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* Added generic ONNX conversion script for PyTorch model. * WIP initial TF support. * TensorFlow/Keras ONNX export working. * Print framework version info * Add possibility to check the model is correctly loading on ONNX runtime. * Remove quantization option. * Specify ONNX opset version when exporting. * Formatting. * Remove unused imports. * Make functions more generally reusable from other part of the code. * isort happy. * flake happy * Export only feature-extraction for now * Correctly check inputs order / filter before export. * Removed task variable * Fix invalid args call in load_graph_from_args. * Fix invalid args call in convert. * Fix invalid args call in infer_shapes. * Raise exception and catch in caller function instead of exit. * Add 04-onnx-export.ipynb notebook * More WIP on the notebook * Remove unused imports * Simplify & remove unused constants. * Export with constant_folding in PyTorch * Let's try to put function args in the right order this time ... * Disable external_data_format temporary * ONNX notebook draft ready. * Updated notebooks charts + wording * Correct error while exporting last chart in notebook. * Adressing @LysandreJik comment. * Set ONNX opset to 11 as default value. * Set opset param mandatory * Added ONNX export unittests * Quality. * flake8 happy * Add keras2onnx dependency on extras["tf"] * Pin keras2onnx on github master to v1.6.5 * Second attempt. * Third attempt. * Use the right repo URL this time ... * Do the same for onnxconverter-common * Added keras2onnx and onnxconveter-common to 1.7.0 to supports TF2.2 * Correct commit hash. * Addressing PR review: Optimization are enabled by default. * Addressing PR review: small changes in the notebook * setup.py comment about keras2onnx versioning. |
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| .. | ||
| 01-training-tokenizers.ipynb | ||
| 02-transformers.ipynb | ||
| 03-pipelines.ipynb | ||
| 04-onnx-export.ipynb | ||
| README.md | ||
Transformers Notebooks
You can find here a list of the official notebooks provided by Hugging Face.
Also, we would like to list here interesting content created by the community. If you wrote some notebook(s) leveraging transformers and would like be listed here, please open a Pull Request and we'll review it so it can be included here.
Hugging Face's notebooks 🤗
| Notebook | Description | |
|---|---|---|
| Getting Started Tokenizers | How to train and use your very own tokenizer |  |
| Getting Started Transformers | How to easily start using transformers | |
| How to use Pipelines | Simple and efficient way to use State-of-the-Art models on downstream tasks through transformers | |
| How to train a language model | Highlight all the steps to effectively train Transformer model on custom data | |
| How to generate text | How to use different decoding methods for language generation with transformers | |
| How to export model to ONNX | Highlight how to export and run inference workloads through ONNX |