transformers/templates/adding_a_new_model

Using cookiecutter to generate models

This folder contains templates to generate new models that fit the current API and pass all tests. It generates models in both PyTorch, TensorFlow, and Flax and completes the __init__.py and auto-modeling files, and creates the documentation.

Usage

Using the cookiecutter utility requires to have all the dev dependencies installed. Let's first clone the repository and install it in our environment:

git clone https://github.com/huggingface/transformers
cd transformers
pip install -e ".[dev]"

Once the installation is done, you can use the CLI command add-new-model to generate your models:

transformers-cli add-new-model

This should launch the cookiecutter package which should prompt you to fill in the configuration.

The modelname should be cased according to the plain text casing, i.e., BERT, RoBERTa, DeBERTa.

modelname [<ModelNAME>]:
uppercase_modelname [<MODEL_NAME>]: 
lowercase_modelname [<model_name>]: 
camelcase_modelname [<ModelName>]: 

Fill in the authors with your team members:

authors [The HuggingFace Team]: 

The checkpoint identifier is the checkpoint that will be used in the examples across the files. Put the name you wish, as it will appear on the modelhub. Do not forget to include the organisation.

checkpoint_identifier [organisation/<model_name>-base-cased]: 

The tokenizer should either be based on BERT if it behaves exactly like the BERT tokenizer, or a standalone otherwise.

Select tokenizer_type:
1 - Based on BERT
2 - Standalone
Choose from 1, 2 [1]: 

Once the command has finished, you should have a total of 7 new files spread across the repository:

docs/source/model_doc/<model_name>.rst
src/transformers/models/<model_name>/configuration_<model_name>.py
src/transformers/models/<model_name>/modeling_<model_name>.py
src/transformers/models/<model_name>/modeling_tf_<model_name>.py
src/transformers/models/<model_name>/tokenization_<model_name>.py
tests/test_modeling_<model_name>.py
tests/test_modeling_tf_<model_name>.py

You can run the tests to ensure that they all pass:

python -m pytest ./tests/test_*<model_name>*.py

Feel free to modify each file to mimic the behavior of your model.

⚠ You should be careful about the classes preceded by the following line:️

# Copied from transformers.[...]

This line ensures that the copy does not diverge from the source. If it should diverge, because the implementation is different, this line needs to be deleted. If you don't delete this line and run make fix-copies, your changes will be overwritten.

Once you have edited the files to fit your architecture, simply re-run the tests (and edit them if a change is needed!) afterwards to make sure everything works as expected.

Once the files are generated and you are happy with your changes, here's a checklist to ensure that your contribution will be merged quickly:

• You should run the make fixup utility to fix the style of the files and to ensure the code quality meets the library's standards.
• You should complete the documentation file (docs/source/model_doc/<model_name>.rst) so that your model may be usable.