Release: v2.4.0

[Umberto] model shortcuts (#2661 )
* [Umberto] model shortcuts cc @loretoparisi @simonefrancia see #2485 * Ensure that tokenizers will be correctly configured
2020-01-31 09:40:32 -05:00 · 2020-01-30 21:05:53 -05:00 · 2020-01-30 20:05:04 -05:00 · 2020-01-30 19:26:59 -05:00 · 2020-01-30 19:26:59 -05:00 · 2020-01-30 19:26:59 -05:00
61 changed files with 2265 additions and 381 deletions
@@ -1,17 +1,17 @@
 ---
-name: "\U0001F5A5 New Benchmark"
-about: You benchmark a part of this library and would like to share your results
+name: "\U0001F5A5 New benchmark"
+about: Benchmark a part of this library and share your results
 title: "[Benchmark]"
 labels: ''
 assignees: ''

 ---

-# Benchmarking Transformers
+# 🖥 Benchmarking `transformers`

 ## Benchmark

-Which part of Transformers did you benchmark?
+Which part of `transformers` did you benchmark?

 ## Set-up

@@ -1,5 +1,5 @@
 ---
-name: "\U0001F31FNew model addition"
+name: "\U0001F31F New model addition"
 about: Submit a proposal/request to implement a new Transformer-based model
 title: ''
 labels: ''
@@ -7,18 +7,14 @@ assignees: ''

 ---

-# 🌟New model addition
+# 🌟 New model addition

 ## Model description

 <!-- Important information -->

-## Open Source status
+## Open source status

 * [ ] the model implementation is available: (give details)
 * [ ] the model weights are available: (give details)
-* [ ] who are the authors: (mention them)
-
-## Additional context
-
-<!-- Add any other context about the problem here. -->
+* [ ] who are the authors: (mention them, if possible by @gh-username)
@@ -1,29 +1,29 @@
 ---
 name: "\U0001F41B Bug Report"
-about: Submit a bug report to help us improve PyTorch Transformers
+about: Submit a bug report to help us improve transformers
 title: ''
 labels: ''
 assignees: ''

 ---

-## 🐛 Bug
+# 🐛 Bug

-<!-- Important information -->
+## Information

-Model I am using (Bert, XLNet....):
+Model I am using (Bert, XLNet ...):

-Language I am using the model on (English, Chinese....):
+Language I am using the model on (English, Chinese ...):

-The problem arise when using:
-* [ ] the official example scripts: (give details)
-* [ ] my own modified scripts: (give details)
+The problem arises when using:
+* [ ] the official example scripts: (give details below)
+* [ ] my own modified scripts: (give details below)

 The tasks I am working on is:
 * [ ] an official GLUE/SQUaD task: (give the name)
-* [ ] my own task or dataset: (give details)
+* [ ] my own task or dataset: (give details below)

-## To Reproduce
+## To reproduce

 Steps to reproduce the behavior:

@@ -31,22 +31,20 @@ Steps to reproduce the behavior:
 2.
 3.

-<!-- If you have a code sample, error messages, stack traces, please provide it here as well. -->
+<!-- If you have code snippets, error messages, stack traces please provide them here as well.
+     Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
+     Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.-->

 ## Expected behavior

-<!-- A clear and concise description of what you expected to happen. -->
+<!-- A clear and concise description of what you would expect to happen. -->

 ## Environment

 * OS:
 * Python version:
 * PyTorch version:
-* PyTorch Transformers version (or branch):
+* `transformers` version (or branch):
 * Using GPU ?
 * Distributed or parallel setup ?
 * Any other relevant information:
-
-## Additional context
-
-<!-- Add any other context about the problem here. -->
@@ -1,20 +1,25 @@
 ---
-name: "\U0001F680 Feature Request"
-about: Submit a proposal/request for a new PyTorch Transformers feature
+name: "\U0001F680 Feature request"
+about: Submit a proposal/request for a new transformers feature
 title: ''
 labels: ''
 assignees: ''

 ---

-## 🚀 Feature
+# 🚀 Feature request

-<!-- A clear and concise description of the feature proposal. Please provide a link to the paper and code in case they exist. -->
+<!-- A clear and concise description of the feature proposal.
+     Please provide a link to the paper and code in case they exist. -->

 ## Motivation

-<!-- Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too. -->
+<!-- Please outline the motivation for the proposal. Is your feature request
+     related to a problem? e.g., I'm always frustrated when [...]. If this is related
+     to another GitHub issue, please link here too. -->

-## Additional context
+## Your contribution

-<!-- Add any other context or screenshots about the feature request here. -->
+<!-- Is there any way that you could help, e.g. by submitting a PR?
+     Make sure to read the CONTRIBUTING.MD readme:
+     https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md -->
@@ -1,47 +1,52 @@
 ---
-name: "\U0001F4DA Migration from PyTorch-pretrained-Bert"
-about: Report a problem when migrating from PyTorch-pretrained-Bert to Transformers
+name: "\U0001F4DA Migration from pytorch-pretrained-bert or pytorch-transformers"
+about: Report a problem when migrating from pytorch-pretrained-bert or pytorch-transformers to transformers
 title: ''
 labels: ''
 assignees: ''

 ---

-## 📚 Migration
+# 📚 Migration
+
+## Information

 <!-- Important information -->

-Model I am using (Bert, XLNet....):
+Model I am using (Bert, XLNet ...):

-Language I am using the model on (English, Chinese....):
+Language I am using the model on (English, Chinese ...):

-The problem arise when using:
-* [ ] the official example scripts: (give details)
-* [ ] my own modified scripts: (give details)
+The problem arises when using:
+* [ ] the official example scripts: (give details below)
+* [ ] my own modified scripts: (give details below)

 The tasks I am working on is:
 * [ ] an official GLUE/SQUaD task: (give the name)
-* [ ] my own task or dataset: (give details)
+* [ ] my own task or dataset: (give details below)

-Details of the issue:
+## Details

-<!-- A clear and concise description of the migration issue. If you have code snippets, please provide it here as well. -->
+<!-- A clear and concise description of the migration issue.
+    If you have code snippets, please provide it here as well.
+    Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
+    Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
+    -->

 ## Environment

 * OS:
 * Python version:
 * PyTorch version:
-* PyTorch Transformers version (or branch):
-* Using GPU ?
-* Distributed or parallel setup ?
+* `pytorch-transformers` or `pytorch-pretrained-bert` version (or branch):
+* `transformers` version (or branch):
+* Using GPU?
+* Distributed or parallel setup?
 * Any other relevant information:

 ## Checklist

 - [ ] I have read the migration guide in the readme.
+ ([pytorch-transformers](https://github.com/huggingface/transformers#migrating-from-pytorch-transformers-to-transformers);
+  [pytorch-pretrained-bert](https://github.com/huggingface/transformers#migrating-from-pytorch-pretrained-bert-to-transformers))
 - [ ] I checked if a related official extension example runs on my machine.
-
-## Additional context
-
-<!-- Add any other context about the problem here. -->
@@ -1,12 +1,29 @@
 ---
-name: "❓Questions & Help"
-about: Start a general discussion related to PyTorch Transformers
+name: "❓ Questions & Help"
+about: Post your general questions on Stack Overflow tagged huggingface-transformers
 title: ''
 labels: ''
 assignees: ''

 ---

-## ❓ Questions & Help
+# ❓ Questions & Help

-<!-- A clear and concise description of the question. -->
+<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
+     new models and benchmarks, and migration questions. For all other questions,
+     we direct you to Stack Overflow (SO) where a whole community of PyTorch and
+     Tensorflow enthusiast can help you out. Make sure to tag your question with the
+     right deep learning framework as well as the huggingface-transformers tag: 
+     https://stackoverflow.com/questions/tagged/huggingface-transformers 
+     
+     If your question wasn't answered after a period of time on Stack Overflow, you
+     can always open a question on GitHub. You should then link to the SO question 
+     that you posted.
+     -->
+
+## Details
+<!-- Description of your issue -->
+
+<!-- You should first ask your question on SO, and only if
+     you didn't get an answer ask it here on GitHub. -->
+**A link to original question on Stack Overflow**: 
@@ -160,8 +160,9 @@ At some point in the future, you'll be able to seamlessly move from pre-training
 12. **[T5](https://github.com/google-research/text-to-text-transfer-transformer)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
 13. **[XLM-RoBERTa](https://github.com/pytorch/fairseq/tree/master/examples/xlmr)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
 14. **[MMBT](https://github.com/facebookresearch/mmbt/)** (from Facebook), released together with the paper a [Supervised Multimodal Bitransformers for Classifying Images and Text](https://arxiv.org/pdf/1909.02950.pdf) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.
-15. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
-16. 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.
+15. **[FlauBERT](https://github.com/getalp/Flaubert)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
+16. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
+17. 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).

@@ -520,8 +521,9 @@ You can create `Pipeline` objects for the following down-stream tasks:
 - `feature-extraction`: Generates a tensor representation for the input sequence
 - `ner`: Generates named entity mapping for each word in the input sequence.
 - `sentiment-analysis`: Gives the polarity (positive / negative) of the whole input sequence.
- - `question-answering`: Provided some context and a question refering to the context, it will extract the answer to the question
- in the context.
+ - `text-classification`: Initialize a `TextClassificationPipeline` directly, or see `sentiment-analysis` for an example.
+ - `question-answering`: Provided some context and a question refering to the context, it will extract the answer to the question in the context.
+ - `fill-mask`: Takes an input sequence containing a masked token (e.g. `<mask>`) and return list of most probable filled sequences, with their probabilities.

 ```python
 from transformers import pipeline
@@ -26,7 +26,7 @@ author = u'huggingface'
 # The short X.Y version
 version = u''
 # The full version, including alpha/beta/rc tags
-release = u'2.3.0'
+release = u'2.4.0'


 # -- General configuration ---------------------------------------------------
@@ -51,6 +51,7 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
 10. `CamemBERT <https://huggingface.co/transformers/model_doc/camembert.html>`_ (from FAIR, Inria, Sorbonne Université) released together with the paper `CamemBERT: a Tasty French Language Model <https://arxiv.org/abs/1911.03894>`_ by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suarez, Yoann Dupont, Laurent Romary, Eric Villemonte de la Clergerie, Djame Seddah, and Benoît Sagot.
 11. `ALBERT <https://github.com/google-research/ALBERT>`_ (from Google Research), released together with the paper a `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations <https://arxiv.org/abs/1909.11942>`_ by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
 12. `XLM-RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/xlmr>`_ (from Facebook AI), released together with the paper `Unsupervised Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`_ by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
+13. `FlauBERT <https://github.com/getalp/Flaubert>`_ (from CNRS) released with the paper `FlauBERT: Unsupervised Language Model Pre-training for French <https://arxiv.org/abs/1912.05372>`_ by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.

 .. toctree::
    :maxdepth: 2
@@ -97,4 +98,5 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
    model_doc/ctrl
    model_doc/camembert
    model_doc/albert
-    model_doc/xlmroberta
+    model_doc/xlmroberta
+    model_doc/flaubert
@@ -3,7 +3,7 @@ AutoModels

 In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you are supplying to the ``from_pretrained`` method.

-AutoClasses are here to do this job for you so that you automatically retreive the relevant model given the name/path to the pretrained weights/config/vocabulary:
+AutoClasses are here to do this job for you so that you automatically retrieve the relevant model given the name/path to the pretrained weights/config/vocabulary:

 Instantiating one of ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer`` will directly create a class of the relevant architecture (ex: ``model = AutoModel.from_pretrained('bert-base-cased')`` will create a instance of ``BertModel``).

@@ -15,6 +15,13 @@ Instantiating one of ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer`` will di
    :members:


+``AutoTokenizer``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AutoTokenizer
+    :members:
+
+
 ``AutoModel``
 ~~~~~~~~~~~~~~~~~~~~~

@@ -22,8 +29,37 @@ Instantiating one of ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer`` will di
    :members:


-``AutoTokenizer``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+``AutoModelForPreTraining``
+~~~~~~~~~~~~~~~~~~~~~

-.. autoclass:: transformers.AutoTokenizer
+.. autoclass:: transformers.AutoModelForPreTraining
    :members:
+
+
+``AutoModelWithLMHead``
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AutoModelWithLMHead
+    :members:
+
+
+``AutoModelForSequenceClassification``
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AutoModelForSequenceClassification
+    :members:
+
+
+``AutoModelForQuestionAnswering``
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AutoModelForQuestionAnswering
+    :members:
+
+
+``AutoModelForTokenClassification``
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AutoModelForTokenClassification
+    :members:
+
@@ -69,3 +69,31 @@ CamembertForTokenClassification

 .. autoclass:: transformers.CamembertForTokenClassification
    :members:
+
+
+TFCamembertModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFCamembertModel
+    :members:
+
+
+TFCamembertForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFCamembertForMaskedLM
+    :members:
+
+
+TFCamembertForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFCamembertForSequenceClassification
+    :members:
+
+
+TFCamembertForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFCamembertForTokenClassification
+    :members:
@@ -0,0 +1,72 @@
+FlauBERT
+----------------------------------------------------
+
+The FlauBERT model was proposed in the paper
+`FlauBERT: Unsupervised Language Model Pre-training for French <https://arxiv.org/abs/1912.05372>`__ by Hang Le et al.
+It's a transformer pre-trained using a masked language modeling (MLM) objective (BERT-like).
+
+The abstract from the paper is the following:
+
+*Language models have become a key step to achieve state-of-the art results in many different Natural Language
+Processing (NLP) tasks. Leveraging the huge amount of unlabeled texts nowadays available, they provide an efficient
+way to pre-train continuous word representations that can be fine-tuned for a downstream task, along with their
+contextualization at the sentence level. This has been widely demonstrated for English using contextualized
+representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018; Radford et al., 2018; Devlin et
+al., 2019; Yang et al., 2019b). In this paper, we introduce and share FlauBERT, a model learned on a very large
+and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre
+for Scientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (text
+classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most
+of the time they outperform other pre-training approaches. Different versions of FlauBERT as well as a unified
+evaluation protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared
+to the research community for further reproducible experiments in French NLP.*
+
+
+FlaubertConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertConfig
+    :members:
+
+
+FlaubertTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertTokenizer
+    :members:
+
+
+FlaubertModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertModel
+    :members:
+
+
+FlaubertWithLMHeadModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertWithLMHeadModel
+    :members:
+
+
+FlaubertForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertForSequenceClassification
+    :members:
+
+
+FlaubertForQuestionAnsweringSimple
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertForQuestionAnsweringSimple
+    :members:
+
+
+FlaubertForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertForQuestionAnswering
+    :members:
+
+
@@ -73,3 +73,30 @@ XLMRobertaForTokenClassification
 .. autoclass:: transformers.XLMRobertaForTokenClassification
    :members:

+
+TFXLMRobertaModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFXLMRobertaModel
+    :members:
+
+
+TFXLMRobertaForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFXLMRobertaForMaskedLM
+    :members:
+
+
+TFXLMRobertaForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFXLMRobertaForSequenceClassification
+    :members:
+
+
+TFXLMRobertaForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFXLMRobertaForTokenClassification
+    :members:
@@ -88,6 +88,10 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
 |                   | ``bert-base-finnish-uncased-v1``                           | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on uncased Finnish text.                                                                                                    |
 |                   |                                                            | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__).                                                                     |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``bert-base-dutch-cased``                                  | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                   |                                                            | | Trained on cased Dutch text.                                                                                                        |
+|                   |                                                            | (see `details on wietsedv repository <https://github.com/wietsedv/bertje/>`__).                                                       |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | GPT               | ``openai-gpt``                                             | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | OpenAI GPT English model                                                                                                            |
@@ -247,6 +251,22 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
 |                   | ``xlm-roberta-large``                                      | | ~355M parameters with 24-layers, 1027-hidden-state, 4096 feed-forward hidden-state, 16-heads,                                       |
 |                   |                                                            | | Trained on 2.5 TB of newly created clean CommonCrawl data in 100 languages                                                          |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+| FlauBERT          | ``flaubert-small-cased``                                   | | 6-layer, 512-hidden, 8-heads, 54M parameters                                                                                        |
+|                   |                                                            | | FlauBERT small architecture                                                                                                         |
+|                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``flaubert-base-uncased``                                  | | 12-layer, 768-hidden, 12-heads, 137M parameters                                                                                     |
+|                   |                                                            | | FlauBERT base architecture with uncased vocabulary                                                                                  |
+|                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``flaubert-base-cased``                                    | | 12-layer, 768-hidden, 12-heads, 138M parameters                                                                                     |
+|                   |                                                            | | FlauBERT base architecture with cased vocabulary                                                                                    |
+|                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``flaubert-large-cased``                                   | | 24-layer, 1024-hidden, 16-heads, 373M parameters                                                                                    |
+|                   |                                                            | | FlauBERT large architecture                                                                                                         |
+|                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+


 .. <https://huggingface.co/transformers/examples.html>`__
@@ -135,21 +135,21 @@ Fine-tuning the library models for sequence classification on the GLUE benchmark
 Evaluation](https://gluebenchmark.com/). This script can fine-tune the following models: BERT, XLM, XLNet and RoBERTa. 

 GLUE is made up of a total of 9 different tasks. We get the following results on the dev set of the benchmark with an
-uncased  BERT base model (the checkpoint `bert-base-uncased`). All experiments ran on 8 V100 GPUs with a total train
-batch size of 24. Some of these tasks have a small dataset and training can lead to high variance in the results
+uncased  BERT base model (the checkpoint `bert-base-uncased`). All experiments ran single V100 GPUs with a total train
+batch sizes between 16 and 64. Some of these tasks have a small dataset and training can lead to high variance in the results
 between different runs. We report the median on 5 runs (with different seeds) for each of the metrics.

 | Task  | Metric                       | Result      |
 |-------|------------------------------|-------------|
-| CoLA  | Matthew's corr               | 48.87       |
-| SST-2 | Accuracy                     | 91.74       |
-| MRPC  | F1/Accuracy                  | 90.70/86.27 |
-| STS-B | Person/Spearman corr.        | 91.39/91.04 |
-| QQP   | Accuracy/F1                  | 90.79/87.66 |
-| MNLI  | Matched acc./Mismatched acc. | 83.70/84.83 |
-| QNLI  | Accuracy                     | 89.31       |
-| RTE   | Accuracy                     | 71.43       |
-| WNLI  | Accuracy                     | 43.66       |
+| CoLA  | Matthew's corr               | 49.23       |
+| SST-2 | Accuracy                     | 91.97       |
+| MRPC  | F1/Accuracy                  | 89.47/85.29 |
+| STS-B | Person/Spearman corr.        | 83.95/83.70 |
+| QQP   | Accuracy/F1                  | 88.40/84.31 |
+| MNLI  | Matched acc./Mismatched acc. | 80.61/81.08 |
+| QNLI  | Accuracy                     | 87.46       |
+| RTE   | Accuracy                     | 61.73       |
+| WNLI  | Accuracy                     | 45.07       |

 Some of these results are significantly different from the ones reported on the test set
 of GLUE benchmark on the website. For QQP and WNLI, please refer to [FAQ #12](https://gluebenchmark.com/faq) on the webite.
@@ -404,12 +404,12 @@ exact_match = 81.22
 #### Distributed training


-Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD1.0:
+Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD1.1:

 ```bash
-python -m torch.distributed.launch --nproc_per_node=8 run_squad.py \
+python -m torch.distributed.launch --nproc_per_node=8 ./examples/run_squad.py \
    --model_type bert \
-    --model_name_or_path bert-base-cased \
+    --model_name_or_path bert-large-uncased-whole-word-masking \
    --do_train \
    --do_eval \
    --do_lower_case \
@@ -419,9 +419,9 @@ python -m torch.distributed.launch --nproc_per_node=8 run_squad.py \
    --num_train_epochs 2 \
    --max_seq_length 384 \
    --doc_stride 128 \
-    --output_dir ../models/wwm_uncased_finetuned_squad/ \
-    --per_gpu_train_batch_size 24 \
-    --gradient_accumulation_steps 12
+    --output_dir ./examples/models/wwm_uncased_finetuned_squad/ \
+    --per_gpu_eval_batch_size=3   \
+    --per_gpu_train_batch_size=3   \
 ```

 Training with the previously defined hyper-parameters yields the following results:
@@ -2,23 +2,25 @@

 This folder contains the original code used to train Distil* as well as examples showcasing how to use DistilBERT, DistilRoBERTa and DistilGPT2.

-**December 6th, 2019 - Update** We release **DistilmBERT**: 92% of `bert-base-multilingual-cased` on XNLI. The model supports 104 different languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).
+**January 20, 2020 - Bug fixing** We have recently discovered and fixed [a bug](https://github.com/huggingface/transformers/commit/48cbf267c988b56c71a2380f748a3e6092ccaed3) in the evaluation of our `run_*.py` scripts that caused the reported metrics to be over-estimated on average. We have updated all the metrics with the latest runs.

