> machen
+
    @classmethod
    def setUpClass(cls) -> None:
-        cls.model_name = "Helsinki-NLP/opus-mt-en-de"
-        cls.tokenizer = MarianSentencePieceTokenizer.from_pretrained(cls.model_name)
+        cls.model_name = f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
+        cls.tokenizer: MarianTokenizer = AutoTokenizer.from_pretrained(cls.model_name)
        cls.eos_token_id = cls.tokenizer.eos_token_id
        return cls

    @cached_property
    def model(self):
-        model = MarianMTModel.from_pretrained(self.model_name).to(torch_device)
+        model: MarianMTModel = AutoModelWithLMHead.from_pretrained(self.model_name).to(torch_device)
+        c = model.config
+        self.assertListEqual(c.bad_words_ids, [[c.pad_token_id]])
+        self.assertEqual(c.max_length, 512)
+        self.assertEqual(c.decoder_start_token_id, c.pad_token_id)
+
        if torch_device == "cuda":
            return model.half()
        else:
            return model

+    def _assert_generated_batch_equal_expected(self, **tokenizer_kwargs):
+        generated_words = self.translate_src_text(**tokenizer_kwargs)
+        self.assertListEqual(self.expected_text, generated_words)
+
+    def translate_src_text(self, **tokenizer_kwargs):
+        model_inputs: dict = self.tokenizer.prepare_translation_batch(src_texts=self.src_text, **tokenizer_kwargs).to(
+            torch_device
+        )
+        self.assertEqual(self.model.device, model_inputs["input_ids"].device)
+        generated_ids = self.model.generate(
+            model_inputs["input_ids"], attention_mask=model_inputs["attention_mask"], num_beams=2
+        )
+        generated_words = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+        return generated_words
+
+
+class TestMarian_EN_DE_More(MarianIntegrationTest):
    @slow
    def test_forward(self):
-        src, tgt = ["I am a small frog"], ["▁Ich ▁bin ▁ein ▁kleiner ▁Fro sch"]
+        src, tgt = ["I am a small frog"], ["Ich bin ein kleiner Frosch."]
        expected = [38, 121, 14, 697, 38848, 0]

        model_inputs: dict = self.tokenizer.prepare_translation_batch(src, tgt_texts=tgt).to(torch_device)
@@ -62,57 +121,112 @@ class IntegrationTests(unittest.TestCase):
        with torch.no_grad():
            logits, *enc_features = self.model(**model_inputs)
        max_indices = logits.argmax(-1)
-        self.tokenizer.decode_batch(max_indices)
+        self.tokenizer.batch_decode(max_indices)

-    @slow
-    def test_repl_generate_one(self):
-        src = ["I am a small frog.", "Hello"]
-        model_inputs: dict = self.tokenizer.prepare_translation_batch(src).to(torch_device)
-        self.assertEqual(self.model.device, model_inputs["input_ids"].device)
-        generated_ids = self.model.generate(model_inputs["input_ids"], num_beams=6,)
-        generated_words = self.tokenizer.decode_batch(generated_ids)[0]
-        expected_words = "Ich bin ein kleiner Frosch."
-        self.assertEqual(expected_words, generated_words)
-
-    @slow
-    def test_repl_generate_batch(self):
-        src = [
-            "I am a small frog.",
-            "Now I can forget the 100 words of german that I know.",
-            "O",
-            "Tom asked his teacher for advice.",
-            "That's how I would do it.",
-            "Tom really admired Mary's courage.",
-            "Turn around and close your eyes.",
-        ]
-        model_inputs: dict = self.tokenizer.prepare_translation_batch(src).to(torch_device)
-        self.assertEqual(self.model.device, model_inputs["input_ids"].device)
-        generated_ids = self.model.generate(
-            model_inputs["input_ids"],
-            length_penalty=1.0,
-            num_beams=2,  # 6 is the default
-            bad_words_ids=[[self.tokenizer.pad_token_id]],
-        )
-        expected = [
-            "Ich bin ein kleiner Frosch.",
-            "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
-            "",
-            "Tom bat seinen Lehrer um Rat.",
-            "So würde ich das tun.",
-            "Tom bewunderte Marias Mut wirklich.",
-            "Umdrehen und die Augen schließen.",
-        ]
-        # actual C++ output differences: (1) des Deutschen removed, (2) ""-> "O", (3) tun -> machen
-        generated_words = self.tokenizer.decode_batch(generated_ids, skip_special_tokens=True)
-        self.assertListEqual(expected, generated_words)
-
-    def test_marian_equivalence(self):
+    def test_tokenizer_equivalence(self):
        batch = self.tokenizer.prepare_translation_batch(["I am a small frog"]).to(torch_device)
        input_ids = batch["input_ids"][0]
        expected = [38, 121, 14, 697, 38848, 0]
        self.assertListEqual(expected, input_ids.tolist())

