4868a830db
* add simple multi gpu complet * add human_eval_multi_gpu * use copy strategy to distribute across gpu, to avoid padding * add doc string * update code style * use task id to arrange output * truncate input to avoid zero pad * Stop the copy mechanism * update style * restore copies to scale better in distributed mode * update style * replace human eval * Apply suggestions from code review 1. Tokenize all input at the same time 2. use attention_mask to get the input length 3. other small fixes Co-authored-by: Leandro von Werra <lvwerra@users.noreply.github.com> * correct typo and update docstring * update code style * remove num sample division constraint * remove max len calculation * use accelerator.gather once to speed up * use accelerate set_seed; update accelerate version * correct gather bug Co-authored-by: Leandro von Werra <lvwerra@users.noreply.github.com> |
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| .. | ||
| 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.