-**November 19th, 2019 - Update** We release German **DistilBERT**: 98.8% of `bert-base-german-dbmdz-cased` on NER tasks.
+**December 6, 2019 - Update** We release **DistilmBERT**: 92% of `bert-base-multilingual-cased` on XNLI. The model supports 104 different languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).

-**October 23rd, 2019 - Update** We release **DistilRoBERTa**: 95% of `RoBERTa-base`'s performance on GLUE, twice as fast as RoBERTa while being 35% smaller.
+**November 19, 2019 - Update** We release German **DistilBERT**: 98.8% of `bert-base-german-dbmdz-cased` on NER tasks.

-**October 3rd, 2019 - Update** We release our [NeurIPS workshop paper](https://arxiv.org/abs/1910.01108) explaining our approach on **DistilBERT**. It includes updated results and further experiments. We applied the same method to GPT2 and release the weights of **DistilGPT2**. DistilGPT2 is two times faster and 33% smaller than GPT2. **The paper superseeds our [previous blogpost](https://medium.com/huggingface/distilbert-8cf3380435b5) with a different distillation loss and better performances. Please use the paper as a reference when comparing/reporting results on DistilBERT.**
+**October 23, 2019 - Update** We release **DistilRoBERTa**: 95% of `RoBERTa-base`'s performance on GLUE, twice as fast as RoBERTa while being 35% smaller.

-**September 19th, 2019 - Update:** We fixed bugs in the code and released an upadted version of the weights trained with a modification of the distillation loss. DistilBERT now reaches 97% of `BERT-base`'s performance on GLUE, and 86.9 F1 score on SQuAD v1.1 dev set (compared to 88.5 for `BERT-base`). We will publish a formal write-up of our approach in the near future!
+**October 3, 2019 - Update** We release our [NeurIPS workshop paper](https://arxiv.org/abs/1910.01108) explaining our approach on **DistilBERT**. It includes updated results and further experiments. We applied the same method to GPT2 and release the weights of **DistilGPT2**. DistilGPT2 is two times faster and 33% smaller than GPT2. **The paper superseeds our [previous blogpost](https://medium.com/huggingface/distilbert-8cf3380435b5) with a different distillation loss and better performances. Please use the paper as a reference when comparing/reporting results on DistilBERT.**
+
+**September 19, 2019 - Update:** We fixed bugs in the code and released an upadted version of the weights trained with a modification of the distillation loss. DistilBERT now reaches 99% of `BERT-base`'s performance on GLUE, and 86.9 F1 score on SQuAD v1.1 dev set (compared to 88.5 for `BERT-base`). We will publish a formal write-up of our approach in the near future!


 ## What is Distil*

-Distil* is a class of compressed models that started with DistilBERT. DistilBERT stands for Distillated-BERT. DistilBERT is a small, fast, cheap and light Transformer model based on Bert architecture. It has 40% less parameters than `bert-base-uncased`, runs 60% faster while preserving 97% of BERT's performances as measured on the GLUE language understanding benchmark. DistilBERT is trained using knowledge distillation, a technique to compress a large model called the teacher into a smaller model called the student. By distillating Bert, we obtain a smaller Transformer model that bears a lot of similarities with the original BERT model while being lighter, smaller and faster to run. DistilBERT is thus an interesting option to put large-scaled trained Transformer model into production.
+Distil* is a class of compressed models that started with DistilBERT. DistilBERT stands for Distillated-BERT. DistilBERT is a small, fast, cheap and light Transformer model based on Bert architecture. It has 40% less parameters than `bert-base-uncased`, runs 60% faster while preserving 99% of BERT's performances as measured on the GLUE language understanding benchmark. DistilBERT is trained using knowledge distillation, a technique to compress a large model called the teacher into a smaller model called the student. By distillating Bert, we obtain a smaller Transformer model that bears a lot of similarities with the original BERT model while being lighter, smaller and faster to run. DistilBERT is thus an interesting option to put large-scaled trained Transformer model into production.

 We have applied the same method to other Transformer architectures and released the weights:
- GPT2: on the [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) benchmark, GPT2 reaches a perplexity on the test set of 15.0 compared to 18.5 for **DistilGPT2** (after fine-tuning on the train set).
+- GPT2: on the [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) benchmark, GPT2 reaches a perplexity on the test set of 16.3 compared to 21.1 for **DistilGPT2** (after fine-tuning on the train set).
 - RoBERTa: **DistilRoBERTa** reaches 95% of `RoBERTa-base`'s performance on GLUE while being twice faster and 35% smaller.
 - German BERT: **German DistilBERT** reaches 99% of `bert-base-german-dbmdz-cased`'s performance on German NER (CoNLL-2003).
 - Multilingual BERT: **DistilmBERT** reaches 92% of Multilingual BERT's performance on XNLI while being twice faster and 25% smaller. The model supports 104 languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).
@@ -29,11 +31,11 @@ Here are the results on the dev sets of GLUE:

 | Model                     | Macro-score                    | CoLA | MNLI | MRPC | QNLI | QQP  | RTE  | SST-2| STS-B| WNLI              |
 | :---:                     |    :---:                       | :---:| :---:| :---:| :---:| :---:| :---:| :---:| :---:| :---:             |
-| BERT-base                 |  **77.6**                      | 48.9 | 84.3 | 88.6 | 89.3 | 89.5 | 71.3 | 91.7 | 91.2 | 43.7              |
-| DistilBERT                |  **76.8**                      | 49.1 | 81.8 | 90.2 | 90.2 | 89.2 | 62.9 | 92.7 | 90.7 | 44.4              |
+| BERT-base-uncased         |  **77.6**                      | 49.2 | 80.8 | 87.4 | 87.5 | 86.4 | 61.7 | 92.0 | 83.8 | 45.1              |
+| DistilBERT-base-uncased   |  **76.8**                      | 43.6 | 79.0 | 87.5 | 85.3 | 84.9 | 59.9 | 90.7 | 81.2 | 56.3              |
 | ---                       |    ---                         |  --- |  --- |  --- |  --- |  --- |  --- |  --- |  --- |  ---              |
 | RoBERTa-base (reported)   |  **83.2**/**86.4**<sup>2</sup> | 63.6 | 87.6 | 90.2 | 92.8 | 91.9 | 78.7 | 94.8 | 91.2 | 57.7<sup>3</sup>  |
-| DistilRoBERTa<sup>1</sup> |  **79.0**/**82.3**<sup>2</sup> | 59.4 | 83.9 | 86.6 | 90.8 | 89.4 | 67.9 | 92.5 | 88.3 | 52.1              |
+| DistilRoBERTa<sup>1</sup> |  **79.0**/**82.3**<sup>2</sup> | 59.3 | 84.0 | 86.6 | 90.8 | 89.4 | 67.9 | 92.5 | 88.3 | 52.1              |

 <sup>1</sup> We did not use the MNLI checkpoint for fine-tuning but directy perform transfer learning on the pre-trained DistilRoBERTa.

@@ -41,6 +41,9 @@ from transformers import (
    DistilBertConfig,
    DistilBertForSequenceClassification,
    DistilBertTokenizer,
+    FlaubertConfig,
+    FlaubertForSequenceClassification,
+    FlaubertTokenizer,
    RobertaConfig,
    RobertaForSequenceClassification,
    RobertaTokenizer,
@@ -80,6 +83,7 @@ ALL_MODELS = sum(
            DistilBertConfig,
            AlbertConfig,
            XLMRobertaConfig,
+            FlaubertConfig,
        )
    ),
    (),
@@ -93,6 +97,7 @@ MODEL_CLASSES = {
    "distilbert": (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer),
    "albert": (AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer),
    "xlmroberta": (XLMRobertaConfig, XLMRobertaForSequenceClassification, XLMRobertaTokenizer),
+    "flaubert": (FlaubertConfig, FlaubertForSequenceClassification, FlaubertTokenizer),
 }


@@ -480,7 +485,7 @@ def main():
    parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
    parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
    parser.add_argument(
-        "--evaluate_during_training", action="store_true", help="Rul evaluation during training at each logging step.",
+        "--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step.",
    )
    parser.add_argument(
        "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model.",
@@ -513,8 +518,8 @@ def main():
    )
    parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")

-    parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
-    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
+    parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.")
+    parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.")
    parser.add_argument(
        "--eval_all_checkpoints",
        action="store_true",
@@ -202,8 +202,9 @@ def mask_tokens(inputs: torch.Tensor, tokenizer: PreTrainedTokenizer, args) -> T
        tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
    ]
    probability_matrix.masked_fill_(torch.tensor(special_tokens_mask, dtype=torch.bool), value=0.0)
-    padding_mask = labels.eq(tokenizer.pad_token_id)
-    probability_matrix.masked_fill_(padding_mask, value=0.0)
+    if tokenizer._pad_token is not None:
+        padding_mask = labels.eq(tokenizer.pad_token_id)
+        probability_matrix.masked_fill_(padding_mask, value=0.0)
    masked_indices = torch.bernoulli(probability_matrix).bool()
    labels[~masked_indices] = -100  # We only compute loss on masked tokens

@@ -228,6 +229,8 @@ def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedToke
    args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)

    def collate(examples: List[torch.Tensor]):
+        if tokenizer._pad_token is None:
+            return pad_sequence(examples, batch_first=True)
        return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)

    train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
@@ -421,6 +424,8 @@ def evaluate(args, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, prefi
    # Note that DistributedSampler samples randomly

    def collate(examples: List[torch.Tensor]):
+        if tokenizer._pad_token is None:
+            return pad_sequence(examples, batch_first=True)
        return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)

    eval_sampler = SequentialSampler(eval_dataset)
@@ -569,8 +574,8 @@ def main():
    )
    parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")

-    parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
-    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
+    parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.")
+    parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.")
    parser.add_argument(
        "--save_total_limit",
        type=int,
@@ -478,8 +478,8 @@ def main():
    )
    parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")

-    parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
-    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
+    parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.")
+    parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.")
    parser.add_argument(
        "--eval_all_checkpoints",
        action="store_true",
@@ -485,8 +485,8 @@ def main():
    )
    parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")

-    parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
-    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
+    parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.")
+    parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.")
    parser.add_argument(
        "--eval_all_checkpoints",
        action="store_true",
@@ -636,8 +636,8 @@ def main():
        "A number of warnings are expected for a normal SQuAD evaluation.",
    )

-    parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
-    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
+    parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.")
+    parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.")
    parser.add_argument(
        "--eval_all_checkpoints",
        action="store_true",
@@ -99,9 +99,6 @@ if TASK == "mrpc":
    inputs_1 = tokenizer.encode_plus(sentence_0, sentence_1, add_special_tokens=True, return_tensors="pt")
    inputs_2 = tokenizer.encode_plus(sentence_0, sentence_2, add_special_tokens=True, return_tensors="pt")

-    del inputs_1["special_tokens_mask"]
-    del inputs_2["special_tokens_mask"]
-
    pred_1 = pytorch_model(**inputs_1)[0].argmax().item()
    pred_2 = pytorch_model(**inputs_2)[0].argmax().item()
    print("sentence_1 is", "a paraphrase" if pred_1 else "not a paraphrase", "of sentence_0")
@@ -473,8 +473,8 @@ def main():
    )
    parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")

-    parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
-    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
+    parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.")
+    parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.")
    parser.add_argument(
        "--eval_all_checkpoints",
        action="store_true",
@@ -63,7 +63,7 @@ extras["sklearn"] = ["scikit-learn"]
 extras["tf"] = ["tensorflow"]
 extras["torch"] = ["torch"]

-extras["serving"] = ["pydantic", "uvicorn", "fastapi"]
+extras["serving"] = ["pydantic", "uvicorn", "fastapi", "starlette"]
 extras["all"] = extras["serving"] + ["tensorflow", "torch"]

 extras["testing"] = ["pytest", "pytest-xdist"]
@@ -73,7 +73,7 @@ extras["dev"] = extras["testing"] + extras["quality"] + ["mecab-python3", "sciki

 setup(
    name="transformers",
-    version="2.3.0",
+    version="2.4.0",
    author="Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Google AI Language Team Authors, Open AI team Authors, Facebook AI Authors, Carnegie Mellon University Authors",
    author_email="thomas@huggingface.co",
    description="State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch",
@@ -2,7 +2,7 @@
 # There's no way to ignore "F401 '...' imported but unused" warnings in this
 # module, but to preserve other warnings. So, don't check this module at all.

-__version__ = "2.3.0"
+__version__ = "2.4.0"

 # Work around to update TensorFlow's absl.logging threshold which alters the
 # default Python logging output behavior when present.
@@ -25,6 +25,7 @@ from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig
 from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig
 from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
 from .configuration_distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig
+from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig
 from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config
 from .configuration_mmbt import MMBTConfig
 from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
@@ -92,6 +93,7 @@ from .modeling_tf_pytorch_utils import (
 from .pipelines import (
    CsvPipelineDataFormat,
    FeatureExtractionPipeline,
+    FillMaskPipeline,
    JsonPipelineDataFormat,
    NerPipeline,
    PipedPipelineDataFormat,
@@ -108,6 +110,7 @@ from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenize
 from .tokenization_camembert import CamembertTokenizer
 from .tokenization_ctrl import CTRLTokenizer
 from .tokenization_distilbert import DistilBertTokenizer
+from .tokenization_flaubert import FlaubertTokenizer
 from .tokenization_gpt2 import GPT2Tokenizer, GPT2TokenizerFast
 from .tokenization_openai import OpenAIGPTTokenizer
 from .tokenization_roberta import RobertaTokenizer
@@ -133,6 +136,7 @@ if is_torch_available():
    from .modeling_utils import PreTrainedModel, prune_layer, Conv1D
    from .modeling_auto import (
        AutoModel,
+        AutoModelForPreTraining,
        AutoModelForSequenceClassification,
        AutoModelForQuestionAnswering,
        AutoModelWithLMHead,
@@ -208,6 +212,13 @@ if is_torch_available():
        RobertaForQuestionAnswering,
        ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
    )
+    from .modeling_camembert import (
+        CamembertForMaskedLM,
+        CamembertModel,
+        CamembertForSequenceClassification,
+        CamembertForTokenClassification,
+        CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+    )
    from .modeling_distilbert import (
        DistilBertPreTrainedModel,
        DistilBertForMaskedLM,
@@ -248,9 +259,19 @@ if is_torch_available():
        XLMRobertaForMultipleChoice,
        XLMRobertaForSequenceClassification,
        XLMRobertaForTokenClassification,
+        XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
    )
    from .modeling_mmbt import ModalEmbeddings, MMBTModel, MMBTForClassification

+    from .modeling_flaubert import (
+        FlaubertModel,
+        FlaubertWithLMHeadModel,
+        FlaubertForSequenceClassification,
+        FlaubertForQuestionAnswering,
+        FlaubertForQuestionAnsweringSimple,
+        FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+    )
+
    # Optimization
    from .optimization import (
        AdamW,
@@ -267,6 +288,7 @@ if is_tf_available():
    from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, TFSequenceSummary, shape_list
    from .modeling_tf_auto import (
        TFAutoModel,
+        TFAutoModelForPreTraining,
        TFAutoModelForSequenceClassification,
        TFAutoModelForQuestionAnswering,
        TFAutoModelWithLMHead,
@@ -336,6 +358,14 @@ if is_tf_available():
        TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
    )

+    from .modeling_tf_xlm_roberta import (
+        TFXLMRobertaForMaskedLM,
+        TFXLMRobertaModel,
+        TFXLMRobertaForSequenceClassification,
+        TFXLMRobertaForTokenClassification,
+        TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
+    )
+
    from .modeling_tf_roberta import (
        TFRobertaPreTrainedModel,
        TFRobertaMainLayer,
@@ -346,6 +376,14 @@ if is_tf_available():
        TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
    )

+    from .modeling_tf_camembert import (
+        TFCamembertModel,
+        TFCamembertForMaskedLM,
+        TFCamembertForSequenceClassification,
+        TFCamembertForTokenClassification,
+        TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+    )
+
    from .modeling_tf_distilbert import (
        TFDistilBertPreTrainedModel,
        TFDistilBertMainLayer,
@@ -372,7 +410,12 @@ if is_tf_available():
        TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
    )

-    from .modeling_tf_t5 import TFT5PreTrainedModel, TFT5Model, TFT5WithLMHeadModel, TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP
+    from .modeling_tf_t5 import (
+        TFT5PreTrainedModel,
+        TFT5Model,
+        TFT5WithLMHeadModel,
+        TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP,
+    )

    # Optimization
    from .optimization_tf import WarmUp, create_optimizer, AdamWeightDecay, GradientAccumulator
@@ -2,7 +2,6 @@ import logging
 from argparse import ArgumentParser, Namespace
 from typing import Any, List, Optional

-from starlette.responses import JSONResponse
 from transformers import Pipeline
 from transformers.commands import BaseTransformersCLICommand
 from transformers.pipelines import SUPPORTED_TASKS, pipeline
@@ -13,15 +12,16 @@ try:
    from fastapi import FastAPI, HTTPException, Body
    from fastapi.routing import APIRoute
    from pydantic import BaseModel
+    from starlette.responses import JSONResponse

-    _serve_dependancies_installed = True
+    _serve_dependencies_installed = True
 except (ImportError, AttributeError):
    BaseModel = object

    def Body(*x, **y):
        pass

-    _serve_dependancies_installed = False
+    _serve_dependencies_installed = False


 logger = logging.getLogger("transformers-cli/serving")
@@ -111,7 +111,7 @@ class ServeCommand(BaseTransformersCLICommand):
        self.port = port
        self.workers = workers

-        if not _serve_dependancies_installed:
+        if not _serve_dependencies_installed:
            raise RuntimeError(
                "Using serve command requires FastAPI and unicorn. "
                'Please install transformers with [serving]: pip install "transformers[serving]".'
@@ -76,6 +76,8 @@ class AlbertConfig(PretrainedConfig):
                The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
            layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
                The epsilon used by the layer normalization layers.
+            classifier_dropout_prob (:obj:`float`, optional, defaults to 0.1):
+                The dropout ratio for attached classifiers.

