huggingface · elk-cloner · Mar 6, 2021 · Mar 6, 2021 · Mar 13, 2021 · Mar 20, 2021
diff --git a/Makefile b/Makefile
@@ -1,5 +1,7 @@
 .PHONY: deps_table_update modified_only_fixup extra_quality_checks quality style fixup fix-copies test test-examples docs
 
+# make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!)
+export PYTHONPATH = src
 
 check_dirs := examples tests src utils
 

diff --git a/README.md b/README.md
@@ -38,14 +38,14 @@ limitations under the License.
 </p>
 
 <h3 align="center">
-<p>State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0
+<p>State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow
 </h3>
 
 🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation, etc in 100+ languages. Its aim is to make cutting-edge NLP easier to use for everyone.
 
 🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets then share them with the community on our [model hub](https://huggingface.co/models). At the same time, each python module defining an architecture can be used as a standalone and modified to enable quick research experiments.
 
-🤗 Transformers is backed by the two most popular deep learning libraries, [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/), with a seamless integration between them, allowing you to train your models with one then load it for inference with the other.
+🤗 Transformers is backed by the three most popular deep learning libraries, [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/), with a seamless integration between them, allowing you to train your models with one then load it for inference with the other.
 
 ## Online demos
 
@@ -152,16 +152,16 @@ The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/sta
 
 ### With pip
 
-This repository is tested on Python 3.6+, PyTorch 1.0.0+ (PyTorch 1.3.1+ for [examples](https://github.com/huggingface/transformers/tree/master/examples)) and TensorFlow 2.0.
+This repository is tested on Python 3.6+, Flax 0.3.2+, PyTorch 1.3.1+ and TensorFlow 2.3+.
 
 You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
 
 First, create a virtual environment with the version of Python you're going to use and activate it.
 
-Then, you will need to install at least one of TensorFlow 2.0, PyTorch or Flax.
-Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available), [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform and/or [Flax installation page](https://github.com/google/flax#quick-install).
+Then, you will need to install at least one of Flax, PyTorch or TensorFlow.
+Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/), [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform and/or [Flax installation page](https://github.com/google/flax#quick-install).
 
-When TensorFlow 2.0 and/or PyTorch has been installed, 🤗 Transformers can be installed using pip as follows:
+When one of those backends has been installed, 🤗 Transformers can be installed using pip as follows:
 
 ```bash
 pip install transformers
@@ -179,7 +179,7 @@ Since Transformers version v4.0.0, we now have a conda channel: `huggingface`.
 conda install -c huggingface transformers
 ```
 
-Follow the installation pages of TensorFlow, PyTorch or Flax to see how to install them with conda.
+Follow the installation pages of Flax, PyTorch or TensorFlow to see how to install them with conda.
 
 ## Models architectures
 
@@ -247,7 +247,7 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 1. **[XLSR-Wav2Vec2](https://huggingface.co/transformers/model_doc/xlsr_wav2vec2.html)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
 1. 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.
 
-To check if each model has an implementation in PyTorch/TensorFlow/Flax or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/transformers/index.html#bigtable)
+To check if each model has an implementation in Flax, PyTorch or TensorFlow, or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/transformers/index.html#bigtable)
 
 These implementations have been tested on several datasets (see the example scripts) and should match the performances of the original implementations. You can find more details on the performances in the Examples section of the [documentation](https://huggingface.co/transformers/examples.html).
 

diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -1,12 +1,12 @@
 Transformers
 =======================================================================================================================
 
-State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
+State-of-the-art Natural Language Processing for Jax, Pytorch and TensorFlow
 
 🤗 Transformers (formerly known as `pytorch-transformers` and `pytorch-pretrained-bert`) provides general-purpose
 architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural Language Understanding (NLU) and Natural
-Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between
-TensorFlow 2.0 and PyTorch.
+Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between Jax,
+PyTorch and TensorFlow.
 
 This is the documentation of our repository `transformers <https://github.com/huggingface/transformers>`_.
 
@@ -43,11 +43,11 @@ Lower compute costs, smaller carbon footprint:
 Choose the right framework for every part of a model's lifetime:
 
 - Train state-of-the-art models in 3 lines of code
-- Deep interoperability between TensorFlow 2.0 and PyTorch models
-- Move a single model between TF2.0/PyTorch frameworks at will
+- Deep interoperability between Jax, Pytorch and TensorFlow models
+- Move a single model between Jax/PyTorch/TensorFlow frameworks at will
 - Seamlessly pick the right framework for training, evaluation, production
 
-Experimental support for Flax with a few models right now, expected to grow in the coming months.
+The support for Jax is still experimental (with a few models right now), expect to see it grow in the coming months!
 
 `All the model checkpoints <https://huggingface.co/models>`__ are seamlessly integrated from the huggingface.co `model
 hub <https://huggingface.co>`__ where they are uploaded directly by `users <https://huggingface.co/users>`__ and
@@ -74,8 +74,8 @@ The documentation is organized in five parts:
     - **MODELS** for the classes and functions related to each model implemented in the library.
     - **INTERNAL HELPERS** for the classes and functions we use internally.
 
-The library currently contains PyTorch, Tensorflow and Flax implementations, pretrained model weights, usage scripts
-and conversion utilities for the following models:
+The library currently contains Jax, PyTorch and Tensorflow implementations, pretrained model weights, usage scripts and
+conversion utilities for the following models:
 
 ..
     This list is updated automatically from the README with `make fix-copies`. Do not update manually!
@@ -251,8 +251,8 @@ and conversion utilities for the following models:
 .. _bigtable:
 
 The table below represents the current support in the library for each of those models, whether they have a Python
-tokenizer (called "slow"). A "fast" tokenizer backed by the 🤗 Tokenizers library, whether they have support in PyTorch,
-TensorFlow and/or Flax.
+tokenizer (called "slow"). A "fast" tokenizer backed by the 🤗 Tokenizers library, whether they have support in Jax (via
+Flax), PyTorch, and/or TensorFlow.
 
 ..
     This table is updated automatically from the auto modules with `make fix-copies`. Do not update manually!

diff --git a/docs/source/installation.md b/docs/source/installation.md
@@ -149,12 +149,6 @@ So if you don't have any specific environment variable set, the cache directory
 (``PYTORCH_TRANSFORMERS_CACHE`` or ``PYTORCH_PRETRAINED_BERT_CACHE``), those will be used if there is no shell
 environment variable for ``TRANSFORMERS_CACHE``.
 
-### Note on model downloads (Continuous Integration or large-scale deployments)
-
-If you expect to be downloading large volumes of models (more than 10,000) from huggingface.co (for instance through
-your CI setup, or a large-scale production deployment), please cache the model files on your end. It will be way
-faster, and cheaper. Feel free to contact us privately, we'd love to help with this.
-
 ### Offline mode
 
 It's possible to run 🤗 Transformers in a firewalled or a no-network environment.

diff --git a/docs/source/main_classes/output.rst b/docs/source/main_classes/output.rst
@@ -13,8 +13,8 @@
 Model outputs
 -----------------------------------------------------------------------------------------------------------------------
 
-PyTorch models have outputs that are instances of subclasses of :class:`~transformers.file_utils.ModelOutput`. Those
-are data structures containing all the information returned by the model, but that can also be used as tuples or
+All models have outputs that are instances of subclasses of :class:`~transformers.file_utils.ModelOutput`. Those are
+data structures containing all the information returned by the model, but that can also be used as tuples or
 dictionaries.
 
 Let's see of this looks on an example: