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@@ -223,35 +223,17 @@ The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/sta
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This repository is tested on Python 3.6+, Flax 0.3.2+, PyTorch 1.3.1+ and TensorFlow 2.3+.
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-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/).
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-
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-First, create a virtual environment with the version of Python you're going to use and activate it.
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-
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-Then, you will need to install at least one of Flax, PyTorch or TensorFlow.
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+you will need to install at least one of Flax, PyTorch or TensorFlow.
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Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/), [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) and/or [Flax](https://github.com/google/flax#quick-install) and [Jax](https://github.com/google/jax#installation) installation pages regarding the specific install command for your platform.
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When one of those backends has been installed, 🤗 Transformers can be installed using pip as follows:
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```bash
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-pip install transformers
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-```
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-
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-If you'd like to play with the examples or need the bleeding edge of the code and can't wait for a new release, you must [install the library from source](https://huggingface.co/docs/transformers/installation#installing-from-source).
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-
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-### With conda
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+pip install transformers==2.2.0
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-Since Transformers version v4.0.0, we now have a conda channel: `huggingface`.
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-
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-🤗 Transformers can be installed using conda as follows:
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-
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-```shell script
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-conda install -c huggingface transformers
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+# conda install -c huggingface transformers
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```
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-Follow the installation pages of Flax, PyTorch or TensorFlow to see how to install them with conda.
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-
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-> **_NOTE:_** On Windows, you may be prompted to activate Developer Mode in order to benefit from caching. If this is not an option for you, please let us know in [this issue](https://github.com/huggingface/huggingface_hub/issues/1062).
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-
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## Model architectures
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**[All the model checkpoints](https://huggingface.co/models)** provided by 🤗 Transformers are seamlessly integrated from the huggingface.co [model hub](https://huggingface.co) where they are uploaded directly by [users](https://huggingface.co/users) and [organizations](https://huggingface.co/organizations).
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@@ -425,20 +407,3 @@ These implementations have been tested on several datasets (see the example scri
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| [Quick tour: Fine-tuning/usage scripts](https://github.com/huggingface/transformers/tree/main/examples) | Example scripts for fine-tuning models on a wide range of tasks |
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| [Model sharing and uploading](https://huggingface.co/docs/transformers/model_sharing) | Upload and share your fine-tuned models with the community |
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| [Migration](https://huggingface.co/docs/transformers/migration) | Migrate to 🤗 Transformers from `pytorch-transformers` or `pytorch-pretrained-bert` |
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-
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-## Citation
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-
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-We now have a [paper](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) you can cite for the 🤗 Transformers library:
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-```bibtex
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-@inproceedings{wolf-etal-2020-transformers,
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- title = "Transformers: State-of-the-Art Natural Language Processing",
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- author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
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- booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
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- month = oct,
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- year = "2020",
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- address = "Online",
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- publisher = "Association for Computational Linguistics",
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- url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
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- pages = "38--45"
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-}
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-```
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