        Example::

@@ -121,6 +123,7 @@ class AlbertConfig(PretrainedConfig):
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
+        classifier_dropout_prob=0.1,
        **kwargs
    ):
        super().__init__(**kwargs)
@@ -140,3 +143,4 @@ class AlbertConfig(PretrainedConfig):
        self.type_vocab_size = type_vocab_size
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
+        self.classifier_dropout_prob = classifier_dropout_prob
@@ -23,6 +23,7 @@ from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig
 from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig
 from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
 from .configuration_distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig
+from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig
 from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config
 from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
 from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig
@@ -53,6 +54,7 @@ ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = dict(
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
+        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ]
    for key, value, in pretrained_map.items()
 )
@@ -66,6 +68,7 @@ CONFIG_MAPPING = OrderedDict(
        ("camembert", CamembertConfig,),
        ("xlm-roberta", XLMRobertaConfig,),
        ("roberta", RobertaConfig,),
+        ("flaubert", FlaubertConfig,),
        ("bert", BertConfig,),
        ("openai-gpt", OpenAIGPTConfig,),
        ("gpt2", GPT2Config,),
@@ -77,7 +80,7 @@ CONFIG_MAPPING = OrderedDict(
 )


-class AutoConfig(object):
+class AutoConfig:
    r"""
        :class:`~transformers.AutoConfig` is a generic configuration class
        that will be instantiated as one of the configuration classes of the library
@@ -126,6 +129,7 @@ class AutoConfig(object):
            - contains `xlnet`: :class:`~transformers.XLNetConfig` (XLNet model)
            - contains `xlm`: :class:`~transformers.XLMConfig` (XLM model)
            - contains `ctrl` : :class:`~transformers.CTRLConfig` (CTRL model)
+            - contains `flaubert` : :class:`~transformers.FlaubertConfig` (Flaubert model)


        Args:
@@ -45,6 +45,7 @@ BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "bert-base-japanese-char-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-whole-word-masking-config.json",
    "bert-base-finnish-cased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-cased-v1/config.json",
    "bert-base-finnish-uncased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-uncased-v1/config.json",
+    "bert-base-dutch-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/wietsedv/bert-base-dutch-cased/config.json",
 }


@@ -25,6 +25,8 @@ logger = logging.getLogger(__name__)

 CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "camembert-base": "https://s3.amazonaws.com/models.huggingface.co/bert/camembert-base-config.json",
+    "umberto-commoncrawl-cased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/Musixmatch/umberto-commoncrawl-cased-v1/config.json",
+    "umberto-wikipedia-uncased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/Musixmatch/umberto-wikipedia-uncased-v1/config.json",
 }


@@ -0,0 +1,152 @@
+# coding=utf-8
+# Copyright 2019-present CNRS, Facebook Inc. and the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Flaubert configuration, based on XLM. """
+
+
+import logging
+
+from .configuration_xlm import XLMConfig
+
+
+logger = logging.getLogger(__name__)
+
+FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "flaubert-small-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_small_cased/config.json",
+    "flaubert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_base_uncased/config.json",
+    "flaubert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_base_cased/config.json",
+    "flaubert-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_large_cased/config.json",
+}
+
+
+class FlaubertConfig(XLMConfig):
+    """
+        Configuration class to store the configuration of a `FlaubertModel`.
+        This is the configuration class to store the configuration of a :class:`~transformers.XLMModel`.
+        It is used to instantiate an XLM model according to the specified arguments, defining the model
+        architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
+        the `xlm-mlm-en-2048 <https://huggingface.co/xlm-mlm-en-2048>`__ architecture.
+
+        Configuration objects inherit from  :class:`~transformers.PretrainedConfig` and can be used
+        to control the model outputs. Read the documentation from  :class:`~transformers.PretrainedConfig`
+        for more information.
+
+        Args:
+            pre_norm (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Whether to apply the layer normalization before or after the feed forward layer following the
+                attention in each layer (Vaswani et al., Tensor2Tensor for Neural Machine Translation. 2018)
+            layerdrop (:obj:`float`, `optional`, defaults to 0.0):
+                Probability to drop layers during training (Fan et al., Reducing Transformer Depth on Demand
+                with Structured Dropout. ICLR 2020)
+            vocab_size (:obj:`int`, optional, defaults to 30145):
+                Vocabulary size of the XLM model. Defines the different tokens that
+                can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.XLMModel`.
+            emb_dim (:obj:`int`, optional, defaults to 2048):
+                Dimensionality of the encoder layers and the pooler layer.
+            n_layer (:obj:`int`, optional, defaults to 12):
+                Number of hidden layers in the Transformer encoder.
+            n_head (:obj:`int`, optional, defaults to 16):
+                Number of attention heads for each attention layer in the Transformer encoder.
+            dropout (:obj:`float`, optional, defaults to 0.1):
+                The dropout probability for all fully connected
+                layers in the embeddings, encoder, and pooler.
+            attention_dropout (:obj:`float`, optional, defaults to 0.1):
+                The dropout probability for the attention mechanism
+            gelu_activation (:obj:`boolean`, optional, defaults to :obj:`True`):
+                The non-linear activation function (function or string) in the
+                encoder and pooler. If set to `True`, "gelu" will be used instead of "relu".
+            sinusoidal_embeddings (:obj:`boolean`, optional, defaults to :obj:`False`):
+                Whether to use sinusoidal positional embeddings instead of absolute positional embeddings.
+            causal (:obj:`boolean`, optional, defaults to :obj:`False`):
+                Set this to `True` for the model to behave in a causal manner.
+                Causal models use a triangular attention mask in order to only attend to the left-side context instead
+                if a bidirectional context.
+            asm (:obj:`boolean`, optional, defaults to :obj:`False`):
+                Whether to use an adaptive log softmax projection layer instead of a linear layer for the prediction
+                layer.
+            n_langs (:obj:`int`, optional, defaults to 1):
+                The number of languages the model handles. Set to 1 for monolingual models.
+            use_lang_emb (:obj:`boolean`, optional, defaults to :obj:`True`)
+                Whether to use language embeddings. Some models use additional language embeddings, see
+                `the multilingual models page <http://huggingface.co/transformers/multilingual.html#xlm-language-embeddings>`__
+                for information on how to use them.
+            max_position_embeddings (:obj:`int`, optional, defaults to 512):
+                The maximum sequence length that this model might
+                ever be used with. Typically set this to something large just in case
+                (e.g., 512 or 1024 or 2048).
+            embed_init_std (:obj:`float`, optional, defaults to 2048^-0.5):
+                The standard deviation of the truncated_normal_initializer for
+                initializing the embedding matrices.
+            init_std (:obj:`int`, optional, defaults to 50257):
+                The standard deviation of the truncated_normal_initializer for
+                initializing all weight matrices except the embedding matrices.
+            layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
+                The epsilon used by the layer normalization layers.
+            bos_index (:obj:`int`, optional, defaults to 0):
+                The index of the beginning of sentence token in the vocabulary.
+            eos_index (:obj:`int`, optional, defaults to 1):
+                The index of the end of sentence token in the vocabulary.
+            pad_index (:obj:`int`, optional, defaults to 2):
+                The index of the padding token in the vocabulary.
+            unk_index (:obj:`int`, optional, defaults to 3):
+                The index of the unknown token in the vocabulary.
+            mask_index (:obj:`int`, optional, defaults to 5):
+                The index of the masking token in the vocabulary.
+            is_encoder(:obj:`boolean`, optional, defaults to :obj:`True`):
+                Whether the initialized model should be a transformer encoder or decoder as seen in Vaswani et al.
+            summary_type (:obj:`string`, optional, defaults to "first"):
+                Argument used when doing sequence summary. Used in for the multiple choice head in
+                :class:`~transformers.XLMForSequenceClassification`.
+                Is one of the following options:
+                    - 'last' => take the last token hidden state (like XLNet)
+                    - 'first' => take the first token hidden state (like Bert)
+                    - 'mean' => take the mean of all tokens hidden states
+                    - 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2)
+                    - 'attn' => Not implemented now, use multi-head attention
+            summary_use_proj (:obj:`boolean`, optional, defaults to :obj:`True`):
+                Argument used when doing sequence summary. Used in for the multiple choice head in
+                :class:`~transformers.XLMForSequenceClassification`.
+                Add a projection after the vector extraction
+            summary_activation (:obj:`string` or :obj:`None`, optional, defaults to :obj:`None`):
+                Argument used when doing sequence summary. Used in for the multiple choice head in
+                :class:`~transformers.XLMForSequenceClassification`.
+                'tanh' => add a tanh activation to the output, Other => no activation.
+            summary_proj_to_labels (:obj:`boolean`, optional, defaults to :obj:`True`):
+                Argument used when doing sequence summary. Used in for the multiple choice head in
+                :class:`~transformers.XLMForSequenceClassification`.
+                If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
+            summary_first_dropout (:obj:`float`, optional, defaults to 0.1):
+                Argument used when doing sequence summary. Used in for the multiple choice head in
+                :class:`~transformers.XLMForSequenceClassification`.
+                Add a dropout before the projection and activation
+            start_n_top (:obj:`int`, optional, defaults to 5):
+                Used in the SQuAD evaluation script for XLM and XLNet.
+            end_n_top (:obj:`int`, optional, defaults to 5):
+                Used in the SQuAD evaluation script for XLM and XLNet.
+            mask_token_id (:obj:`int`, optional, defaults to 0):
+                Model agnostic parameter to identify masked tokens when generating text in an MLM context.
+            lang_id (:obj:`int`, optional, defaults to 1):
+                The ID of the language used by the model. This parameter is used when generating
+                text in a given language.
+    """
+
+    pretrained_config_archive_map = FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
+    model_type = "flaubert"
+
+    def __init__(self, layerdrop=0.0, pre_norm=False, **kwargs):
+        """Constructs FlaubertConfig.
+        """
+        super().__init__(**kwargs)
+        self.layerdrop = layerdrop
+        self.pre_norm = pre_norm
@@ -40,12 +40,17 @@ class PretrainedConfig(object):
            - ``pretrained_config_archive_map``: a python ``dict`` with `shortcut names` (string) as keys and `url` (string) of associated pretrained model configurations as values.
            - ``model_type``: a string that identifies the model type, that we serialize into the JSON file, and that we use to recreate the correct object in :class:`~transformers.AutoConfig`.

-        Parameters:
-            ``finetuning_task``: string, default `None`. Name of the task used to fine-tune the model. This can be used when converting from an original (TensorFlow or PyTorch) checkpoint.
-            ``num_labels``: integer, default `2`. Number of classes to use when the model is a classification model (sequences/tokens)
-            ``output_attentions``: boolean, default `False`. Should the model returns attentions weights.
-            ``output_hidden_states``: string, default `False`. Should the model returns all hidden-states.
-            ``torchscript``: string, default `False`. Is the model used with Torchscript.
+        Args:
+            finetuning_task (:obj:`string` or :obj:`None`, `optional`, defaults to :obj:`None`):
+                Name of the task used to fine-tune the model. This can be used when converting from an original (TensorFlow or PyTorch) checkpoint.
+            num_labels (:obj:`int`, `optional`, defaults to `2`):
+                Number of classes to use when the model is a classification model (sequences/tokens)
+            output_attentions (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Should the model returns attentions weights.
+            output_hidden_states (:obj:`string`, `optional`, defaults to :obj:`False`):
+                Should the model returns all hidden-states.
+            torchscript (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Is the model used with Torchscript (for PyTorch models).
    """
    pretrained_config_archive_map = {}  # type: Dict[str, str]
    model_type = ""  # type: str
@@ -77,6 +82,7 @@ class PretrainedConfig(object):
        self.num_return_sequences = kwargs.pop("num_return_sequences", 1)

        # Fine-tuning task arguments
+        self.architectures = kwargs.pop("architectures", None)
        self.finetuning_task = kwargs.pop("finetuning_task", None)
        self.num_labels = kwargs.pop("num_labels", 2)
        self.id2label = kwargs.pop("id2label", {i: "LABEL_{}".format(i) for i in range(self.num_labels)})
@@ -93,8 +99,13 @@ class PretrainedConfig(object):
                raise err

    def save_pretrained(self, save_directory):
-        """ Save a configuration object to the directory `save_directory`, so that it
-            can be re-loaded using the :func:`~transformers.PretrainedConfig.from_pretrained` class method.
+        """
+        Save a configuration object to the directory `save_directory`, so that it
+        can be re-loaded using the :func:`~transformers.PretrainedConfig.from_pretrained` class method.
+
+        Args:
+            save_directory (:obj:`string`):
+                Directory where the configuration JSON file will be saved.
        """
        assert os.path.isdir(
            save_directory
@@ -107,40 +118,45 @@ class PretrainedConfig(object):
        logger.info("Configuration saved in {}".format(output_config_file))

    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
-        r""" Instantiate a :class:`~transformers.PretrainedConfig` (or a derived class) from a pre-trained model configuration.
+    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs) -> "PretrainedConfig":
+        r"""

-        Parameters:
-            pretrained_model_name_or_path: either:
+        Instantiate a :class:`~transformers.PretrainedConfig` (or a derived class) from a pre-trained model configuration.

-                - a string with the `shortcut name` of a pre-trained model configuration to load from cache or download, e.g.: ``bert-base-uncased``.
-                - a string with the `identifier name` of a pre-trained model configuration that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
-                - a path to a `directory` containing a configuration file saved using the :func:`~transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.
-                - a path or url to a saved configuration JSON `file`, e.g.: ``./my_model_directory/configuration.json``.
-
-            cache_dir: (`optional`) string:
+        Args:
+            pretrained_model_name_or_path (:obj:`string`):
+                either:
+                  - a string with the `shortcut name` of a pre-trained model configuration to load from cache or
+                    download, e.g.: ``bert-base-uncased``.
+                  - a string with the `identifier name` of a pre-trained model configuration that was user-uploaded to
+                    our S3, e.g.: ``dbmdz/bert-base-german-cased``.
+                  - a path to a `directory` containing a configuration file saved using the
+                    :func:`~transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.
+                  - a path or url to a saved configuration JSON `file`, e.g.:
+                    ``./my_model_directory/configuration.json``.
+            cache_dir (:obj:`string`, `optional`):
                Path to a directory in which a downloaded pre-trained model
                configuration should be cached if the standard cache should not be used.
-
-            kwargs: (`optional`) dict: key/value pairs with which to update the configuration object after loading.
-
-                - The values in kwargs of any keys which are configuration attributes will be used to override the loaded values.
-                - Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled by the `return_unused_kwargs` keyword parameter.
-
-            force_download: (`optional`) boolean, default False:
-                Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
-
-            resume_download: (`optional`) boolean, default False:
+            kwargs (:obj:`Dict[str, any]`, `optional`):
+                The values in kwargs of any keys which are configuration attributes will be used to override the loaded
+                values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is
+                controlled by the `return_unused_kwargs` keyword parameter.
+            force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Force to (re-)download the model weights and configuration files and override the cached versions if they exist.
+            resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
-
-            proxies: (`optional`) dict, default None:
-                A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
+            proxies (:obj:`Dict`, `optional`):
+                A dictionary of proxy servers to use by protocol or endpoint, e.g.:
+                :obj:`{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.`
                The proxies are used on each request.
-
            return_unused_kwargs: (`optional`) bool:
+                If False, then this function returns just the final configuration object.
+                If True, then this functions returns a :obj:`Tuple(config, unused_kwargs)` where `unused_kwargs` is a
+                dictionary consisting of the key/value pairs whose keys are not configuration attributes: ie the part
+                of kwargs which has not been used to update `config` and is otherwise ignored.

-                - If False, then this function returns just the final configuration object.
-                - If True, then this functions returns a tuple `(config, unused_kwargs)` where `unused_kwargs` is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: ie the part of kwargs which has not been used to update `config` and is otherwise ignored.
+        Returns:
+            :class:`PretrainedConfig`: An instance of a configuration object

        Examples::

@@ -169,9 +185,14 @@ class PretrainedConfig(object):
        for instantiating a Config using `from_dict`.

        Parameters:
-            pretrained_config_archive_map: (`optional`) Dict:
-                A map of `shortcut names` to `url`.
-                By default, will use the current class attribute.
+            pretrained_model_name_or_path (:obj:`string`):
+                The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
+            pretrained_config_archive_map: (:obj:`Dict[str, str]`, `optional`) Dict:
+                A map of `shortcut names` to `url`. By default, will use the current class attribute.
+
+        Returns:
+            :obj:`Tuple[Dict, Dict]`: The dictionary that will be used to instantiate the configuration object.
+
        """
        cache_dir = kwargs.pop("cache_dir", None)
        force_download = kwargs.pop("force_download", False)
@@ -235,8 +256,21 @@ class PretrainedConfig(object):
        return config_dict, kwargs

    @classmethod
-    def from_dict(cls, config_dict: Dict, **kwargs):
-        """Constructs a `Config` from a Python dictionary of parameters."""
+    def from_dict(cls, config_dict: Dict, **kwargs) -> "PretrainedConfig":
+        """
+        Constructs a `Config` from a Python dictionary of parameters.
+
+        Args:
+            config_dict (:obj:`Dict[str, any]`):
+                Dictionary that will be used to instantiate the configuration object. Such a dictionary can be retrieved
+                from a pre-trained checkpoint by leveraging the :func:`~transformers.PretrainedConfig.get_config_dict`
+                method.
+            kwargs (:obj:`Dict[str, any]`):
+                Additional parameters from which to initialize the configuration object.
+
+        Returns:
+            :class:`PretrainedConfig`: An instance of a configuration object
+        """
        return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)

        config = cls(**config_dict)
@@ -260,8 +294,18 @@ class PretrainedConfig(object):
            return config

    @classmethod
-    def from_json_file(cls, json_file: str):
-        """Constructs a `Config` from the path to a json file of parameters."""
+    def from_json_file(cls, json_file: str) -> "PretrainedConfig":
+        """
+        Constructs a `Config` from the path to a json file of parameters.
+
+        Args:
+            json_file (:obj:`string`):
+                Path to the JSON file containing the parameters.
+
+        Returns:
+            :class:`PretrainedConfig`: An instance of a configuration object
+
+        """
        config_dict = cls._dict_from_json_file(json_file)
        return cls(**config_dict)

@@ -278,17 +322,33 @@ class PretrainedConfig(object):
        return "{} {}".format(self.__class__.__name__, self.to_json_string())

    def to_dict(self):
-        """Serializes this instance to a Python dictionary."""
+        """
+        Serializes this instance to a Python dictionary.
+
+        Returns:
+            :obj:`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
+        """
        output = copy.deepcopy(self.__dict__)
        if hasattr(self.__class__, "model_type"):
            output["model_type"] = self.__class__.model_type
        return output

    def to_json_string(self):
-        """Serializes this instance to a JSON string."""
+        """
+        Serializes this instance to a JSON string.
+
+        Returns:
+            :obj:`string`: String containing all the attributes that make up this configuration instance in JSON format.
+        """
        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"

    def to_json_file(self, json_file_path):
-        """ Save this instance to a json file."""
+        """
+        Save this instance to a json file.
+
+        Args:
+            json_file_path (:obj:`string`):
+                Path to the JSON file in which this configuration instance's parameters will be saved.
+        """
        with open(json_file_path, "w", encoding="utf-8") as writer:
            writer.write(self.to_json_string())
@@ -22,6 +22,7 @@ import os
 from transformers import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
+    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
@@ -30,9 +31,11 @@ from transformers import (
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
+    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BertConfig,
+    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    GPT2Config,
@@ -43,6 +46,7 @@ from transformers import (
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
+    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
@@ -52,10 +56,12 @@ from transformers import (
    TFRobertaForSequenceClassification,
    TFT5WithLMHeadModel,
    TFTransfoXLLMHeadModel,
+    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    XLMConfig,
+    XLMRobertaConfig,
    XLNetConfig,
    cached_path,
    is_torch_available,
@@ -77,6 +83,8 @@ if is_torch_available():
        XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
+        XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
+        XLMRobertaForMaskedLM,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
        OpenAIGPTLMHeadModel,
@@ -84,6 +92,9 @@ if is_torch_available():
        RobertaForMaskedLM,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
+        CamembertForMaskedLM,
+        CamembertForSequenceClassification,
+        CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        DistilBertForMaskedLM,
        DistilBertForQuestionAnswering,
        DistilBertForSequenceClassification,
@@ -107,6 +118,8 @@ else:
        XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
+        XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
+        XLMRobertaForMaskedLM,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
        OpenAIGPTLMHeadModel,
@@ -114,6 +127,9 @@ else:
        RobertaForMaskedLM,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
+        CamembertForMaskedLM,
+        CamembertForSequenceClassification,
+        CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        DistilBertForMaskedLM,
        DistilBertForSequenceClassification,
        DistilBertForQuestionAnswering,
@@ -152,6 +168,11 @@ else:
        None,
        None,
        None,
+        None,
+        None,
+        None,
+        None,
+        None,
    )


@@ -207,6 +228,13 @@ MODEL_CLASSES = {
        XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
+    "xlm-roberta": (
+        XLMRobertaConfig,
+        TFXLMRobertaForMaskedLM,
+        XLMRobertaForMaskedLM,
+        XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
+        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
+    ),
    "transfo-xl": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
@@ -235,6 +263,13 @@ MODEL_CLASSES = {
        ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
+    "camembert": (
+        CamembertConfig,
+        TFCamembertForMaskedLM,
+        CamembertForMaskedLM,
+        CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
+    ),
    "distilbert": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
@@ -249,13 +284,6 @@ MODEL_CLASSES = {
        DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
-    "distilbert-base-uncased-distilled-squad": (
-        DistilBertConfig,
-        TFDistilBertForQuestionAnswering,
-        DistilBertForQuestionAnswering,
-        DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
-        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    ),
    "ctrl": (
        CTRLConfig,
        TFCTRLLMHeadModel,
@@ -152,6 +152,8 @@ class ModelCard(object):
            resolved_model_card_file = cached_path(
                model_card_file, cache_dir=cache_dir, force_download=True, proxies=proxies, resume_download=False
            )
+            if resolved_model_card_file is None:
+                raise EnvironmentError
            if resolved_model_card_file == model_card_file:
                logger.info("loading model card file {}".format(model_card_file))
            else:
@@ -698,7 +698,7 @@ class AlbertForSequenceClassification(AlbertPreTrainedModel):
        self.num_labels = config.num_labels

        self.albert = AlbertModel(config)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.dropout = nn.Dropout(config.classifier_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)

        self.init_weights()
@@ -25,6 +25,7 @@ from .configuration_auto import (
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
+    FlaubertConfig,
    GPT2Config,
    OpenAIGPTConfig,
    RobertaConfig,
@@ -45,6 +46,7 @@ from .modeling_albert import (
 from .modeling_bert import (
    BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
    BertForMaskedLM,
+    BertForPreTraining,
    BertForQuestionAnswering,
    BertForSequenceClassification,
    BertForTokenClassification,
@@ -66,11 +68,19 @@ from .modeling_distilbert import (
    DistilBertForTokenClassification,
    DistilBertModel,
 )
+from .modeling_flaubert import (
+    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+    FlaubertForQuestionAnswering,
+    FlaubertForSequenceClassification,
+    FlaubertModel,
+    FlaubertWithLMHeadModel,
+)
 from .modeling_gpt2 import GPT2_PRETRAINED_MODEL_ARCHIVE_MAP, GPT2LMHeadModel, GPT2Model
 from .modeling_openai import OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP, OpenAIGPTLMHeadModel, OpenAIGPTModel
 from .modeling_roberta import (
    ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
    RobertaForMaskedLM,
+    RobertaForQuestionAnswering,
    RobertaForSequenceClassification,
    RobertaForTokenClassification,
    RobertaModel,
@@ -120,6 +130,7 @@ ALL_PRETRAINED_MODEL_ARCHIVE_MAP = dict(
        CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        T5_PRETRAINED_MODEL_ARCHIVE_MAP,
        XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
+        FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
    ]
    for key, value, in pretrained_map.items()
 )
@@ -139,6 +150,26 @@ MODEL_MAPPING = OrderedDict(
        (XLNetConfig, XLNetModel),
        (XLMConfig, XLMModel),
        (CTRLConfig, CTRLModel),
+        (FlaubertConfig, FlaubertModel),
+    ]
+)
+
+MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
+    [
+        (T5Config, T5WithLMHeadModel),
+        (DistilBertConfig, DistilBertForMaskedLM),
+        (AlbertConfig, AlbertForMaskedLM),
+        (CamembertConfig, CamembertForMaskedLM),
+        (XLMRobertaConfig, XLMRobertaForMaskedLM),
+        (RobertaConfig, RobertaForMaskedLM),
+        (BertConfig, BertForPreTraining),
+        (OpenAIGPTConfig, OpenAIGPTLMHeadModel),
+        (GPT2Config, GPT2LMHeadModel),
+        (TransfoXLConfig, TransfoXLLMHeadModel),
+        (XLNetConfig, XLNetLMHeadModel),
+        (XLMConfig, XLMWithLMHeadModel),
+        (CTRLConfig, CTRLLMHeadModel),
+        (FlaubertConfig, FlaubertWithLMHeadModel),
    ]
 )

@@ -157,6 +188,7 @@ MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
        (XLNetConfig, XLNetLMHeadModel),
        (XLMConfig, XLMWithLMHeadModel),
        (CTRLConfig, CTRLLMHeadModel),
+        (FlaubertConfig, FlaubertWithLMHeadModel),
    ]
 )

@@ -170,6 +202,7 @@ MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
        (BertConfig, BertForSequenceClassification),
        (XLNetConfig, XLNetForSequenceClassification),
        (XLMConfig, XLMForSequenceClassification),
+        (FlaubertConfig, FlaubertForSequenceClassification),
    ]
 )

@@ -177,9 +210,11 @@ MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
    [
        (DistilBertConfig, DistilBertForQuestionAnswering),
        (AlbertConfig, AlbertForQuestionAnswering),
+        (RobertaConfig, RobertaForQuestionAnswering),
        (BertConfig, BertForQuestionAnswering),
        (XLNetConfig, XLNetForQuestionAnswering),
        (XLMConfig, XLMForQuestionAnswering),
+        (FlaubertConfig, FlaubertForQuestionAnswering),
    ]
 )

@@ -202,26 +237,6 @@ class AutoModel(object):
        when created with the `AutoModel.from_pretrained(pretrained_model_name_or_path)`
        or the `AutoModel.from_config(config)` class methods.