+    def test_unk_support(self):
+        t = self.tokenizer
+        ids = t.prepare_translation_batch(["||"]).to(torch_device)["input_ids"][0].tolist()
+        expected = [t.unk_token_id, t.unk_token_id, t.eos_token_id]
+        self.assertEqual(expected, ids)
+
    def test_pad_not_split(self):
        input_ids_w_pad = self.tokenizer.prepare_translation_batch(["I am a small frog <pad>"])["input_ids"][0]
        expected_w_pad = [38, 121, 14, 697, 38848, self.tokenizer.pad_token_id, 0]  # pad
        self.assertListEqual(expected_w_pad, input_ids_w_pad.tolist())
+
+    @slow
+    def test_batch_generation_en_de(self):
+        self._assert_generated_batch_equal_expected()
+
+    def test_auto_config(self):
+        config = AutoConfig.from_pretrained(self.model_name)
+        self.assertIsInstance(config, MarianConfig)
+
+
+class TestMarian_EN_FR(MarianIntegrationTest):
+    src = "en"
+    tgt = "fr"
+    src_text = [
+        "I am a small frog.",
+        "Now I can forget the 100 words of german that I know.",
+    ]
+    expected_text = [
+        "Je suis une petite grenouille.",
+        "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
+    ]
+
+    @slow
+    def test_batch_generation_en_fr(self):
+        self._assert_generated_batch_equal_expected()
+
+
+class TestMarian_FR_EN(MarianIntegrationTest):
+    src = "fr"
+    tgt = "en"
+    src_text = [
+        "Donnez moi le micro.",
+        "Tom et Mary étaient assis à une table.",  # Accents
+    ]
+    expected_text = [
+        "Give me the microphone.",
+        "Tom and Mary were sitting at a table.",
+    ]
+
+    @slow
+    def test_batch_generation_fr_en(self):
+        self._assert_generated_batch_equal_expected()
+
+
+class TestMarian_RU_FR(MarianIntegrationTest):
+    src = "ru"
+    tgt = "fr"
+    src_text = ["Он показал мне рукопись своей новой пьесы."]
+    expected_text = ["Il me montre un manuscrit de sa nouvelle pièce."]
+
+    @slow
+    def test_batch_generation_ru_fr(self):
+        self._assert_generated_batch_equal_expected()
+
+
+class TestMarian_MT_EN(MarianIntegrationTest):
+    src = "mt"
+    tgt = "en"
+    src_text = ["Il - Babiloniżi b'mod żbaljat ikkonkludew li l - Alla l - veru kien dgħajjef."]
+    expected_text = ["The Babylonians wrongly concluded that the true God was weak."]
+
+    @unittest.skip("")  # Known Issue: This model generates a string of .... at the end of the translation.
+    def test_batch_generation_mt_en(self):
+        self._assert_generated_batch_equal_expected()
+
+
+class TestMarian_DE_Multi(MarianIntegrationTest):
+    src = "de"
+    tgt = "ch_group"
+    src_text = ["Er aber sprach: Das ist die Gottlosigkeit."]
+
+    @slow
+    def test_translation_de_multi_does_not_error(self):
+        self.translate_src_text()
+
+    @unittest.skip("")  # "Language codes are not yet supported."
+    def test_batch_generation_de_multi_tgt(self):
+        self._assert_generated_batch_equal_expected()
+
+    @unittest.skip("")  # "Language codes are not yet supported."
+    def test_lang_code(self):
+        t = "Er aber sprach"
+        zh_code = self.code
+        tok_fn = self.tokenizer.prepare_translation_batch
+        pass_code = tok_fn(src_texts=[t], tgt_lang_code=zh_code)["input_ids"][0]
+        preprocess_with_code = tok_fn(src_texts=[zh_code + "  " + t])["input_ids"][0]
+        self.assertListEqual(pass_code.tolist(), preprocess_with_code.tolist())
+        for code in self.tokenizer.supported_language_codes:
+            self.assertIn(code, self.tokenizer.encoder)
+        pass_only_code = tok_fn(src_texts=[""], tgt_lang_code=zh_code)["input_ids"][0].tolist()
+        self.assertListEqual(pass_only_code, [self.tokenizer.encoder[zh_code], self.tokenizer.eos_token_id])
Author	SHA1	Message	Date
