48bf7e47a0
* Add some nicety flags for better controlling evaluation. * Fix dependency issue with outdated requirement * Add additional flag to example to ensure eval is done * Wrap code into main function for accelerate launcher to find * Fix valid batch size flag in readme * Add note to install git-lfs when initializing/training the model * Update examples/research_projects/codeparrot/scripts/arguments.py Co-authored-by: Leandro von Werra <lvwerra@users.noreply.github.com> * Update examples/research_projects/codeparrot/README.md Co-authored-by: Leandro von Werra <lvwerra@users.noreply.github.com> * Revert "Wrap code into main function for accelerate launcher to find" This reverts commit ff11df1c810d4df198d04b827538eb4572147ba3. * Fix formatting issue * Move git-lfs instructions to installation section * Add a quick check before code generation for code evaluation * Fix styling issue * Update examples/research_projects/codeparrot/scripts/human_eval.py Co-authored-by: Leandro von Werra <lvwerra@users.noreply.github.com> * Make iterable dataset use passed in tokenizer rather than globally defined one Co-authored-by: Leandro von Werra <lvwerra@users.noreply.github.com> Co-authored-by: ncoop57 <nac33@students.uwf.edu> |
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| onnx/pytorch/summarization | ||
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| README.md | ||
Examples
This folder contains actively maintained examples of use of 🤗 Transformers organized along NLP tasks. If you are looking for an example that used to be in this folder, it may have moved to the corresponding framework subfolder (pytorch, tensorflow or flax), our research projects subfolder (which contains frozen snapshots of research projects) or to the legacy subfolder.
While we strive to present as many use cases as possible, the scripts in this folder are just 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. This way, you can easily tweak them.
This is similar if you want the scripts to report another metric than the one they currently use: look at the compute_metrics function inside the script. It takes the full arrays of predictions and labels and has to return a dictionary of string keys and float values. Just change it to add (or replace) your own metric to the ones already reported.
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 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.