-        The `from_pretrained()` method takes care of returning the correct model class instance
-        based on the `model_type` property of the config object, or when it's missing,
-        falling back to using pattern matching on the `pretrained_model_name_or_path` string.
-
-        The base model class to instantiate is selected as the first pattern matching
-        in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `t5`: T5Model (T5 model)
-            - contains `distilbert`: DistilBertModel (DistilBERT model)
-            - contains `albert`: AlbertModel (ALBERT model)
-            - contains `camembert`: CamembertModel (CamemBERT model)
-            - contains `xlm-roberta`: XLMRobertaModel (XLM-RoBERTa model)
-            - contains `roberta`: RobertaModel (RoBERTa model)
-            - contains `bert`: BertModel (Bert model)
-            - contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
-            - contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
-            - contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
-            - contains `xlnet`: XLNetModel (XLNet model)
-            - contains `xlm`: XLMModel (XLM model)
-            - contains `ctrl`: CTRLModel (Salesforce CTRL  model)
-
        This class cannot be instantiated using `__init__()` (throws an error).
    """

@@ -237,17 +252,20 @@ class AutoModel(object):
        r""" Instantiates one of the base model classes of the library
        from a configuration.

-            config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
+        Args:
+            config (:class:`~transformers.PretrainedConfig`):
                The model class to instantiate is selected based on the configuration class:
-                    - isInstance of `distilbert` configuration class: DistilBertModel (DistilBERT model)
-                    - isInstance of `roberta` configuration class: RobertaModel (RoBERTa model)
-                    - isInstance of `bert` configuration class: BertModel (Bert model)
-                    - isInstance of `openai-gpt` configuration class: OpenAIGPTModel (OpenAI GPT model)
-                    - isInstance of `gpt2` configuration class: GPT2Model (OpenAI GPT-2 model)
-                    - isInstance of `ctrl` configuration class: CTRLModel (Salesforce CTRL  model)
-                    - isInstance of `transfo-xl` configuration class: TransfoXLModel (Transformer-XL model)
-                    - isInstance of `xlnet` configuration class: XLNetModel (XLNet model)
-                    - isInstance of `xlm` configuration class: XLMModel (XLM model)
+
+                - isInstance of `distilbert` configuration class: :class:`~transformers.DistilBertModel` (DistilBERT model)
+                - isInstance of `roberta` configuration class: :class:`~transformers.RobertaModel` (RoBERTa model)
+                - isInstance of `bert` configuration class: :class:`~transformers.BertModel` (Bert model)
+                - isInstance of `openai-gpt` configuration class: :class:`~transformers.OpenAIGPTModel` (OpenAI GPT model)
+                - isInstance of `gpt2` configuration class: :class:`~transformers.GPT2Model` (OpenAI GPT-2 model)
+                - isInstance of `ctrl` configuration class: :class:`~transformers.CTRLModel` (Salesforce CTRL  model)
+                - isInstance of `transfo-xl` configuration class: :class:`~transformers.TransfoXLModel` (Transformer-XL model)
+                - isInstance of `xlnet` configuration class: :class:`~transformers.XLNetModel` (XLNet model)
+                - isInstance of `xlm` configuration class: :class:`~transformers.XLMModel` (XLM model)
+                - isInstance of `flaubert` configuration class: :class:`~transformers.FlaubertModel` (XLM model)

        Examples::

@@ -269,26 +287,31 @@ class AutoModel(object):
        r""" Instantiates one of the base model classes of the library
        from a pre-trained model configuration.

-        The model class to instantiate is selected as the first pattern matching
+        The `from_pretrained()` method takes care of returning the correct model class instance
+        based on the `model_type` property of the config object, or when it's missing,
+        falling back to using pattern matching on the `pretrained_model_name_or_path` string.
+
+        The base model class to instantiate is selected as the first pattern matching
        in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `t5`: T5Model (T5 model)
-            - contains `distilbert`: DistilBertModel (DistilBERT model)
-            - contains `albert`: AlbertModel (ALBERT model)
-            - contains `camembert`: CamembertModel (CamemBERT model)
-            - contains `xlm-roberta`: XLMRobertaModel (XLM-RoBERTa model)
-            - contains `roberta`: RobertaModel (RoBERTa model)
-            - contains `bert`: BertModel (Bert model)
-            - contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
-            - contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
-            - contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
-            - contains `xlnet`: XLNetModel (XLNet model)
-            - contains `xlm`: XLMModel (XLM model)
-            - contains `ctrl`: CTRLModel (Salesforce CTRL model)
+            - contains `t5`: :class:`~transformers.T5Model` (T5 model)
+            - contains `distilbert`: :class:`~transformers.DistilBertModel` (DistilBERT model)
+            - contains `albert`: :class:`~transformers.AlbertModel` (ALBERT model)
+            - contains `camembert`: :class:`~transformers.CamembertModel` (CamemBERT model)
+            - contains `xlm-roberta`: :class:`~transformers.XLMRobertaModel` (XLM-RoBERTa model)
+            - contains `roberta`: :class:`~transformers.RobertaModel` (RoBERTa model)
+            - contains `bert`: :class:`~transformers.BertModel` (Bert model)
+            - contains `openai-gpt`: :class:`~transformers.OpenAIGPTModel` (OpenAI GPT model)
+            - contains `gpt2`: :class:`~transformers.GPT2Model` (OpenAI GPT-2 model)
+            - contains `transfo-xl`: :class:`~transformers.TransfoXLModel` (Transformer-XL model)
+            - contains `xlnet`: :class:`~transformers.XLNetModel` (XLNet model)
+            - contains `xlm`: :class:`~transformers.XLMModel` (XLM model)
+            - contains `ctrl`: :class:`~transformers.CTRLModel` (Salesforce CTRL  model)
+            - contains `flaubert`: :class:`~transformers.Flaubert` (Flaubert  model)

            The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
            To train the model, you should first set it back in training mode with `model.train()`

-        Params:
+        Args:
            pretrained_model_name_or_path: either:

                - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
@@ -360,32 +383,164 @@ class AutoModel(object):
        )


-class AutoModelWithLMHead(object):
+class AutoModelForPreTraining(object):
    r"""
-        :class:`~transformers.AutoModelWithLMHead` is a generic model class
-        that will be instantiated as one of the language modeling model classes of the library
-        when created with the `AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)`
+        :class:`~transformers.AutoModelForPreTraining` is a generic model class
+        that will be instantiated as one of the model classes of the library -with the architecture used for pretraining this model– when created with the `AutoModelForPreTraining.from_pretrained(pretrained_model_name_or_path)`
        class method.

+        This class cannot be instantiated using `__init__()` (throws an error).
+    """
+
+    def __init__(self):
+        raise EnvironmentError(
+            "AutoModelForPreTraining is designed to be instantiated "
+            "using the `AutoModelForPreTraining.from_pretrained(pretrained_model_name_or_path)` or "
+            "`AutoModelForPreTraining.from_config(config)` methods."
+        )
+
+    @classmethod
+    def from_config(cls, config):
+        r""" Instantiates one of the base model classes of the library
+        from a configuration.
+
+        Args:
+            config (:class:`~transformers.PretrainedConfig`):
+                The model class to instantiate is selected based on the configuration class:
+
+                - isInstance of `distilbert` configuration class: :class:`~transformers.DistilBertModelForMaskedLM` (DistilBERT model)
+                - isInstance of `roberta` configuration class: :class:`~transformers.RobertaModelForMaskedLM` (RoBERTa model)
+                - isInstance of `bert` configuration class: :class:`~transformers.BertForPreTraining` (Bert model)
+                - isInstance of `openai-gpt` configuration class: :class:`~transformers.OpenAIGPTLMHeadModel` (OpenAI GPT model)
+                - isInstance of `gpt2` configuration class: :class:`~transformers.GPT2ModelLMHeadModel` (OpenAI GPT-2 model)
+                - isInstance of `ctrl` configuration class: :class:`~transformers.CTRLModelLMHeadModel` (Salesforce CTRL  model)
+                - isInstance of `transfo-xl` configuration class: :class:`~transformers.TransfoXLLMHeadModel` (Transformer-XL model)
+                - isInstance of `xlnet` configuration class: :class:`~transformers.XLNetLMHeadModel` (XLNet model)
+                - isInstance of `xlm` configuration class: :class:`~transformers.XLMWithLMHeadModel` (XLM model)
+                - isInstance of `flaubert` configuration class: :class:`~transformers.FlaubertWithLMHeadModel` (Flaubert model)
+
+        Examples::
+
+            config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
+            model = AutoModelForPreTraining.from_config(config)  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
+        """
+        for config_class, model_class in MODEL_FOR_PRETRAINING_MAPPING.items():
+            if isinstance(config, config_class):
+                return model_class(config)
+        raise ValueError(
+            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
+            "Model type should be one of {}.".format(
+                config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_FOR_PRETRAINING_MAPPING.keys())
+            )
+        )
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+        r""" Instantiates one of the model classes of the library -with the architecture used for pretraining this model– from a pre-trained model configuration.
+
        The `from_pretrained()` method takes care of returning the correct model class instance
        based on the `model_type` property of the config object, or when it's missing,
        falling back to using pattern matching on the `pretrained_model_name_or_path` string.

        The model class to instantiate is selected as the first pattern matching
        in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `t5`: T5ModelWithLMHead (T5 model)
-            - contains `distilbert`: DistilBertForMaskedLM (DistilBERT model)
-            - contains `albert`: AlbertForMaskedLM (ALBERT model)
-            - contains `camembert`: CamembertForMaskedLM (CamemBERT model)
-            - contains `xlm-roberta`: XLMRobertaForMaskedLM (XLM-RoBERTa model)
-            - contains `roberta`: RobertaForMaskedLM (RoBERTa model)
-            - contains `bert`: BertForMaskedLM (Bert model)
-            - contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model)
-            - contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model)
-            - contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model)
-            - contains `xlnet`: XLNetLMHeadModel (XLNet model)
-            - contains `xlm`: XLMWithLMHeadModel (XLM model)
-            - contains `ctrl`: CTRLLMHeadModel (Salesforce CTRL model)
+            - contains `t5`: :class:`~transformers.T5ModelWithLMHead` (T5 model)
+            - contains `distilbert`: :class:`~transformers.DistilBertForMaskedLM` (DistilBERT model)
+            - contains `albert`: :class:`~transformers.AlbertForMaskedLM` (ALBERT model)
+            - contains `camembert`: :class:`~transformers.CamembertForMaskedLM` (CamemBERT model)
+            - contains `xlm-roberta`: :class:`~transformers.XLMRobertaForMaskedLM` (XLM-RoBERTa model)
+            - contains `roberta`: :class:`~transformers.RobertaForMaskedLM` (RoBERTa model)
+            - contains `bert`: :class:`~transformers.BertForPreTraining` (Bert model)
+            - contains `openai-gpt`: :class:`~transformers.OpenAIGPTLMHeadModel` (OpenAI GPT model)
+            - contains `gpt2`: :class:`~transformers.GPT2LMHeadModel` (OpenAI GPT-2 model)
+            - contains `transfo-xl`: :class:`~transformers.TransfoXLLMHeadModel` (Transformer-XL model)
+            - contains `xlnet`: :class:`~transformers.XLNetLMHeadModel` (XLNet model)
+            - contains `xlm`: :class:`~transformers.XLMWithLMHeadModel` (XLM model)
+            - contains `ctrl`: :class:`~transformers.CTRLLMHeadModel` (Salesforce CTRL model)
+            - contains `flaubert`: :class:`~transformers.FlaubertWithLMHeadModel` (Flaubert model)
+
+        The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
+        To train the model, you should first set it back in training mode with `model.train()`
+
+        Args:
+            pretrained_model_name_or_path:
+                Either:
+
+                - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
+                - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
+                - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
+                - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
+            model_args: (`optional`) Sequence of positional arguments:
+                All remaning positional arguments will be passed to the underlying model's ``__init__`` method
+            config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
+                Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
+
+                - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
+                - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
+                - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
+
+            state_dict: (`optional`) dict:
+                an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
+                This option can be used if you want to create a model from a pretrained configuration but load your own weights.
+                In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
+            cache_dir: (`optional`) string:
+                Path to a directory in which a downloaded pre-trained model
+                configuration should be cached if the standard cache should not be used.
+            force_download: (`optional`) boolean, default False:
+                Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
+            resume_download: (`optional`) boolean, default False:
+                Do not delete incompletely received file. Attempt to resume the download if such a file exists.
+            proxies: (`optional`) dict, default None:
+                A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
+                The proxies are used on each request.
+            output_loading_info: (`optional`) boolean:
+                Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
+            kwargs: (`optional`) Remaining dictionary of keyword arguments:
+                Can be used to update the configuration object (after it being loaded) and initiate the model.
+                (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or
+                automatically loaded:
+
+                - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the
+                  underlying model's ``__init__`` method (we assume all relevant updates to the configuration have
+                  already been done)
+                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class
+                  initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of
+                  ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute
+                  with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration
+                  attribute will be passed to the underlying model's ``__init__`` function.
+
+        Examples::
+
+            model = AutoModelForPreTraining.from_pretrained('bert-base-uncased')    # Download model and configuration from S3 and cache.
+            model = AutoModelForPreTraining.from_pretrained('./test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
+            model = AutoModelForPreTraining.from_pretrained('bert-base-uncased', output_attention=True)  # Update configuration during loading
+            assert model.config.output_attention == True
+            # Loading from a TF checkpoint file instead of a PyTorch model (slower)
+            config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
+            model = AutoModelForPreTraining.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
+
+        """
+        config = kwargs.pop("config", None)
+        if not isinstance(config, PretrainedConfig):
+            config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
+
+        for config_class, model_class in MODEL_FOR_PRETRAINING_MAPPING.items():
+            if isinstance(config, config_class):
+                return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
+        raise ValueError(
+            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
+            "Model type should be one of {}.".format(
+                config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_FOR_PRETRAINING_MAPPING.keys())
+            )
+        )
+
+
+class AutoModelWithLMHead(object):
+    r"""
+        :class:`~transformers.AutoModelWithLMHead` is a generic model class
+        that will be instantiated as one of the language modeling model classes of the library
+        when created with the `AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)`
+        class method.

        This class cannot be instantiated using `__init__()` (throws an error).
    """
@@ -402,17 +557,20 @@ class AutoModelWithLMHead(object):
        r""" Instantiates one of the base model classes of the library
        from a configuration.

-            config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
+        Args:
+            config (:class:`~transformers.PretrainedConfig`):
                The model class to instantiate is selected based on the configuration class:
-                    - isInstance of `distilbert` configuration class: DistilBertModel (DistilBERT model)
-                    - isInstance of `roberta` configuration class: RobertaModel (RoBERTa model)
-                    - isInstance of `bert` configuration class: BertModel (Bert model)
-                    - isInstance of `openai-gpt` configuration class: OpenAIGPTModel (OpenAI GPT model)
-                    - isInstance of `gpt2` configuration class: GPT2Model (OpenAI GPT-2 model)
-                    - isInstance of `ctrl` configuration class: CTRLModel (Salesforce CTRL  model)
-                    - isInstance of `transfo-xl` configuration class: TransfoXLModel (Transformer-XL model)
-                    - isInstance of `xlnet` configuration class: XLNetModel (XLNet model)
-                    - isInstance of `xlm` configuration class: XLMModel (XLM model)
+
+                - isInstance of `distilbert` configuration class: :class:`~transformers.DistilBertModelForMaskedLM` (DistilBERT model)
+                - isInstance of `roberta` configuration class: :class:`~transformers.RobertaModelForMaskedLM` (RoBERTa model)
+                - isInstance of `bert` configuration class: :class:`~transformers.BertModelForMaskedLM` (Bert model)
+                - isInstance of `openai-gpt` configuration class: :class:`~transformers.OpenAIGPTLMHeadModel` (OpenAI GPT model)
+                - isInstance of `gpt2` configuration class: :class:`~transformers.GPT2ModelLMHeadModel` (OpenAI GPT-2 model)
+                - isInstance of `ctrl` configuration class: :class:`~transformers.CTRLModelLMHeadModel` (Salesforce CTRL  model)
+                - isInstance of `transfo-xl` configuration class: :class:`~transformers.TransfoXLLMHeadModel` (Transformer-XL model)
+                - isInstance of `xlnet` configuration class: :class:`~transformers.XLNetLMHeadModel` (XLNet model)
+                - isInstance of `xlm` configuration class: :class:`~transformers.XLMWithLMHeadModel` (XLM model)
+                - isInstance of `flaubert` configuration class: :class:`~transformers.FlaubertWithLMHeadModel` (Flaubert model)

        Examples::

@@ -440,34 +598,34 @@ class AutoModelWithLMHead(object):

        The model class to instantiate is selected as the first pattern matching
        in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `t5`: T5ModelWithLMHead (T5 model)
-            - contains `distilbert`: DistilBertForMaskedLM (DistilBERT model)
-            - contains `albert`: AlbertForMaskedLM (ALBERT model)
-            - contains `camembert`: CamembertForMaskedLM (CamemBERT model)
-            - contains `xlm-roberta`: XLMRobertaForMaskedLM (XLM-RoBERTa model)
-            - contains `roberta`: RobertaForMaskedLM (RoBERTa model)
-            - contains `bert`: BertForMaskedLM (Bert model)
-            - contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model)
-            - contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model)
-            - contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model)
-            - contains `xlnet`: XLNetLMHeadModel (XLNet model)
-            - contains `xlm`: XLMWithLMHeadModel (XLM model)
-            - contains `ctrl`: CTRLLMHeadModel (Salesforce CTRL model)
+            - contains `t5`: :class:`~transformers.T5ModelWithLMHead` (T5 model)
+            - contains `distilbert`: :class:`~transformers.DistilBertForMaskedLM` (DistilBERT model)
+            - contains `albert`: :class:`~transformers.AlbertForMaskedLM` (ALBERT model)
+            - contains `camembert`: :class:`~transformers.CamembertForMaskedLM` (CamemBERT model)
+            - contains `xlm-roberta`: :class:`~transformers.XLMRobertaForMaskedLM` (XLM-RoBERTa model)
+            - contains `roberta`: :class:`~transformers.RobertaForMaskedLM` (RoBERTa model)
+            - contains `bert`: :class:`~transformers.BertForMaskedLM` (Bert model)
+            - contains `openai-gpt`: :class:`~transformers.OpenAIGPTLMHeadModel` (OpenAI GPT model)
+            - contains `gpt2`: :class:`~transformers.GPT2LMHeadModel` (OpenAI GPT-2 model)
+            - contains `transfo-xl`: :class:`~transformers.TransfoXLLMHeadModel` (Transformer-XL model)
+            - contains `xlnet`: :class:`~transformers.XLNetLMHeadModel` (XLNet model)
+            - contains `xlm`: :class:`~transformers.XLMWithLMHeadModel` (XLM model)
+            - contains `ctrl`: :class:`~transformers.CTRLLMHeadModel` (Salesforce CTRL model)
+            - contains `flaubert`: :class:`~transformers.FlaubertWithLMHeadModel` (Flaubert model)

        The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
        To train the model, you should first set it back in training mode with `model.train()`

-        Params:
-            pretrained_model_name_or_path: either:
+        Args:
+            pretrained_model_name_or_path:
+                Either:

                - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
                - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
                - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
-
            model_args: (`optional`) Sequence of positional arguments:
                All remaning positional arguments will be passed to the underlying model's ``__init__`` method
-
            config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
                Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:

@@ -479,28 +637,31 @@ class AutoModelWithLMHead(object):
                an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
                This option can be used if you want to create a model from a pretrained configuration but load your own weights.
                In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
-
            cache_dir: (`optional`) string:
                Path to a directory in which a downloaded pre-trained model
                configuration should be cached if the standard cache should not be used.
-
            force_download: (`optional`) boolean, default False:
                Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
            resume_download: (`optional`) boolean, default False:
-                Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
-
+                Do not delete incompletely received file. Attempt to resume the download if such a file exists.
            proxies: (`optional`) dict, default None:
                A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
                The proxies are used on each request.
-
            output_loading_info: (`optional`) boolean:
                Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
-
            kwargs: (`optional`) Remaining dictionary of keyword arguments:
-                Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
+                Can be used to update the configuration object (after it being loaded) and initiate the model.
+                (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or
+                automatically loaded:

-                - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
-                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
+                - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the
+                  underlying model's ``__init__`` method (we assume all relevant updates to the configuration have
+                  already been done)
+                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class
+                  initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of
+                  ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute
+                  with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration
+                  attribute will be passed to the underlying model's ``__init__`` function.

