From 41cc5f3f596747ec72da9d3e034a00dd7c250ee8 Mon Sep 17 00:00:00 2001 From: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Date: Tue, 27 Oct 2020 17:32:20 -0400 Subject: [PATCH] Move installation instructions to the top (#8106) --- examples/README.md | 51 ++++++++++++++++++++++++---------------------- 1 file changed, 27 insertions(+), 24 deletions(-) diff --git a/examples/README.md b/examples/README.md index d38e72afb9..6afd32de86 100644 --- a/examples/README.md +++ b/examples/README.md @@ -1,41 +1,20 @@ # Examples -Version 2.9 of 🤗 Transformers introduces a new [`Trainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py) class for PyTorch, and its equivalent [`TFTrainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer_tf.py) for TF 2. +Version 2.9 of 🤗 Transformers introduced a new [`Trainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py) class for PyTorch, and its equivalent [`TFTrainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer_tf.py) for TF 2. Running the examples requires PyTorch 1.3.1+ or TensorFlow 2.2+. Here is the list of all our examples: - **grouped by task** (all official examples work for multiple models) - with information on whether they are **built on top of `Trainer`/`TFTrainer`** (if not, they still work, they might just lack some features), -- whether they also include examples for **`pytorch-lightning`**, which is a great fully-featured, general-purpose training library for PyTorch, - links to **Colab notebooks** to walk through the scripts and run them easily, - links to **Cloud deployments** to be able to deploy large-scale trainings in the Cloud with little to no setup. -This is still a work-in-progress – in particular documentation is still sparse – so please **contribute improvements/pull requests.** - - -## The Big Table of Tasks - -| Task | Example datasets | Trainer support | TFTrainer support | pytorch-lightning | Colab -|---|---|:---:|:---:|:---:|:---:| -| [**`language-modeling`**](https://github.com/huggingface/transformers/tree/master/examples/language-modeling) | Raw text | ✅ | - | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb) -| [**`text-classification`**](https://github.com/huggingface/transformers/tree/master/examples/text-classification) | GLUE, XNLI | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/trainer/01_text_classification.ipynb) -| [**`token-classification`**](https://github.com/huggingface/transformers/tree/master/examples/token-classification) | CoNLL NER | ✅ | ✅ | ✅ | - -| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/master/examples/multiple-choice) | SWAG, RACE, ARC | ✅ | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb) -| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/question-answering) | SQuAD | ✅ | ✅ | - | - -| [**`text-generation`**](https://github.com/huggingface/transformers/tree/master/examples/text-generation) | - | n/a | n/a | n/a | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb) -| [**`distillation`**](https://github.com/huggingface/transformers/tree/master/examples/distillation) | All | - | - | - | - -| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq) | CNN/Daily Mail | ✅ | - | ✅ | - -| [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq) | WMT | ✅ | - | ✅ | - -| [**`bertology`**](https://github.com/huggingface/transformers/tree/master/examples/bertology) | - | - | - | - | - -| [**`adversarial`**](https://github.com/huggingface/transformers/tree/master/examples/adversarial) | HANS | ✅ | - | - | - - - -
## Important note **Important** -To make sure you can successfully run the latest versions of the example scripts, you have to install the library from source and install some example-specific requirements. + +To make sure you can successfully run the latest versions of the example scripts, you have to **install the library from source** and install some example-specific requirements. Execute the following steps in a new virtual environment: ```bash @@ -45,6 +24,30 @@ pip install . pip install -r ./examples/requirements.txt ``` +Alternatively, you can run the version of the examples as they were for your current version of Transformers via (for instance with v3.4.0): +```bash +git checkout tags/v3.4.0 +``` + +## The Big Table of Tasks + +| Task | Example datasets | Trainer support | TFTrainer support | Colab +|---|---|:---:|:---:|:---:|:---:| +| [**`language-modeling`**](https://github.com/huggingface/transformers/tree/master/examples/language-modeling) | Raw text | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb) +| [**`text-classification`**](https://github.com/huggingface/transformers/tree/master/examples/text-classification) | GLUE, XNLI | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/trainer/01_text_classification.ipynb) +| [**`token-classification`**](https://github.com/huggingface/transformers/tree/master/examples/token-classification) | CoNLL NER | ✅ | ✅ | - +| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/master/examples/multiple-choice) | SWAG, RACE, ARC | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb) +| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/question-answering) | SQuAD | ✅ | ✅ | - +| [**`text-generation`**](https://github.com/huggingface/transformers/tree/master/examples/text-generation) | - | n/a | n/a | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb) +| [**`distillation`**](https://github.com/huggingface/transformers/tree/master/examples/distillation) | All | - | - | - +| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq) | CNN/Daily Mail | ✅ | - | - +| [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq) | WMT | ✅ | - | | - +| [**`bertology`**](https://github.com/huggingface/transformers/tree/master/examples/bertology) | - | - | - | - +| [**`adversarial`**](https://github.com/huggingface/transformers/tree/master/examples/adversarial) | HANS | ✅ | - | - + + +
+ ## One-click Deploy to Cloud (wip) **Coming soon!**