Lysandre Debut	30e343862f	pin TF to 2.1 (#4297 ) * pin TF to 2.1 * Pin flake8 as well	2020-05-11 21:03:30 -04:00
Julien Chaumond	56e8ef632f	[ci] Restrict GPU tests to actual code commits	2020-05-11 20:40:41 -04:00
Julien Chaumond	ba6f6e44a8	[ci] Re-enable torch GPU tests	2020-05-12 00:05:36 +00:00
Lysandre Debut	9524956819	Documentation specification (#4294 )	2020-05-11 16:43:57 -04:00
Bram Vanroy	61d22f9cc7	Simplify cache vars and allow for TRANSFORMERS_CACHE env (#4226 ) * simplify cache vars and allow for TRANSFORMERS_CACHE env As it currently stands, "TRANSFORMERS_CACHE" is not an accepted variable. It seems that the these variables were not updated when moving from version pytorch_transformers to transformers. In addition, the fallback procedure could be improved. and simplified. Pathlib seems redundant here. * Update file_utils.py	2020-05-11 15:24:02 -04:00
Lysandre Debut	cd40cb8879	Fix special token doc (#4292 )	2020-05-11 15:05:36 -04:00
Tianlei Wu	82601f4c1a	Allow gpt2 to be exported to valid ONNX (#4244 ) * allow gpt2 to be exported to valid ONNX model * cast size from int to float explictly	2020-05-11 14:55:55 -04:00
Guo, Quan	39994051e4	Add migrating from `pytorch-transformers` (#4273 ) "Migrating from pytorch-transformers to transformers" is missing in the main document. It is available in the main `readme` thought. Just move it to the document.	2020-05-11 13:35:13 -04:00
Lysandre Debut	051dcb2a07	CamemBERT does not make use of Token Type IDs (#4289 )	2020-05-11 13:31:03 -04:00
fgaim	41e8291217	Add ALBERT to the Tensorflow to Pytorch model conversion cli (#3933 ) * Add ALBERT to convert command of transformers-cli * Document ALBERT tf to pytorch model conversion	2020-05-11 13:10:00 -04:00
Stefan Schweter	3f42eb979f	Documentation: fix links to NER examples (#4279 ) * docs: fix link to token classification (NER) example * examples: fix links to NER scripts	2020-05-11 12:48:21 -04:00
Funtowicz Morgan	8fdb7997c6	Align sentiment-analysis' tokenizer (currently uncased) to the model (uncased). (#4264 )	2020-05-11 12:45:53 -04:00
Sam Shleifer	4658896ee1	[Marian] Fix typo in docstring (#4284 )	2020-05-11 11:47:51 -04:00
Levent SerinolandJulien Chaumond	bf64b8cf09	Model card for bert-turkish-question-answering question-answering model (#4281 ) * Create README.md * Update model_cards/lserinol/bert-turkish-question-answering/README.md Co-authored-by: Julien Chaumond <chaumond@gmail.com>	2020-05-11 11:32:25 -04:00
Julien Plu	94b57bf796	[TF 2.2 compat] use tf.VariableAggregation.ONLY_FIRST_REPLICA (#4283 ) * Fix the issue to properly run the accumulator with TF 2.2 * Apply style * Fix training_args_tf for TF 2.2 * Fix the TF training args when only one GPU is available * Remove the fixed version of TF in setup.py	2020-05-11 11:28:37 -04:00
Savaş Yıldırım	cffbb3d8ed	Update README.md (#4276 )	2020-05-11 11:24:41 -04:00
Julien Plu	5f50d619dd	Fix XTREME link + add number of eval documents + fix usage code (#4280 )	2020-05-11 11:24:10 -04:00
theblackcat102	7751be7cee	fix reformer apex scaling issue (#4242 )	2020-05-11 16:53:42 +02:00
Patrick von Platen	ac7d5f67a2	[Reformer] Add Enwiki8 Reformer Model - Adapt convert script (#4282 ) * adapt convert script * update convert script * finish * fix marian pretrained docs	2020-05-11 16:38:07 +02:00
Patrick von Platen	336116d960	Reformer enwik8 - Model card (#4286 )	2020-05-11 16:22:08 +02:00
flozi00	b290c32e16	[docs] fix typo (#4249 )	2020-05-10 14:07:08 -04:00
Sam Shleifer	3487be75ef	[Marian] documentation and AutoModel support (#4152 ) - MarianSentencepieceTokenizer - > MarianTokenizer - Start using unk token. - add docs page - add better generation params to MarianConfig - more conversion utilities	2020-05-10 13:54:57 -04:00
Girishkumar	9d2f467bfb	[README] Corrected some grammatical mistakes (#4199 )	2020-05-10 09:02:36 -04:00