        Examples::

@@ -535,21 +696,6 @@ class AutoModelForSequenceClassification(object):
        when created with the `AutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path)`
        class method.

-        The `from_pretrained()` method takes care of returning the correct model class instance
-        based on the `model_type` property of the config object, or when it's missing,
-        falling back to using pattern matching on the `pretrained_model_name_or_path` string.
-
-        The model class to instantiate is selected as the first pattern matching
-        in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `distilbert`: DistilBertForSequenceClassification (DistilBERT model)
-            - contains `albert`: AlbertForSequenceClassification (ALBERT model)
-            - contains `camembert`: CamembertForSequenceClassification (CamemBERT model)
-            - contains `xlm-roberta`: XLMRobertaForSequenceClassification (XLM-RoBERTa model)
-            - contains `roberta`: RobertaForSequenceClassification (RoBERTa model)
-            - contains `bert`: BertForSequenceClassification (Bert model)
-            - contains `xlnet`: XLNetForSequenceClassification (XLNet model)
-            - contains `xlm`: XLMForSequenceClassification (XLM model)
-
        This class cannot be instantiated using `__init__()` (throws an error).
    """

@@ -565,13 +711,20 @@ class AutoModelForSequenceClassification(object):
        r""" Instantiates one of the base model classes of the library
        from a configuration.

-            config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
+        Args:
+            config (:class:`~transformers.PretrainedConfig`):
                The model class to instantiate is selected based on the configuration class:
-                    - isInstance of `distilbert` configuration class: DistilBertModel (DistilBERT model)
-                    - isInstance of `roberta` configuration class: RobertaModel (RoBERTa model)
-                    - isInstance of `bert` configuration class: BertModel (Bert model)
-                    - isInstance of `xlnet` configuration class: XLNetModel (XLNet model)
-                    - isInstance of `xlm` configuration class: XLMModel (XLM model)
+
+                - isInstance of `distilbert` configuration class: :class:`~transformers.DistilBertModelForSequenceClassification` (DistilBERT model)
+                - isInstance of `albert` configuration class: :class:`~transformers.AlbertModelForSequenceClassification` (ALBERT model)
+                - isInstance of `camembert` configuration class: :class:`~transformers.CamembertModelForSequenceClassification` (CamemBERT model)
+                - isInstance of `xlm roberta` configuration class: :class:`~transformers.XLMRobertaModelForSequenceClassification` (XLM-RoBERTa model)
+                - isInstance of `roberta` configuration class: :class:`~transformers.RobertaModelForSequenceClassification` (RoBERTa model)
+                - isInstance of `bert` configuration class: :class:`~transformers.BertModelForSequenceClassification` (Bert model)
+                - isInstance of `xlnet` configuration class: :class:`~transformers.XLNetModelForSequenceClassification` (XLNet model)
+                - isInstance of `xlm` configuration class: :class:`~transformers.XLMModelForSequenceClassification` (XLM model)
+                - isInstance of `flaubert` configuration class: :class:`~transformers.FlaubertForSequenceClassification` (Flaubert model)
+

        Examples::

@@ -601,19 +754,19 @@ class AutoModelForSequenceClassification(object):

        The model class to instantiate is selected as the first pattern matching
        in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `distilbert`: DistilBertForSequenceClassification (DistilBERT model)
-            - contains `albert`: AlbertForSequenceClassification (ALBERT model)
-            - contains `camembert`: CamembertForSequenceClassification (CamemBERT model)
-            - contains `xlm-roberta`: XLMRobertaForSequenceClassification (XLM-RoBERTa model)
-            - contains `roberta`: RobertaForSequenceClassification (RoBERTa model)
-            - contains `bert`: BertForSequenceClassification (Bert model)
-            - contains `xlnet`: XLNetForSequenceClassification (XLNet model)
-            - contains `xlm`: XLMForSequenceClassification (XLM model)
+            - contains `distilbert`: :class:`~transformers.DistilBertForSequenceClassification` (DistilBERT model)
+            - contains `albert`: :class:`~transformers.AlbertForSequenceClassification` (ALBERT model)
+            - contains `camembert`: :class:`~transformers.CamembertForSequenceClassification` (CamemBERT model)
+            - contains `xlm-roberta`: :class:`~transformers.XLMRobertaForSequenceClassification` (XLM-RoBERTa model)
+            - contains `roberta`: :class:`~transformers.RobertaForSequenceClassification` (RoBERTa model)
+            - contains `bert`: :class:`~transformers.BertForSequenceClassification` (Bert model)
+            - contains `xlnet`: :class:`~transformers.XLNetForSequenceClassification` (XLNet model)
+            - contains `flaubert`: :class:`~transformers.FlaubertForSequenceClassification` (Flaubert model)

        The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
        To train the model, you should first set it back in training mode with `model.train()`

-        Params:
+        Args:
            pretrained_model_name_or_path: either:

                - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
@@ -622,7 +775,7 @@ class AutoModelForSequenceClassification(object):
                - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.

            model_args: (`optional`) Sequence of positional arguments:
-                All remaning positional arguments will be passed to the underlying model's ``__init__`` method
+                All remaining positional arguments will be passed to the underlying model's ``__init__`` method

            config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
                Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
@@ -694,18 +847,6 @@ class AutoModelForQuestionAnswering(object):
        when created with the `AutoModelForQuestionAnswering.from_pretrained(pretrained_model_name_or_path)`
        class method.

-        The `from_pretrained()` method takes care of returning the correct model class instance
-        based on the `model_type` property of the config object, or when it's missing,
-        falling back to using pattern matching on the `pretrained_model_name_or_path` string.
-
-        The model class to instantiate is selected as the first pattern matching
-        in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `distilbert`: DistilBertForQuestionAnswering (DistilBERT model)
-            - contains `albert`: AlbertForQuestionAnswering (ALBERT model)
-            - contains `bert`: BertForQuestionAnswering (Bert model)
-            - contains `xlnet`: XLNetForQuestionAnswering (XLNet model)
-            - contains `xlm`: XLMForQuestionAnswering (XLM model)
-
        This class cannot be instantiated using `__init__()` (throws an error).
    """

@@ -721,12 +862,16 @@ class AutoModelForQuestionAnswering(object):
        r""" Instantiates one of the base model classes of the library
        from a configuration.

-            config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
+        Args:
+            config (:class:`~transformers.PretrainedConfig`):
                The model class to instantiate is selected based on the configuration class:
-                    - isInstance of `distilbert` configuration class: DistilBertModel (DistilBERT model)
-                    - isInstance of `bert` configuration class: BertModel (Bert model)
-                    - isInstance of `xlnet` configuration class: XLNetModel (XLNet model)
-                    - isInstance of `xlm` configuration class: XLMModel (XLM model)
+
+                - isInstance of `distilbert` configuration class: :class:`~transformers.DistilBertModelForQuestionAnswering` (DistilBERT model)
+                - isInstance of `albert` configuration class: :class:`~transformers.AlbertModelForQuestionAnswering` (ALBERT model)
+                - isInstance of `bert` configuration class: :class:`~transformers.BertModelForQuestionAnswering` (Bert model)
+                - isInstance of `xlnet` configuration class: :class:`~transformers.XLNetModelForQuestionAnswering` (XLNet model)
+                - isInstance of `xlm` configuration class: :class:`~transformers.XLMModelForQuestionAnswering` (XLM model)
+                - isInstance of `flaubert` configuration class: :class:`~transformers.FlaubertForQuestionAnswering` (XLM model)

        Examples::

@@ -757,16 +902,17 @@ class AutoModelForQuestionAnswering(object):

        The model class to instantiate is selected as the first pattern matching
        in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `distilbert`: DistilBertForQuestionAnswering (DistilBERT model)
-            - contains `albert`: AlbertForQuestionAnswering (ALBERT model)
-            - contains `bert`: BertForQuestionAnswering (Bert model)
-            - contains `xlnet`: XLNetForQuestionAnswering (XLNet model)
-            - contains `xlm`: XLMForQuestionAnswering (XLM model)
+            - contains `distilbert`: :class:`~transformers.DistilBertForQuestionAnswering` (DistilBERT model)
+            - contains `albert`: :class:`~transformers.AlbertForQuestionAnswering` (ALBERT model)
+            - contains `bert`: :class:`~transformers.BertForQuestionAnswering` (Bert model)
+            - contains `xlnet`: :class:`~transformers.XLNetForQuestionAnswering` (XLNet model)
+            - contains `xlm`: :class:`~transformers.XLMForQuestionAnswering` (XLM model)
+            - contains `flaubert`: :class:`~transformers.FlaubertForQuestionAnswering` (XLM model)

        The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
        To train the model, you should first set it back in training mode with `model.train()`

-        Params:
+        Args:
            pretrained_model_name_or_path: either:

                - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
@@ -839,6 +985,15 @@ class AutoModelForQuestionAnswering(object):


 class AutoModelForTokenClassification:
+    r"""
+        :class:`~transformers.AutoModelForTokenClassification` is a generic model class
+        that will be instantiated as one of the token classification model classes of the library
+        when created with the `AutoModelForTokenClassification.from_pretrained(pretrained_model_name_or_path)`
+        class method.
+
+        This class cannot be instantiated using `__init__()` (throws an error).
+    """
+
    def __init__(self):
        raise EnvironmentError(
            "AutoModelForTokenClassification is designed to be instantiated "
@@ -851,13 +1006,16 @@ class AutoModelForTokenClassification:
        r""" Instantiates one of the base model classes of the library
        from a configuration.

-            config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
+        Args:
+            config (:class:`~transformers.PretrainedConfig`):
                The model class to instantiate is selected based on the configuration class:
-                    - isInstance of `distilbert` configuration class: DistilBertModel (DistilBERT model)
-                    - isInstance of `bert` configuration class: BertModel (Bert model)
-                    - isInstance of `xlnet` configuration class: XLNetModel (XLNet model)
-                    - isInstance of `camembert` configuration class: CamembertModel (Camembert model)
-                    - isInstance of `roberta` configuration class: RobertaModel (Roberta model)
+
+                - isInstance of `distilbert` configuration class: :class:`~transformers.DistilBertModelForTokenClassification` (DistilBERT model)
+                - isInstance of `xlm roberta` configuration class: :class:`~transformers.XLMRobertaModelForTokenClassification` (XLMRoberta model)
+                - isInstance of `bert` configuration class: :class:`~transformers.BertModelForTokenClassification` (Bert model)
+                - isInstance of `xlnet` configuration class: :class:`~transformers.XLNetModelForTokenClassification` (XLNet model)
+                - isInstance of `camembert` configuration class: :class:`~transformers.CamembertModelForTokenClassification` (Camembert model)
+                - isInstance of `roberta` configuration class: :class:`~transformers.RobertaModelForTokenClassification` (Roberta model)

        Examples::

@@ -888,17 +1046,19 @@ class AutoModelForTokenClassification:

        The model class to instantiate is selected as the first pattern matching
        in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `distilbert`: DistilBertForTokenClassification (DistilBERT model)
-            - contains `camembert`: CamembertForTokenClassification (Camembert model)
-            - contains `bert`: BertForTokenClassification (Bert model)
-            - contains `xlnet`: XLNetForTokenClassification (XLNet model)
-            - contains `roberta`: RobertaForTokenClassification (Roberta model)
+            - contains `distilbert`: :class:`~transformers.DistilBertForTokenClassification` (DistilBERT model)
+            - contains `xlm-roberta`: :class:`~transformers.XLMRobertaForTokenClassification` (XLM-RoBERTa?Para model)
+            - contains `camembert`: :class:`~transformers.CamembertForTokenClassification` (Camembert model)
+            - contains `bert`: :class:`~transformers.BertForTokenClassification` (Bert model)
+            - contains `xlnet`: :class:`~transformers.XLNetForTokenClassification` (XLNet model)
+            - contains `roberta`: :class:`~transformers.RobertaForTokenClassification` (Roberta model)

        The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
        To train the model, you should first set it back in training mode with `model.train()`

-        Params:
-            pretrained_model_name_or_path: either:
+        Args:
+            pretrained_model_name_or_path:
+                Either:

                - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
                - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
@@ -53,6 +53,7 @@ BERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
    "bert-base-japanese-char-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-whole-word-masking-pytorch_model.bin",
    "bert-base-finnish-cased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-cased-v1/pytorch_model.bin",
    "bert-base-finnish-uncased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-uncased-v1/pytorch_model.bin",
+    "bert-base-dutch-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/wietsedv/bert-base-dutch-cased/pytorch_model.bin",
 }


@@ -33,6 +33,8 @@ logger = logging.getLogger(__name__)

 CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
    "camembert-base": "https://s3.amazonaws.com/models.huggingface.co/bert/camembert-base-pytorch_model.bin",
+    "umberto-commoncrawl-cased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/Musixmatch/umberto-commoncrawl-cased-v1/pytorch_model.bin",
+    "umberto-wikipedia-uncased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/Musixmatch/umberto-wikipedia-uncased-v1/pytorch_model.bin",
 }


