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+.
Running the examples requires PyTorch 1.3.1+ or TensorFlow 2.2+.
Here is the list of all our examples:
Here is the list of all our examples:
-**grouped by task** (all official examples work for multiple models)
-**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),
- 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 **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.
- 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 | ✅ | - | - | [](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)
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:
Execute the following steps in a new virtual environment:
```bash
```bash
...
@@ -45,6 +24,30 @@ pip install .
...
@@ -45,6 +24,30 @@ pip install .
pip install-r ./examples/requirements.txt
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 | ✅ | - | [](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)
