d3bd9ac728
* begin do_init * add params_shape_tree * raise error if params are accessed when do_init is False * don't allow do_init=False when keys are missing * make shape tree a property * assign self._params at the end * add test for do_init * add do_init arg to all flax models * fix param setting * disbale do_init for composite models * update test * add do_init in FlaxBigBirdForMultipleChoice * better names and errors * improve test * style * add a warning when do_init=False * remove extra if * set params after _required_params * add test for from_pretrained * do_init => _do_init * chage warning to info * fix typo * add params in init_weights * add params to gpt neo init * add params to init_weights * update do_init test * Trigger CI * Apply suggestions from code review Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * update template * trigger CI * style * style * fix template Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> |
||
|---|---|---|
| .. | ||
| flax | ||
| legacy | ||
| pytorch | ||
| research_projects | ||
| tensorflow | ||
| README.md | ||
Examples
We host a wide range of example scripts for multiple learning frameworks. Simply choose your favorite: TensorFlow, PyTorch or JAX/Flax.
We also have some research projects, as well as some legacy examples. Note that unlike the main examples these are not actively maintained, and may require specific older versions of dependencies in order to run.
While we strive to present as many use cases as possible, the example scripts are just that - examples. It is expected that they won't work out-of-the box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs. To help you with that, most of the examples fully expose the preprocessing of the data, allowing you to tweak and edit them as required.
Please discuss on the forum or in an issue a feature you would like to implement in an example before submitting a PR; we welcome bug fixes, but since we want to keep the examples as simple as possible it's unlikely that we will merge a pull request adding more functionality at the cost of readability.
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 do this, execute the following steps in a new virtual environment:
git clone https://github.com/huggingface/transformers
cd transformers
pip install .
Then cd in the example folder of your choice and run
pip install -r requirements.txt
To browse the examples corresponding to released versions of 🤗 Transformers, click on the line below and then on your desired version of the library:
Examples for older versions of 🤗 Transformers
Alternatively, you can switch your cloned 🤗 Transformers to a specific version (for instance with v3.5.1) with
git checkout tags/v3.5.1
and run the example command as usual afterward.