@@ -0,0 +1,385 @@
+# coding=utf-8
+# Copyright 2019-present CNRS, Facebook Inc. and the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch Flaubert model, based on XLM. """
+
+
+import logging
+import random
+
+import torch
+from torch.nn import functional as F
+
+from .configuration_flaubert import FlaubertConfig
+from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
+from .modeling_xlm import (
+    XLMForQuestionAnswering,
+    XLMForQuestionAnsweringSimple,
+    XLMForSequenceClassification,
+    XLMModel,
+    XLMWithLMHeadModel,
+    get_masks,
+)
+
+
+logger = logging.getLogger(__name__)
+
+FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
+    "flaubert-small-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_small_cased/pytorch_model.bin",
+    "flaubert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_base_uncased/pytorch_model.bin",
+    "flaubert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_base_cased/pytorch_model.bin",
+    "flaubert-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_large_cased/pytorch_model.bin",
+}
+
+
+FLAUBERT_START_DOCSTRING = r"""
+
+    This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
+    usage and behavior.
+
+    Parameters:
+        config (:class:`~transformers.FlaubertConfig`): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the configuration.
+            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
+"""
+
+FLAUBERT_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary.
+
+            Indices can be obtained using :class:`transformers.BertTokenizer`.
+            See :func:`transformers.PreTrainedTokenizer.encode` and
+            :func:`transformers.PreTrainedTokenizer.encode_plus` for details.
+
+            `What are input IDs? <../glossary.html#input-ids>`__
+        attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
+            Mask to avoid performing attention on padding token indices.
+            Mask values selected in ``[0, 1]``:
+            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
+
+            `What are attention masks? <../glossary.html#attention-mask>`__
+        token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
+            Segment token indices to indicate first and second portions of the inputs.
+            Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
+            corresponds to a `sentence B` token
+
+            `What are token type IDs? <../glossary.html#token-type-ids>`_
+        position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
+            Indices of positions of each input sequence tokens in the position embeddings.
+            Selected in the range ``[0, config.max_position_embeddings - 1]``.
+
+            `What are position IDs? <../glossary.html#position-ids>`_
+        lengths (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
+            Length of each sentence that can be used to avoid performing attention on padding token indices.
+            You can also use `attention_mask` for the same result (see above), kept here for compatbility.
+            Indices selected in ``[0, ..., input_ids.size(-1)]``:
+        cache (:obj:`Dict[str, torch.FloatTensor]`, `optional`, defaults to :obj:`None`):
+            dictionary with ``torch.FloatTensor`` that contains pre-computed
+            hidden-states (key and values in the attention blocks) as computed by the model
+            (see `cache` output below). Can be used to speed up sequential decoding.
+            The dictionary object will be modified in-place during the forward pass to add newly computed hidden-states.
+        head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
+            Mask to nullify selected heads of the self-attention modules.
+            Mask values selected in ``[0, 1]``:
+            :obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
+        input_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
+            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
+            This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+            than the model's internal embedding lookup matrix.
+"""
+
+
+@add_start_docstrings(
+    "The bare Flaubert Model transformer outputting raw hidden-states without any specific head on top.",
+    FLAUBERT_START_DOCSTRING,
+)
+class FlaubertModel(XLMModel):
+
+    config_class = FlaubertConfig
+    pretrained_model_archive_map = FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP
+
+    def __init__(self, config):  # , dico, is_encoder, with_output):
+        super(FlaubertModel, self).__init__(config)
+        self.layerdrop = getattr(config, "layerdrop", 0.0)
+        self.pre_norm = getattr(config, "pre_norm", False)
+
+    @add_start_docstrings_to_callable(FLAUBERT_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        langs=None,
+        token_type_ids=None,
+        position_ids=None,
+        lengths=None,
+        cache=None,
+        head_mask=None,
+        inputs_embeds=None,
+    ):
+        r"""
+    Return:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs:
+        last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
+            Sequence of hidden-states at the output of the last layer of the model.
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
+            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+
+    Examples::
+
+        tokenizer = FlaubertTokenizer.from_pretrained('flaubert-base-cased')
+        model = FlaubertModel.from_pretrained('flaubert-base-cased')
+        input_ids = torch.tensor(tokenizer.encode("Le chat manges une pomme.", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
+        outputs = model(input_ids)
+        last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
+
+        """
+        # removed: src_enc=None, src_len=None
+        if input_ids is not None:
+            bs, slen = input_ids.size()
+        else:
+            bs, slen = inputs_embeds.size()[:-1]
+
+        if lengths is None:
+            if input_ids is not None:
+                lengths = (input_ids != self.pad_index).sum(dim=1).long()
+            else:
+                lengths = torch.LongTensor([slen] * bs)
+        # mask = input_ids != self.pad_index
+
+        # check inputs
+        assert lengths.size(0) == bs
+        assert lengths.max().item() <= slen
+        # input_ids = input_ids.transpose(0, 1)  # batch size as dimension 0
+        # assert (src_enc is None) == (src_len is None)
+        # if src_enc is not None:
+        #     assert self.is_decoder
+        #     assert src_enc.size(0) == bs
+
+        # generate masks
+        mask, attn_mask = get_masks(slen, lengths, self.causal, padding_mask=attention_mask)
+        # if self.is_decoder and src_enc is not None:
+        #     src_mask = torch.arange(src_len.max(), dtype=torch.long, device=lengths.device) < src_len[:, None]
+
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        # position_ids
+        if position_ids is None:
+            position_ids = torch.arange(slen, dtype=torch.long, device=device)
+            position_ids = position_ids.unsqueeze(0).expand((bs, slen))
+        else:
+            assert position_ids.size() == (bs, slen)  # (slen, bs)
+            # position_ids = position_ids.transpose(0, 1)
+
+        # langs
+        if langs is not None:
+            assert langs.size() == (bs, slen)  # (slen, bs)
+            # langs = langs.transpose(0, 1)
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x qlen x klen]
+        if head_mask is not None:
+            if head_mask.dim() == 1:
+                head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+                head_mask = head_mask.expand(self.n_layers, -1, -1, -1, -1)
+            elif head_mask.dim() == 2:
+                head_mask = (
+                    head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
+                )  # We can specify head_mask for each layer
+            head_mask = head_mask.to(
+                dtype=next(self.parameters()).dtype
+            )  # switch to fload if need + fp16 compatibility
+        else:
+            head_mask = [None] * self.n_layers
+
+        # do not recompute cached elements
+        if cache is not None and input_ids is not None:
+            _slen = slen - cache["slen"]
+            input_ids = input_ids[:, -_slen:]
+            position_ids = position_ids[:, -_slen:]
+            if langs is not None:
+                langs = langs[:, -_slen:]
+            mask = mask[:, -_slen:]
+            attn_mask = attn_mask[:, -_slen:]
+
+        # embeddings
+        if inputs_embeds is None:
+            inputs_embeds = self.embeddings(input_ids)
+
+        tensor = inputs_embeds + self.position_embeddings(position_ids).expand_as(inputs_embeds)
+        if langs is not None and self.use_lang_emb:
+            tensor = tensor + self.lang_embeddings(langs)
+        if token_type_ids is not None:
+            tensor = tensor + self.embeddings(token_type_ids)
+        tensor = self.layer_norm_emb(tensor)
+        tensor = F.dropout(tensor, p=self.dropout, training=self.training)
+        tensor *= mask.unsqueeze(-1).to(tensor.dtype)
+
+        # transformer layers
+        hidden_states = ()
+        attentions = ()
+        for i in range(self.n_layers):
+            # LayerDrop
+            dropout_probability = random.uniform(0, 1)
+            if self.training and (dropout_probability < self.layerdrop):
+                continue
+
+            if self.output_hidden_states:
+                hidden_states = hidden_states + (tensor,)
+
+            # self attention
+            if not self.pre_norm:
+                attn_outputs = self.attentions[i](tensor, attn_mask, cache=cache, head_mask=head_mask[i])
+                attn = attn_outputs[0]
+                if self.output_attentions:
+                    attentions = attentions + (attn_outputs[1],)
+                attn = F.dropout(attn, p=self.dropout, training=self.training)
+                tensor = tensor + attn
+                tensor = self.layer_norm1[i](tensor)
+            else:
+                tensor_normalized = self.layer_norm1[i](tensor)
+                attn_outputs = self.attentions[i](tensor_normalized, attn_mask, cache=cache, head_mask=head_mask[i])
+                attn = attn_outputs[0]
+                if self.output_attentions:
+                    attentions = attentions + (attn_outputs[1],)
+                attn = F.dropout(attn, p=self.dropout, training=self.training)
+                tensor = tensor + attn
+
+            # encoder attention (for decoder only)
+            # if self.is_decoder and src_enc is not None:
+            #     attn = self.encoder_attn[i](tensor, src_mask, kv=src_enc, cache=cache)
+            #     attn = F.dropout(attn, p=self.dropout, training=self.training)
+            #     tensor = tensor + attn
+            #     tensor = self.layer_norm15[i](tensor)
+
+            # FFN
+            if not self.pre_norm:
+                tensor = tensor + self.ffns[i](tensor)
+                tensor = self.layer_norm2[i](tensor)
+            else:
+                tensor_normalized = self.layer_norm2[i](tensor)
+                tensor = tensor + self.ffns[i](tensor_normalized)
+
+            tensor *= mask.unsqueeze(-1).to(tensor.dtype)
+
+        # Add last hidden state
+        if self.output_hidden_states:
+            hidden_states = hidden_states + (tensor,)
+
+        # update cache length
+        if cache is not None:
+            cache["slen"] += tensor.size(1)
+
+        # move back sequence length to dimension 0
+        # tensor = tensor.transpose(0, 1)
+
+        outputs = (tensor,)
+        if self.output_hidden_states:
+            outputs = outputs + (hidden_states,)
+        if self.output_attentions:
+            outputs = outputs + (attentions,)
+        return outputs  # outputs, (hidden_states), (attentions)
+
+
+@add_start_docstrings(
+    """The Flaubert Model transformer with a language modeling head on top
+    (linear layer with weights tied to the input embeddings). """,
+    FLAUBERT_START_DOCSTRING,
+)
+class FlaubertWithLMHeadModel(XLMWithLMHeadModel):
+    """
+    This class overrides :class:`~transformers.XLMWithLMHeadModel`. Please check the
+    superclass for the appropriate documentation alongside usage examples.
+    """
+
+    config_class = FlaubertConfig
+    pretrained_model_archive_map = FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP
+
+    def __init__(self, config):
+        super(FlaubertWithLMHeadModel, self).__init__(config)
+        self.transformer = FlaubertModel(config)
+        self.init_weights()
+
+
+@add_start_docstrings(
+    """Flaubert Model with a sequence classification/regression head on top (a linear layer on top of
+    the pooled output) e.g. for GLUE tasks. """,
+    FLAUBERT_START_DOCSTRING,
+)
+class FlaubertForSequenceClassification(XLMForSequenceClassification):
+    """
+    This class overrides :class:`~transformers.XLMForSequenceClassification`. Please check the
+    superclass for the appropriate documentation alongside usage examples.
+    """
+
+    config_class = FlaubertConfig
+    pretrained_model_archive_map = FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP
+
+    def __init__(self, config):
+        super(FlaubertForSequenceClassification, self).__init__(config)
+        self.transformer = FlaubertModel(config)
+        self.init_weights()
+
+
+@add_start_docstrings(
+    """Flaubert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
+    the hidden-states output to compute `span start logits` and `span end logits`). """,
+    FLAUBERT_START_DOCSTRING,
+)
+class FlaubertForQuestionAnsweringSimple(XLMForQuestionAnsweringSimple):
+    """
+    This class overrides :class:`~transformers.XLMForQuestionAnsweringSimple`. Please check the
+    superclass for the appropriate documentation alongside usage examples.
+    """
+
+    config_class = FlaubertConfig
+    pretrained_model_archive_map = FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP
+
+    def __init__(self, config):
+        super(FlaubertForQuestionAnsweringSimple, self).__init__(config)
+        self.transformer = FlaubertModel(config)
+        self.init_weights()
+
+
+@add_start_docstrings(
+    """Flaubert Model with a beam-search span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
+    the hidden-states output to compute `span start logits` and `span end logits`). """,
+    FLAUBERT_START_DOCSTRING,
+)
+class FlaubertForQuestionAnswering(XLMForQuestionAnswering):
+    """
+    This class overrides :class:`~transformers.XLMForQuestionAnswering`. Please check the
+    superclass for the appropriate documentation alongside usage examples.
+    """
+
+    config_class = FlaubertConfig
+    pretrained_model_archive_map = FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP
+
+    def __init__(self, config):
+        super(FlaubertForQuestionAnswering, self).__init__(config)
+        self.transformer = FlaubertModel(config)
+        self.init_weights()
@@ -802,9 +802,9 @@ class T5WithLMHeadModel(T5PreTrainedModel):
    r"""
        **lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
            Labels for computing the masked language modeling loss.
-            Indices should be in ``[-1, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
-            Tokens with indices set to ``-1`` are ignored (masked), the loss is only computed for the tokens with labels
-            in ``[0, ..., config.vocab_size]``
+            Indices should either be in ``[0, ..., config.vocab_size]`` or -100 (see ``input_ids`` docstring).
+            Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
+            in ``[0, ..., config.vocab_size]``.

    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
        **loss**: (`optional`, returned when ``lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
@@ -42,6 +42,7 @@ from .modeling_tf_albert import (
 from .modeling_tf_bert import (
    TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
    TFBertForMaskedLM,
+    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFBertForTokenClassification,
@@ -125,6 +126,22 @@ TF_MODEL_MAPPING = OrderedDict(
    ]
 )

+TF_MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
+    [
+        (T5Config, TFT5WithLMHeadModel),
+        (DistilBertConfig, TFDistilBertForMaskedLM),
+        (AlbertConfig, TFAlbertForMaskedLM),
+        (RobertaConfig, TFRobertaForMaskedLM),
+        (BertConfig, TFBertForPreTraining),
+        (OpenAIGPTConfig, TFOpenAIGPTLMHeadModel),
+        (GPT2Config, TFGPT2LMHeadModel),
+        (TransfoXLConfig, TFTransfoXLLMHeadModel),
+        (XLNetConfig, TFXLNetLMHeadModel),
+        (XLMConfig, TFXLMWithLMHeadModel),
+        (CTRLConfig, TFCTRLLMHeadModel),
+    ]
+)
+
 TF_MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
    [
        (T5Config, TFT5WithLMHeadModel),
@@ -329,6 +346,154 @@ class TFAutoModel(object):
        )


+class TFAutoModelForPreTraining(object):
+    r"""
+        :class:`~transformers.TFAutoModelForPreTraining` is a generic model class
+        that will be instantiated as one of the model classes of the library -with the architecture used for pretraining this model– when created with the `TFAutoModelForPreTraining.from_pretrained(pretrained_model_name_or_path)`
+        class method.
+
+        This class cannot be instantiated using `__init__()` (throws an error).
+    """
+
+    def __init__(self):
+        raise EnvironmentError(
+            "TFAutoModelForPreTraining is designed to be instantiated "
+            "using the `TFAutoModelForPreTraining.from_pretrained(pretrained_model_name_or_path)` or "
+            "`TFAutoModelForPreTraining.from_config(config)` methods."
+        )
+
+    @classmethod
+    def from_config(cls, config):
+        r""" Instantiates one of the base model classes of the library
+        from a configuration.
+
+        Args:
+            config (:class:`~transformers.PretrainedConfig`):
+                The model class to instantiate is selected based on the configuration class:
+
+                - isInstance of `distilbert` configuration class: :class:`~transformers.TFDistilBertModelForMaskedLM` (DistilBERT model)
+                - isInstance of `roberta` configuration class: :class:`~transformers.TFRobertaModelForMaskedLM` (RoBERTa model)
+                - isInstance of `bert` configuration class: :class:`~transformers.TFBertForPreTraining` (Bert model)
+                - isInstance of `openai-gpt` configuration class: :class:`~transformers.TFOpenAIGPTLMHeadModel` (OpenAI GPT model)
+                - isInstance of `gpt2` configuration class: :class:`~transformers.TFGPT2ModelLMHeadModel` (OpenAI GPT-2 model)
+                - isInstance of `ctrl` configuration class: :class:`~transformers.TFCTRLModelLMHeadModel` (Salesforce CTRL  model)
+                - isInstance of `transfo-xl` configuration class: :class:`~transformers.TFTransfoXLLMHeadModel` (Transformer-XL model)
+                - isInstance of `xlnet` configuration class: :class:`~transformers.TFXLNetLMHeadModel` (XLNet model)
+                - isInstance of `xlm` configuration class: :class:`~transformers.TFXLMWithLMHeadModel` (XLM model)
+
+        Examples::
+
+            config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
+            model = TFAutoModelForPreTraining.from_config(config)  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
+        """
+        for config_class, model_class in TF_MODEL_FOR_PRETRAINING_MAPPING.items():
+            if isinstance(config, config_class):
+                return model_class(config)
+        raise ValueError(
+            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
+            "Model type should be one of {}.".format(
+                config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_FOR_PRETRAINING_MAPPING.keys())
+            )
+        )
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+        r""" Instantiates one of the model classes of the library -with the architecture used for pretraining this model– from a pre-trained model configuration.
+
+        The `from_pretrained()` method takes care of returning the correct model class instance
+        based on the `model_type` property of the config object, or when it's missing,
+        falling back to using pattern matching on the `pretrained_model_name_or_path` string.
+
+        The model class to instantiate is selected as the first pattern matching
+        in the `pretrained_model_name_or_path` string (in the following order):
+            - contains `t5`: :class:`~transformers.TFT5ModelWithLMHead` (T5 model)
+            - contains `distilbert`: :class:`~transformers.TFDistilBertForMaskedLM` (DistilBERT model)
+            - contains `albert`: :class:`~transformers.TFAlbertForMaskedLM` (ALBERT model)
+            - contains `roberta`: :class:`~transformers.TFRobertaForMaskedLM` (RoBERTa model)
+            - contains `bert`: :class:`~transformers.TFBertForPreTraining` (Bert model)
+            - contains `openai-gpt`: :class:`~transformers.TFOpenAIGPTLMHeadModel` (OpenAI GPT model)
+            - contains `gpt2`: :class:`~transformers.TFGPT2LMHeadModel` (OpenAI GPT-2 model)
+            - contains `transfo-xl`: :class:`~transformers.TFTransfoXLLMHeadModel` (Transformer-XL model)
+            - contains `xlnet`: :class:`~transformers.TFXLNetLMHeadModel` (XLNet model)
+            - contains `xlm`: :class:`~transformers.TFXLMWithLMHeadModel` (XLM model)
+            - contains `ctrl`: :class:`~transformers.TFCTRLLMHeadModel` (Salesforce CTRL model)
+
+        The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
+        To train the model, you should first set it back in training mode with `model.train()`
+
+        Args:
+            pretrained_model_name_or_path:
+                Either:
+
+                - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
+                - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
+                - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
+                - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
+            model_args: (`optional`) Sequence of positional arguments:
+                All remaning positional arguments will be passed to the underlying model's ``__init__`` method
+            config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
+                Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
+
+                - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
+                - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
+                - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
+
+            state_dict: (`optional`) dict:
+                an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
+                This option can be used if you want to create a model from a pretrained configuration but load your own weights.
+                In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
+            cache_dir: (`optional`) string:
+                Path to a directory in which a downloaded pre-trained model
+                configuration should be cached if the standard cache should not be used.
+            force_download: (`optional`) boolean, default False:
+                Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
+            resume_download: (`optional`) boolean, default False:
+                Do not delete incompletely received file. Attempt to resume the download if such a file exists.
+            proxies: (`optional`) dict, default None:
+                A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
+                The proxies are used on each request.
+            output_loading_info: (`optional`) boolean:
+                Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
+            kwargs: (`optional`) Remaining dictionary of keyword arguments:
+                Can be used to update the configuration object (after it being loaded) and initiate the model.
+                (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or
+                automatically loaded:
+
+                - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the
+                  underlying model's ``__init__`` method (we assume all relevant updates to the configuration have
+                  already been done)
+                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class
+                  initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of
+                  ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute
+                  with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration
+                  attribute will be passed to the underlying model's ``__init__`` function.
+
+        Examples::
+
+            model = TFAutoModelForPreTraining.from_pretrained('bert-base-uncased')    # Download model and configuration from S3 and cache.
+            model = TFAutoModelForPreTraining.from_pretrained('./test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
+            model = TFAutoModelForPreTraining.from_pretrained('bert-base-uncased', output_attention=True)  # Update configuration during loading
+            assert model.config.output_attention == True
+            # Loading from a TF checkpoint file instead of a PyTorch model (slower)
+            config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
+            model = TFAutoModelForPreTraining.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
+
+        """
+        config = kwargs.pop("config", None)
+        if not isinstance(config, PretrainedConfig):
+            config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
+
+        for config_class, model_class in TF_MODEL_FOR_PRETRAINING_MAPPING.items():
+            if isinstance(config, config_class):
+                return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
+        raise ValueError(
+            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
+            "Model type should be one of {}.".format(
+                config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_FOR_PRETRAINING_MAPPING.keys())
+            )
+        )
+
+
 class TFAutoModelWithLMHead(object):
    r"""
        :class:`~transformers.TFAutoModelWithLMHead` is a generic model class
@@ -383,7 +548,7 @@ class TFAutoModelWithLMHead(object):
        Examples::

            config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
-            model = AutoModelWithLMHead.from_config(config)  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
+            model = TFAutoModelWithLMHead.from_config(config)  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
        """
        for config_class, model_class in TF_MODEL_WITH_LM_HEAD_MAPPING.items():
            if isinstance(config, config_class):
@@ -49,6 +49,7 @@ TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
    "bert-base-japanese-char-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-whole-word-masking-tf_model.h5",
    "bert-base-finnish-cased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-cased-v1/tf_model.h5",
    "bert-base-finnish-uncased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-uncased-v1/tf_model.h5",
+    "bert-base-dutch-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/wietsedv/bert-base-dutch-cased/tf_model.h5",
 }


@@ -0,0 +1,118 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" TF 2.0 RoBERTa model. """
+
+
+import logging
+
+from .configuration_camembert import CamembertConfig
+from .file_utils import add_start_docstrings
+from .modeling_tf_roberta import (
+    TFRobertaForMaskedLM,
+    TFRobertaForSequenceClassification,
+    TFRobertaForTokenClassification,
+    TFRobertaModel,
+)
+
+
+logger = logging.getLogger(__name__)
+
+TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {}
+
+
+CAMEMBERT_START_DOCSTRING = r"""
+
+    .. note::
+
+        TF 2.0 models accepts two formats as inputs:
+
+            - having all inputs as keyword arguments (like PyTorch models), or
+            - having all inputs as a list, tuple or dict in the first positional arguments.
+
+        This second option is useful when using :obj:`tf.keras.Model.fit()` method which currently requires having
+        all the tensors in the first argument of the model call function: :obj:`model(inputs)`.
+
+        If you choose this second option, there are three possibilities you can use to gather all the input Tensors
+        in the first positional argument :
+
+        - a single Tensor with input_ids only and nothing else: :obj:`model(inputs_ids)`
+        - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
+          :obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
+        - a dictionary with one or several input Tensors associated to the input names given in the docstring:
+          :obj:`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
+
+    Parameters:
+        config (:class:`~transformers.CamembertConfig`): Model configuration class with all the parameters of the
+            model. Initializing with a config file does not load the weights associated with the model, only the configuration.
+            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top.",
+    CAMEMBERT_START_DOCSTRING,
+)
+class TFCamembertModel(TFRobertaModel):
+    """
+    This class overrides :class:`~transformers.TFRobertaModel`. Please check the
+    superclass for the appropriate documentation alongside usage examples.
+    """
+
+    config_class = CamembertConfig
+    pretrained_model_archive_map = TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
+
+
+@add_start_docstrings(
+    """CamemBERT Model with a `language modeling` head on top. """, CAMEMBERT_START_DOCSTRING,
+)
+class TFCamembertForMaskedLM(TFRobertaForMaskedLM):
+    """
+    This class overrides :class:`~transformers.TFRobertaForMaskedLM`. Please check the
+    superclass for the appropriate documentation alongside usage examples.
+    """
+
+    config_class = CamembertConfig
+    pretrained_model_archive_map = TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
+
+
+@add_start_docstrings(
+    """CamemBERT Model transformer with a sequence classification/regression head on top (a linear layer
+    on top of the pooled output) e.g. for GLUE tasks. """,
+    CAMEMBERT_START_DOCSTRING,
+)
+class TFCamembertForSequenceClassification(TFRobertaForSequenceClassification):
+    """
+    This class overrides :class:`~transformers.TFRobertaForSequenceClassification`. Please check the
+    superclass for the appropriate documentation alongside usage examples.
+    """
+
+    config_class = CamembertConfig
+    pretrained_model_archive_map = TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
+
+
+@add_start_docstrings(
+    """CamemBERT Model with a token classification head on top (a linear layer on top of
+    the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
+    CAMEMBERT_START_DOCSTRING,
+)
+class TFCamembertForTokenClassification(TFRobertaForTokenClassification):
+    """
+    This class overrides :class:`~transformers.TFRobertaForTokenClassification`. Please check the
+    superclass for the appropriate documentation alongside usage examples.
+    """
+
+    config_class = CamembertConfig
+    pretrained_model_archive_map = TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
@@ -0,0 +1,118 @@
+# coding=utf-8
+# Copyright 2019 Facebook AI Research and the HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" TF 2.0  XLM-RoBERTa model. """
+
+
+import logging
+
+from .configuration_xlm_roberta import XLMRobertaConfig
+from .file_utils import add_start_docstrings
+from .modeling_tf_roberta import (
+    TFRobertaForMaskedLM,
+    TFRobertaForSequenceClassification,
+    TFRobertaForTokenClassification,
+    TFRobertaModel,
+)
+
+
+logger = logging.getLogger(__name__)
+
+TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP = {}
+
+
+XLM_ROBERTA_START_DOCSTRING = r"""
+
+    .. note::
+
+        TF 2.0 models accepts two formats as inputs:
+
+            - having all inputs as keyword arguments (like PyTorch models), or
+            - having all inputs as a list, tuple or dict in the first positional arguments.
+
+        This second option is useful when using :obj:`tf.keras.Model.fit()` method which currently requires having
+        all the tensors in the first argument of the model call function: :obj:`model(inputs)`.
+
+        If you choose this second option, there are three possibilities you can use to gather all the input Tensors
+        in the first positional argument :
+
+        - a single Tensor with input_ids only and nothing else: :obj:`model(inputs_ids)`
+        - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
+          :obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
+        - a dictionary with one or several input Tensors associated to the input names given in the docstring:
+          :obj:`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
+
+    Parameters:
+        config (:class:`~transformers.XLMRobertaConfig`): Model configuration class with all the parameters of the
+            model. Initializing with a config file does not load the weights associated with the model, only the configuration.
+            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare XLM-RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
+    XLM_ROBERTA_START_DOCSTRING,
+)
+class TFXLMRobertaModel(TFRobertaModel):
+    """
+    This class overrides :class:`~transformers.TFRobertaModel`. Please check the
+    superclass for the appropriate documentation alongside usage examples.
+    """
+
+    config_class = XLMRobertaConfig
+    pretrained_model_archive_map = TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
+
+
+@add_start_docstrings(
+    """XLM-RoBERTa Model with a `language modeling` head on top. """, XLM_ROBERTA_START_DOCSTRING,
+)
+class TFXLMRobertaForMaskedLM(TFRobertaForMaskedLM):
+    """
+    This class overrides :class:`~transformers.TFRobertaForMaskedLM`. Please check the
+    superclass for the appropriate documentation alongside usage examples.
+    """
+
+    config_class = XLMRobertaConfig
+    pretrained_model_archive_map = TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
+
+
+@add_start_docstrings(
+    """XLM-RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer
+    on top of the pooled output) e.g. for GLUE tasks. """,
+    XLM_ROBERTA_START_DOCSTRING,
+)
+class TFXLMRobertaForSequenceClassification(TFRobertaForSequenceClassification):
+    """
+    This class overrides :class:`~transformers.TFRobertaForSequenceClassification`. Please check the
+    superclass for the appropriate documentation alongside usage examples.
+    """
+
+    config_class = XLMRobertaConfig
+    pretrained_model_archive_map = TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
+
+
+@add_start_docstrings(
+    """XLM-RoBERTa Model with a token classification head on top (a linear layer on top of
+    the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
+    XLM_ROBERTA_START_DOCSTRING,
+)
+class TFXLMRobertaForTokenClassification(TFRobertaForTokenClassification):
+    """
+    This class overrides :class:`~transformers.TFRobertaForTokenClassification`. Please check the
+    superclass for the appropriate documentation alongside usage examples.
+    """
+
+    config_class = XLMRobertaConfig
+    pretrained_model_archive_map = TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
@@ -284,6 +284,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
        # Only save the model itself if we are using distributed training
        model_to_save = self.module if hasattr(self, "module") else self

+        # Attach architecture to the config
+        model_to_save.config.architectures = [model_to_save.__class__.__name__]
+
        # Save configuration file
        model_to_save.config.save_pretrained(save_directory)

@@ -512,7 +515,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):

            # PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants
            # so we need to apply the function recursively.
-            def load(module, prefix=""):
+            def load(module: nn.Module, prefix=""):
                local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
                module._load_from_state_dict(
                    state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs
@@ -756,7 +756,7 @@ class XLNetModel(XLNetPreTrainedModel):
            input_ids = input_ids.transpose(0, 1).contiguous()
            qlen, bsz = input_ids.shape[0], input_ids.shape[1]
        elif inputs_embeds is not None:
-            inputs_embeds.transpose(0, 1).contiguous()
+            inputs_embeds = inputs_embeds.transpose(0, 1).contiguous()
            qlen, bsz = inputs_embeds.shape[0], inputs_embeds.shape[1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")
@@ -28,7 +28,10 @@ from typing import Dict, List, Optional, Tuple, Union
 import numpy as np

 from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, AutoConfig
+from .configuration_distilbert import DistilBertConfig
+from .configuration_roberta import RobertaConfig
 from .configuration_utils import PretrainedConfig
+from .configuration_xlm import XLMConfig
 from .data import SquadExample, squad_convert_examples_to_features
 from .file_utils import is_tf_available, is_torch_available
 from .modelcard import ModelCard
@@ -44,6 +47,7 @@ if is_tf_available():
        TFAutoModelForSequenceClassification,
        TFAutoModelForQuestionAnswering,
        TFAutoModelForTokenClassification,
+        TFAutoModelWithLMHead,
    )

 if is_torch_available():
@@ -53,6 +57,7 @@ if is_torch_available():
        AutoModelForSequenceClassification,
        AutoModelForQuestionAnswering,
        AutoModelForTokenClassification,
+        AutoModelWithLMHead,
    )


@@ -64,7 +69,7 @@ def get_framework(model=None):
        If both frameworks are installed and no specific model is provided, defaults to using PyTorch.
    """
    if is_tf_available() and is_torch_available() and model is not None and not isinstance(model, str):
-        # Both framework are available but the use supplied a model class instance.
+        # Both framework are available but the user supplied a model class instance.
        # Try to guess which framework to use from the model classname
        framework = "tf" if model.__class__.__name__.startswith("TF") else "pt"
    elif not is_tf_available() and not is_torch_available():
@@ -364,7 +369,6 @@ class Pipeline(_ScikitCompat):
    def predict(self, X):
        """
        Scikit / Keras interface to transformers' pipelines. This method will forward to __call__().
-        Se
        """
        return self(X=X)

@@ -406,9 +410,8 @@ class Pipeline(_ScikitCompat):
            dict holding all the required parameters for model's forward
        """
        args = ["input_ids", "attention_mask"]
-        model_type = type(self.model).__name__.lower()

-        if "distilbert" not in model_type and "xlm" not in model_type:
+        if not isinstance(self.model.config, (DistilBertConfig, XLMConfig, RobertaConfig)):
            args += ["token_type_ids"]

        # PR #1548 (CLI) There is an issue with attention_mask
@@ -420,7 +423,10 @@ class Pipeline(_ScikitCompat):
        else:
            return {k: [feature[k] for feature in features] for k in args}

-    def __call__(self, *texts, **kwargs):
+    def _parse_and_tokenize(self, *texts, **kwargs):
+        """
+        Parse arguments and tokenize
+        """
        # Parse arguments
        inputs = self._args_parser(*texts, **kwargs)
        inputs = self.tokenizer.batch_encode_plus(
@@ -429,13 +435,19 @@ class Pipeline(_ScikitCompat):

        # Filter out features not available on specific models
        inputs = self.inputs_for_model(inputs)
+
+        return inputs
+
+    def __call__(self, *texts, **kwargs):
+        inputs = self._parse_and_tokenize(*texts, **kwargs)
        return self._forward(inputs)

-    def _forward(self, inputs):
+    def _forward(self, inputs, return_tensors=False):
        """
        Internal framework specific forward dispatching.
        Args:
            inputs: dict holding all the keyworded arguments for required by the model forward method.
+            return_tensors: Whether to return native framework (pt/tf) tensors rather than numpy array.
        Returns:
            Numpy array
        """
@@ -449,7 +461,10 @@ class Pipeline(_ScikitCompat):
                    inputs = self.ensure_tensor_on_device(**inputs)
                    predictions = self.model(**inputs)[0].cpu()

-        return predictions.numpy()
+        if return_tensors:
+            return predictions
+        else:
+            return predictions.numpy()


 class FeatureExtractionPipeline(Pipeline):
@@ -491,6 +506,71 @@ class TextClassificationPipeline(Pipeline):
        return [{"label": self.model.config.id2label[item.argmax()], "score": item.max()} for item in scores]


+class FillMaskPipeline(Pipeline):
+    """
+    Masked language modeling prediction pipeline using ModelWithLMHead head.
+    """
+
+    def __init__(
+        self,
+        model,
+        tokenizer: PreTrainedTokenizer = None,
+        modelcard: ModelCard = None,
+        framework: Optional[str] = None,
+        args_parser: ArgumentHandler = None,
+        device: int = -1,
+        topk=5,
+    ):
+        super().__init__(
+            model=model,
+            tokenizer=tokenizer,
+            modelcard=modelcard,
+            framework=framework,
+            args_parser=args_parser,
+            device=device,
+            binary_output=True,
+        )
+
+        self.topk = topk
+
+    def __call__(self, *args, **kwargs):
+        inputs = self._parse_and_tokenize(*args, **kwargs)
+        outputs = self._forward(inputs, return_tensors=True)
+
+        results = []
+        batch_size = outputs.shape[0] if self.framework == "tf" else outputs.size(0)
+
+        for i in range(batch_size):
+            input_ids = inputs["input_ids"][i]
+            result = []
+
+            if self.framework == "tf":
+                masked_index = tf.where(input_ids == self.tokenizer.mask_token_id).numpy().item()
+                logits = outputs[i, masked_index, :]
+                probs = tf.nn.softmax(logits)
+                topk = tf.math.top_k(probs, k=self.topk)
+                values, predictions = topk.values.numpy(), topk.indices.numpy()
+            else:
+                masked_index = (input_ids == self.tokenizer.mask_token_id).nonzero().item()
+                logits = outputs[i, masked_index, :]
+                probs = logits.softmax(dim=0)
+                values, predictions = probs.topk(self.topk)
+
+            for v, p in zip(values.tolist(), predictions.tolist()):
+                tokens = input_ids.numpy()
+                tokens[masked_index] = p
+                # Filter padding out:
+                tokens = tokens[np.where(tokens != self.tokenizer.pad_token_id)]
+                result.append({"sequence": self.tokenizer.decode(tokens), "score": v, "token": p})
+
+            # Append
+            results += [result]
+
+        if len(results) == 1:
+            return results[0]
+        return results
+
+
 class NerPipeline(Pipeline):
    """
    Named Entity Recognition pipeline using ModelForTokenClassification head.
@@ -523,7 +603,8 @@ class NerPipeline(Pipeline):
        self.ignore_labels = ignore_labels

    def __call__(self, *texts, **kwargs):
-        inputs, answers = self._args_parser(*texts, **kwargs), []
+        inputs = self._args_parser(*texts, **kwargs)
+        answers = []
        for sentence in inputs:

            # Manage correct placement of the tensors
@@ -903,6 +984,16 @@ SUPPORTED_TASKS = {
            "tokenizer": "distilbert-base-uncased",
        },
    },
+    "fill-mask": {
+        "impl": FillMaskPipeline,
+        "tf": TFAutoModelWithLMHead if is_tf_available() else None,
+        "pt": AutoModelWithLMHead if is_torch_available() else None,
+        "default": {
+            "model": {"pt": "distilroberta-base", "tf": "distilroberta-base"},
+            "config": None,
+            "tokenizer": "distilroberta-base",
+        },
+    },
 }


@@ -73,7 +73,7 @@ TOKENIZER_MAPPING = OrderedDict(
 )


-class AutoTokenizer(object):
+class AutoTokenizer:
    r""":class:`~transformers.AutoTokenizer` is a generic tokenizer class
        that will be instantiated as one of the tokenizer classes of the library
        when created with the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)`
@@ -48,6 +48,7 @@ PRETRAINED_VOCAB_FILES_MAP = {
        "bert-base-german-dbmdz-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-vocab.txt",
        "bert-base-finnish-cased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-cased-v1/vocab.txt",
        "bert-base-finnish-uncased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-uncased-v1/vocab.txt",
+        "bert-base-dutch-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/wietsedv/bert-base-dutch-cased/vocab.txt",
    }
 }

@@ -69,6 +70,7 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "bert-base-german-dbmdz-uncased": 512,
    "bert-base-finnish-cased-v1": 512,
    "bert-base-finnish-uncased-v1": 512,
+    "bert-base-dutch-cased": 512,
 }

 PRETRAINED_INIT_CONFIGURATION = {
@@ -89,6 +91,7 @@ PRETRAINED_INIT_CONFIGURATION = {
    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
    "bert-base-finnish-cased-v1": {"do_lower_case": False},
    "bert-base-finnish-uncased-v1": {"do_lower_case": True},
+    "bert-base-dutch-cased": {"do_lower_case": False},
 }


@@ -40,6 +40,13 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "camembert-base": None,
 }

+SHARED_MODEL_IDENTIFIERS = [
+    # Load with
+    # `tokenizer = AutoTokenizer.from_pretrained("username/pretrained_model")`
+    "Musixmatch/umberto-commoncrawl-cased-v1",
+    "Musixmatch/umberto-wikipedia-uncased-v1",
+]
+

 class CamembertTokenizer(PreTrainedTokenizer):
    """
@@ -169,6 +176,24 @@ class CamembertTokenizer(PreTrainedTokenizer):
            return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset)

+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        return state
+
+    def __setstate__(self, d):
+        self.__dict__ = d
+        try:
+            import sentencepiece as spm
+        except ImportError:
+            logger.warning(
+                "You need to install SentencePiece to use AlbertTokenizer: https://github.com/google/sentencepiece"
+                "pip install sentencepiece"
+            )
+            raise
+        self.sp_model = spm.SentencePieceProcessor()
+        self.sp_model.Load(self.vocab_file)
+
    def convert_tokens_to_string(self, tokens):
        """Converts a sequence of tokens (strings for sub-words) in a single string."""
        out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
@@ -0,0 +1,145 @@
+# coding=utf-8
+# Copyright 2019-present CNRS, Facebook Inc. and the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes for Flaubert, based on XLM."""
+
+
+import logging
+import unicodedata
+
+import six
+
+from .tokenization_xlm import XLMTokenizer
+
+
+logger = logging.getLogger(__name__)
+
+VOCAB_FILES_NAMES = {
+    "vocab_file": "vocab.json",
+    "merges_file": "merges.txt",
+}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "flaubert-small-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_small_cased/vocab.json",
+        "flaubert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_base_uncased/vocab.json",
+        "flaubert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_base_cased/vocab.json",
+        "flaubert-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_large_cased/vocab.json",
+    },
+    "merges_file": {
+        "flaubert-small-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_small_cased/merges.txt",
+        "flaubert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_base_uncased/merges.txt",
+        "flaubert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_base_cased/merges.txt",
+        "flaubert-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_large_cased/merges.txt",
+    },
+}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "flaubert-small-cased": 512,
+    "flaubert-base-uncased": 512,
+    "flaubert-base-cased": 512,
+    "flaubert-large-cased": 512,
+}
+
+PRETRAINED_INIT_CONFIGURATION = {
+    "flaubert-small-cased": {"do_lowercase": False},
+    "flaubert-base-uncased": {"do_lowercase": True},
+    "flaubert-base-cased": {"do_lowercase": False},
+    "flaubert-large-cased": {"do_lowercase": False},
+}
+
+
+def convert_to_unicode(text):
+    """
+    Converts `text` to Unicode (if it's not already), assuming UTF-8 input.
+    """
+    # six_ensure_text is copied from https://github.com/benjaminp/six
+    def six_ensure_text(s, encoding="utf-8", errors="strict"):
+        if isinstance(s, six.binary_type):
+            return s.decode(encoding, errors)
+        elif isinstance(s, six.text_type):
+            return s
+        else:
+            raise TypeError("not expecting type '%s'" % type(s))
+
+    return six_ensure_text(text, encoding="utf-8", errors="ignore")
+
+
+class FlaubertTokenizer(XLMTokenizer):
+    """
+    BPE tokenizer for Flaubert
+
+        - Moses preprocessing & tokenization
+
+        - Normalize all inputs text
+
+        - argument ``special_tokens`` and function ``set_special_tokens``, can be used to add additional symbols \
+        (ex: "__classify__") to a vocabulary
+
+        - `do_lowercase` controle lower casing (automatically set for pretrained vocabularies)
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+
+    def __init__(self, do_lowercase=False, **kwargs):
+        super().__init__(**kwargs)
+        self.do_lowercase = do_lowercase
+        self.do_lowercase_and_remove_accent = False
+
+    def preprocess_text(self, text):
+        text = text.replace("``", '"').replace("''", '"')
+        text = convert_to_unicode(text)
+        text = unicodedata.normalize("NFC", text)
+
+        if self.do_lowercase:
+            text = text.lower()
+
+        return text
+
+    def _tokenize(self, text, bypass_tokenizer=False):
+        """
+        Tokenize a string given language code using Moses.
+
+        Details of tokenization:
+        - [sacremoses](https://github.com/alvations/sacremoses): port of Moses
+            - Install with `pip install sacremoses`
+
+        Args:
+            - bypass_tokenizer: Allow users to preprocess and tokenize the sentences externally (default = False)  (bool). If True, we only apply BPE.
+
+        Returns:
+            List of tokens.
+        """
+        lang = "fr"
+        if lang and self.lang2id and lang not in self.lang2id:
+            logger.error(
+                "Supplied language code not found in lang2id mapping. Please check that your language is supported by the loaded pretrained model."
+            )
+
+        if bypass_tokenizer:
+            text = text.split()
+        else:
+            text = self.preprocess_text(text)
+            text = self.moses_pipeline(text, lang=lang)
+            text = self.moses_tokenize(text, lang=lang)
+
+        split_tokens = []
+        for token in text:
+            if token:
+                split_tokens.extend([t for t in self.bpe(token).split(" ")])
+
+        return split_tokens
@@ -326,7 +326,7 @@ class PreTrainedTokenizer(object):
                cls.pretrained_init_configuration
                and pretrained_model_name_or_path in cls.pretrained_init_configuration
            ):
-                init_configuration = cls.pretrained_init_configuration[pretrained_model_name_or_path]
+                init_configuration = cls.pretrained_init_configuration[pretrained_model_name_or_path].copy()
        else:
            # Get the vocabulary from local files
            logger.info(
@@ -998,7 +998,8 @@ class PreTrainedTokenizer(object):
            for key, value in batch_outputs.items():

                padded_value = value
-                if key != "input_len":
+                # verify that the tokenizer has a pad_token_id
+                if key != "input_len" and self._pad_token is not None:
                    # Padding handle
                    padded_value = [
                        v + [self.pad_token_id if key == "input_ids" else 1] * (max_seq_len - len(v))
@@ -176,10 +176,7 @@ class XLMRobertaTokenizer(PreTrainedTokenizer):
    def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
        """
        Creates a mask from the two sequences passed to be used in a sequence-pair classification task.
-        A RoBERTa sequence pair mask has the following format:
-        0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
-        | first sequence    | second sequence
-
+        RoBERTa does not make use of token type ids, therefore a list of zeros is returned.
        if token_ids_1 is None, only returns the first portion of the mask (0's).
        """
        sep = [self.sep_token_id]
@@ -187,7 +184,7 @@ class XLMRobertaTokenizer(PreTrainedTokenizer):

        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):
@@ -333,7 +333,8 @@ def evaluate(args, model, tokenizer, prefix=""):

 def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False):
    if args.local_rank not in [-1, 0] and not evaluate:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
+        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset,
+        # and the others will use the cache

    # Load data features from cache or dataset file
    input_file = args.predict_file if evaluate else args.train_file
@@ -366,7 +367,8 @@ def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=Fal
            torch.save(features, cached_features_file)

    if args.local_rank == 0 and not evaluate:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
+        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset,
+        # and the others will use the cache

    # Convert to Tensors and build dataset
    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
@@ -620,7 +622,8 @@ def main():

    # Load pretrained model and tokenizer
    if args.local_rank not in [-1, 0]:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
+        torch.distributed.barrier()  # Make sure only the first process in distributed training will
+        # download model & vocab

    args.model_type = args.model_type.lower()
    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
@@ -641,15 +644,16 @@ def main():
    )

    if args.local_rank == 0:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
+        torch.distributed.barrier()  # Make sure only the first process in distributed training will
+        # download model & vocab

    model.to(args.device)

    logger.info("Training/evaluation parameters %s", args)

-    # Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum if args.fp16 is set.
-    # Otherwise it'll default to "promote" mode, and we'll get fp32 operations. Note that running `--fp16_opt_level="O2"` will
-    # remove the need for this code, but it is still valid.
+    # Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum
+    # if args.fp16 is set. Otherwise it'll default to "promote" mode, and we'll get fp32 operations.
+    # Note that running `--fp16_opt_level="O2"` will remove the need for this code, but it is still valid.
    if args.fp16:
        try:
            import apex
@@ -28,6 +28,8 @@ if is_torch_available():
        BertConfig,
        AutoModel,
        BertModel,
+        AutoModelForPreTraining,
+        BertForPreTraining,
        AutoModelWithLMHead,
        BertForMaskedLM,
        RobertaForMaskedLM,
@@ -56,6 +58,21 @@ class AutoModelTest(unittest.TestCase):
            for value in loading_info.values():
                self.assertEqual(len(value), 0)

+    @slow
+    def test_model_for_pretraining_from_pretrained(self):
+        logging.basicConfig(level=logging.INFO)
+        for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
+            config = AutoConfig.from_pretrained(model_name)
+            self.assertIsNotNone(config)
+            self.assertIsInstance(config, BertConfig)
+
+            model = AutoModelForPreTraining.from_pretrained(model_name)
+            model, loading_info = AutoModelForPreTraining.from_pretrained(model_name, output_loading_info=True)
+            self.assertIsNotNone(model)
+            self.assertIsInstance(model, BertForPreTraining)
+            for value in loading_info.values():
+                self.assertEqual(len(value), 0)
+
    @slow
    def test_lmhead_model_from_pretrained(self):
        logging.basicConfig(level=logging.INFO)
@@ -28,6 +28,8 @@ if is_tf_available():
        BertConfig,
        TFAutoModel,
        TFBertModel,
+        TFAutoModelForPreTraining,
+        TFBertForPreTraining,
        TFAutoModelWithLMHead,
        TFBertForMaskedLM,
        TFRobertaForMaskedLM,
@@ -57,6 +59,23 @@ class TFAutoModelTest(unittest.TestCase):
            self.assertIsNotNone(model)
            self.assertIsInstance(model, TFBertModel)

+    @slow
+    def test_model_for_pretraining_from_pretrained(self):
+        import h5py
+
+        self.assertTrue(h5py.version.hdf5_version.startswith("1.10"))
+
+        logging.basicConfig(level=logging.INFO)
+        # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
+        for model_name in ["bert-base-uncased"]:
+            config = AutoConfig.from_pretrained(model_name)
+            self.assertIsNotNone(config)
+            self.assertIsInstance(config, BertConfig)
+
+            model = TFAutoModelForPreTraining.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+            self.assertIsInstance(model, TFBertForPreTraining)
+
    @slow
    def test_lmhead_model_from_pretrained(self):
        logging.basicConfig(level=logging.INFO)
@@ -112,8 +112,15 @@ class TFModelTesterMixin:
            tfo = tf_model(inputs_dict, training=False)
            tf_hidden_states = tfo[0].numpy()
            pt_hidden_states = pto[0].numpy()
-            tf_hidden_states[np.isnan(tf_hidden_states)] = 0
-            pt_hidden_states[np.isnan(pt_hidden_states)] = 0
+
+            tf_nans = np.copy(np.isnan(tf_hidden_states))
+            pt_nans = np.copy(np.isnan(pt_hidden_states))
+
+            pt_hidden_states[tf_nans] = 0
+            tf_hidden_states[tf_nans] = 0
+            pt_hidden_states[pt_nans] = 0
+            tf_hidden_states[pt_nans] = 0
+
            max_diff = np.amax(np.abs(tf_hidden_states - pt_hidden_states))
            # Debug info (remove when fixed)
            if max_diff >= 2e-2:
@@ -144,8 +151,14 @@ class TFModelTesterMixin:
            tfo = tf_model(inputs_dict)
            tfo = tfo[0].numpy()
            pto = pto[0].numpy()
-            tfo[np.isnan(tfo)] = 0
-            pto[np.isnan(pto)] = 0
+            tf_nans = np.copy(np.isnan(tfo))
+            pt_nans = np.copy(np.isnan(pto))
+
+            pto[tf_nans] = 0
+            tfo[tf_nans] = 0
+            pto[pt_nans] = 0
+            tfo[pt_nans] = 0
+
            max_diff = np.amax(np.abs(tfo - pto))
            self.assertLessEqual(max_diff, 2e-2)

@@ -219,5 +219,5 @@ class TFDistilBertModelTest(TFModelTesterMixin, unittest.TestCase):
    # @slow
    # def test_model_from_pretrained(self):
    #     for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-    #         model = DistilBertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
+    #         model = DistilBertModesss.from_pretrained(model_name, cache_dir=CACHE_DIR)
    #         self.assertIsNotNone(model)
@@ -1,7 +1,8 @@
 import unittest
-from typing import Iterable
+from typing import Iterable, List, Optional

 from transformers import pipeline
+from transformers.pipelines import Pipeline

 from .utils import require_tf, require_torch

@@ -62,9 +63,25 @@ TEXT_CLASSIF_FINETUNED_MODELS = {
    )
 }

+FILL_MASK_FINETUNED_MODELS = {
+    ("distilroberta-base", "distilroberta-base", None),
+}
+
+TF_FILL_MASK_FINETUNED_MODELS = {
+    ("distilroberta-base", "distilroberta-base", None),
+}
+

 class MonoColumnInputTestCase(unittest.TestCase):
-    def _test_mono_column_pipeline(self, nlp, valid_inputs: list, invalid_inputs: list, output_keys: Iterable[str]):
+    def _test_mono_column_pipeline(
+        self,
+        nlp: Pipeline,
+        valid_inputs: List,
+        invalid_inputs: List,
+        output_keys: Iterable[str],
+        expected_multi_result: Optional[List] = None,
+        expected_check_keys: Optional[List[str]] = None,
+    ):
        self.assertIsNotNone(nlp)

        mono_result = nlp(valid_inputs[0])
@@ -81,6 +98,13 @@ class MonoColumnInputTestCase(unittest.TestCase):
        self.assertIsInstance(multi_result, list)
        self.assertIsInstance(multi_result[0], (dict, list))

+        if expected_multi_result is not None:
+            for result, expect in zip(multi_result, expected_multi_result):
+                for key in expected_check_keys or []:
+                    self.assertEqual(
+                        set([o[key] for o in result]), set([o[key] for o in expect]),
+                    )
+
        if isinstance(multi_result[0], list):
            multi_result = multi_result[0]

@@ -110,7 +134,7 @@ class MonoColumnInputTestCase(unittest.TestCase):

    @require_torch
    def test_sentiment_analysis(self):
-        mandatory_keys = {"label"}
+        mandatory_keys = {"label", "score"}
        valid_inputs = ["HuggingFace is solving NLP one commit at a time.", "HuggingFace is based in New-York & Paris"]
        invalid_inputs = [None]
        for tokenizer, model, config in TEXT_CLASSIF_FINETUNED_MODELS:
@@ -119,7 +143,7 @@ class MonoColumnInputTestCase(unittest.TestCase):

    @require_tf
    def test_tf_sentiment_analysis(self):
-        mandatory_keys = {"label"}
+        mandatory_keys = {"label", "score"}
        valid_inputs = ["HuggingFace is solving NLP one commit at a time.", "HuggingFace is based in New-York & Paris"]
        invalid_inputs = [None]
        for tokenizer, model, config in TF_TEXT_CLASSIF_FINETUNED_MODELS:
@@ -127,21 +151,87 @@ class MonoColumnInputTestCase(unittest.TestCase):
            self._test_mono_column_pipeline(nlp, valid_inputs, invalid_inputs, mandatory_keys)

    @require_torch
-    def test_features_extraction(self):
+    def test_feature_extraction(self):
        valid_inputs = ["HuggingFace is solving NLP one commit at a time.", "HuggingFace is based in New-York & Paris"]
        invalid_inputs = [None]
        for tokenizer, model, config in FEATURE_EXTRACT_FINETUNED_MODELS:
-            nlp = pipeline(task="sentiment-analysis", model=model, config=config, tokenizer=tokenizer)
+            nlp = pipeline(task="feature-extraction", model=model, config=config, tokenizer=tokenizer)
            self._test_mono_column_pipeline(nlp, valid_inputs, invalid_inputs, {})

    @require_tf
-    def test_tf_features_extraction(self):
+    def test_tf_feature_extraction(self):
        valid_inputs = ["HuggingFace is solving NLP one commit at a time.", "HuggingFace is based in New-York & Paris"]
        invalid_inputs = [None]
        for tokenizer, model, config in TF_FEATURE_EXTRACT_FINETUNED_MODELS:
-            nlp = pipeline(task="sentiment-analysis", model=model, config=config, tokenizer=tokenizer)
+            nlp = pipeline(task="feature-extraction", model=model, config=config, tokenizer=tokenizer)
            self._test_mono_column_pipeline(nlp, valid_inputs, invalid_inputs, {})

+    @require_torch
+    def test_fill_mask(self):
+        mandatory_keys = {"sequence", "score", "token"}
+        valid_inputs = [
+            "My name is <mask>",
+            "The largest city in France is <mask>",
+        ]
+        invalid_inputs = [None]
+        expected_multi_result = [
+            [
+                {"score": 0.008698059245944023, "sequence": "<s>My name is John</s>", "token": 610},
+                {"score": 0.007750614080578089, "sequence": "<s>My name is Chris</s>", "token": 1573},
+            ],
+            [
+                {"score": 0.2721288502216339, "sequence": "<s>The largest city in France is Paris</s>", "token": 2201},
+                {
+                    "score": 0.19764970242977142,
+                    "sequence": "<s>The largest city in France is Lyon</s>",
+                    "token": 12790,
+                },
+            ],
+        ]
+        for tokenizer, model, config in FILL_MASK_FINETUNED_MODELS:
+            nlp = pipeline(task="fill-mask", model=model, config=config, tokenizer=tokenizer, topk=2)
+            self._test_mono_column_pipeline(
+                nlp,
+                valid_inputs,
+                invalid_inputs,
+                mandatory_keys,
+                expected_multi_result=expected_multi_result,
+                expected_check_keys=["sequence"],
+            )
+
+    @require_tf
+    def test_tf_fill_mask(self):
+        mandatory_keys = {"sequence", "score", "token"}
+        valid_inputs = [
+            "My name is <mask>",
+            "The largest city in France is <mask>",
+        ]
+        invalid_inputs = [None]
+        expected_multi_result = [
+            [
+                {"score": 0.008698059245944023, "sequence": "<s>My name is John</s>", "token": 610},
+                {"score": 0.007750614080578089, "sequence": "<s>My name is Chris</s>", "token": 1573},
+            ],
+            [
+                {"score": 0.2721288502216339, "sequence": "<s>The largest city in France is Paris</s>", "token": 2201},
+                {
+                    "score": 0.19764970242977142,
+                    "sequence": "<s>The largest city in France is Lyon</s>",
+                    "token": 12790,
+                },
+            ],
+        ]
+        for tokenizer, model, config in TF_FILL_MASK_FINETUNED_MODELS:
+            nlp = pipeline(task="fill-mask", model=model, config=config, tokenizer=tokenizer, topk=2)
+            self._test_mono_column_pipeline(
+                nlp,
+                valid_inputs,
+                invalid_inputs,
+                mandatory_keys,
+                expected_multi_result=expected_multi_result,
+                expected_check_keys=["sequence"],
+            )
+

 class MultiColumnInputTestCase(unittest.TestCase):
    def _test_multicolumn_pipeline(self, nlp, valid_inputs: list, invalid_inputs: list, output_keys: Iterable[str]):
@@ -495,3 +495,16 @@ class TokenizerTesterMixin:
        assert [token_type_padding_idx] * padding_size + token_type_ids == padded_token_type_ids
        assert [0] * padding_size + attention_mask == padded_attention_mask
        assert [1] * padding_size + special_tokens_mask == padded_special_tokens_mask
+
+    def test_separate_tokenizers(self):
+        # This tests that tokenizers don't impact others. Unfortunately the case where it fails is when
+        # we're loading an S3 configuration from a pre-trained identifier, and we have no way of testing those today.
+
+        tokenizer = self.get_tokenizer(random_argument=True)
+        print(tokenizer.init_kwargs)
+        assert tokenizer.init_kwargs["random_argument"] is True
+        new_tokenizer = self.get_tokenizer(random_argument=False)
+        print(tokenizer.init_kwargs)
+        print(new_tokenizer.init_kwargs)
+        assert tokenizer.init_kwargs["random_argument"] is True
+        assert new_tokenizer.init_kwargs["random_argument"] is False
Author	SHA1	Message	Date
Lysandre	6664ea943d	Release: v2.4.0	2020-01-31 09:40:32 -05:00
Julien Chaumond	5a6b138b00	[Umberto] model shortcuts (#2661 ) * [Umberto] model shortcuts cc @loretoparisi @simonefrancia see #2485 * Ensure that tokenizers will be correctly configured	2020-01-30 21:05:53 -05:00
Julien Chaumond	7fe294bf07	Hotfix: same handling of non-existent files as for config	2020-01-30 20:05:04 -05:00
Julien Chaumond	b85c59f997	config.architectures	2020-01-30 19:26:59 -05:00
Julien Chaumond	f9bc3f5771	style tweak	2020-01-30 19:26:59 -05:00
Julien Chaumond	0b13fb822a	No need for a model_type here cc @lysandrejik	2020-01-30 19:26:59 -05:00
Jared Nielsen	71a382319f	Correct documentation	2020-01-30 18:41:24 -05:00
Lysandre	01a14ebd8d	Add FlauBERT to automodels	2020-01-30 18:40:22 -05:00
Julien Chaumond	9fa836a73f	fill_mask helper (#2576 ) * fill_mask helper * [poc] FillMaskPipeline * Revert "[poc] FillMaskPipeline" This reverts commit 67eeea55b0f97b46c2b828de0f4ee97d87338335. * Revert "fill_mask helper" This reverts commit cacc17b884e14bb6b07989110ffe884ad9e36eaa. * README: clarify that Pipelines can also do text-classification cf. question at the AI&ML meetup last week, @mfuntowicz * Fix test: test feature-extraction pipeline * Test tweaks * Slight refactor of existing pipeline (in preparation of new FillMaskPipeline) * Extraneous doc * More robust way of doing this @mfuntowicz as we don't rely on the model name anymore (see AutoConfig) * Also add RobertaConfig as a quickfix for wrong token_type_ids * cs * [BIG] FillMaskPipeline	2020-01-30 18:15:42 -05:00
Hang Le	b43cb09aaa	Add layerdrop	2020-01-30 12:05:01 -05:00
Lysandre	df27648bd9	Rename test_examples to test_doc_samples	2020-01-30 10:07:22 -05:00
Lysandre	93dccf527b	Pretrained models	2020-01-30 10:04:18 -05:00
Lysandre	90787fed81	Style	2020-01-30 10:04:18 -05:00
Lysandre	73306d028b	FlauBERT documentation	2020-01-30 10:04:18 -05:00
Lysandre	ce2f4227ab	Fix failing FlauBERT test	2020-01-30 10:04:18 -05:00
Hang Le	f0a4fc6cd6	Add Flaubert	2020-01-30 10:04:18 -05:00
Peter Izsak	a5381495e6	Added classifier dropout rate in ALBERT	2020-01-30 09:52:34 -05:00
Bram Vanroy	83446a88d9	Use _pad_token of pad_token_id Requesting pad_token_id would cause an error message when it is None. Use private _pad_token instead.	2020-01-29 17:44:58 -05:00
BramVanroy	9fde13a3ac	Add check to verify existence of pad_token_id In batch_encode_plus we have to ensure that the tokenizer has a pad_token_id so that, when padding, no None values are added as padding. That would happen with gpt2, openai, transfoxl. closes https://github.com/huggingface/transformers/issues/2640	2020-01-29 17:44:58 -05:00
Lysandre	e63a81dd25	Style	2020-01-29 16:29:20 -05:00
Lysandre	217349016a	Copy object instead of passing the reference	2020-01-29 16:15:39 -05:00
Jared Nielsen	adb8c93134	Remove lines causing a KeyError	2020-01-29 14:01:16 -05:00
Lysandre	c69b082601	Update documentation	2020-01-29 12:06:13 -05:00
Julien Plu	ca1d66734d	Apply quality and style requirements once again	2020-01-29 12:06:13 -05:00
Julien Plu	5e3c72842d	bugfix on model name	2020-01-29 12:06:13 -05:00
Julien Plu	0731fa1587	Apply quality and style requirements	2020-01-29 12:06:13 -05:00
Julien Plu	a3998e76ae	Add TF2 CamemBERT model	2020-01-29 12:06:13 -05:00
Lysandre	b5625f131d	Style	2020-01-29 11:47:49 -05:00
Lysandre	44a5b4bbe7	Update documentation	2020-01-29 11:47:49 -05:00
Julien Plu	7fc628d98e	Apply style	2020-01-29 11:47:49 -05:00
Julien Plu	64ca855617	Add TF2 XLM-RoBERTa model	2020-01-29 11:47:49 -05:00
BramVanroy	9d87eafd11	Streamlining - mostly stylistic streamlining - removed 'additional context' sections. They seem to be rarely used and might cause confusion. If more details are needed, users can add them to the 'details' section	2020-01-28 10:41:10 -05:00
BramVanroy	a3b3638f6f	phrasing	2020-01-28 10:41:10 -05:00
BramVanroy	c96ca70f25	Update ---new-benchmark.md	2020-01-28 10:41:10 -05:00
BramVanroy	7b5eda32bb	Update --new-model-addition.md Motivate users to @-tag authors of models to increase visibility and expand the community	2020-01-28 10:41:10 -05:00
BramVanroy	c63d91dd1c	Update bug-report.md - change references to pytorch-transformers to transformers - link to code formatting guidelines	2020-01-28 10:41:10 -05:00
BramVanroy	b2907cd06e	Update feature-request.md - add 'your contribution' section - add code formatting link to 'additional context'	2020-01-28 10:41:10 -05:00
BramVanroy	2fec88ee02	Update question-help.md Prefer that general questions are asked on Stack Overflow	2020-01-28 10:41:10 -05:00
BramVanroy	7e03d2bd7c	update migration guide Streamlines usages of pytorch-transformers and pytorch-pretrained-bert. Add link to the README for the migration guide.	2020-01-28 10:41:10 -05:00
Lysandre	335dd5e68a	Default save steps 50 to 500 in all scripts	2020-01-28 09:42:11 -05:00
Lysandre	ea2600bd5f	Absolute definitive HeisenDistilBug solve cc @julien-c @thomwolf	2020-01-27 21:58:36 -05:00
Wietse de Vries	5c3d441ee1	Fix formatting	2020-01-27 21:00:34 -05:00
Wietse de Vries	f5a236c3ca	Add Dutch pre-trained BERT model	2020-01-27 21:00:34 -05:00
Julien Chaumond	6b4c3ee234	[run_lm_finetuning] GPT2 tokenizer doesn't have a pad_token ping @lysandrejik	2020-01-27 20:14:02 -05:00
Julien Chaumond	79815bf666	[serving] Fix typo	2020-01-27 19:58:25 -05:00
Julien Chaumond	5004d5af42	[serving] Update dependencies	2020-01-27 19:58:00 -05:00
Lysandre	9ca21c838b	Style	2020-01-27 14:49:12 -05:00
thomwolf	e0849a66ac	adding in the doc	2020-01-27 14:27:07 -05:00
thomwolf	6b081f04e6	style and quality	2020-01-27 14:27:07 -05:00
thomwolf	0e31e06a75	Add AutoModelForPreTraining	2020-01-27 14:27:07 -05:00
Julien Chaumond	ea56d305be	make style	2020-01-27 12:13:32 -05:00
Malte Pietsch	d440e21f5b	add mapping of roberta for QA	2020-01-27 12:12:46 -05:00
Lysandre	875c4ae48f	Definitive HeisenDistilBug fix cc @julien-c @@thomwolf	2020-01-27 12:09:58 -05:00
Lysandre	f09f42d4d3	Input Embeddings should be assigned cc @julien-c	2020-01-27 11:46:00 -05:00
Maksym Del	bac51fba3a	Fix token_type_ids for XLM-R	2020-01-27 11:08:31 -05:00
Lysandre	babd41e7fa	Code quality	2020-01-24 17:06:55 -05:00
Lysandre	974d083c7b	Accurate model for configuration	2020-01-24 16:46:03 -05:00
Lysandre	983fef469c	AutoModels doc	2020-01-24 16:37:30 -05:00
Lysandre	009fcb0ec1	Configuration utils	2020-01-24 16:37:30 -05:00
Julien Chaumond	11b13e94a3	Add type to help my IDE out	2020-01-24 14:00:57 -05:00
VictorSanh	1ce3fb5cc7	update correct eval metrics (distilbert & co)	2020-01-24 11:45:22 -05:00
Nicholas Lourie	62f5804608	Update the doc string for T5WithLMHeadModel T5WithLMHeadModel's doc string claims that indices of -1 are ignored while computing the cross-entropy loss in the forward pass; however, indices of -1 throw an error while indices of -100 are ignored. This commit updates the doc string to be consistent with the class's behavior.	2020-01-24 10:28:20 -05:00
Lysandre	908230d261	Pickle CamemBERT tokenizer	2020-01-24 10:08:59 -05:00