Looking at the current community notebooks, it seems that few are targeted for absolute beginners and even fewer are written with TensorFlow. This notebook describes absolutely everything a beginner would need to know, including how to save/load their model and use it for new predictions (this is often omitted in tutorials)
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* t5 t5 community notebook added
* author link updated
* t5 t5 community notebook added
* author link updated
* new colab link updated
Co-authored-by: harris <muhammad.harris@visionx.io>
* added multilabel classification using distilbert notebook to community notebooks
* added multilabel classification using distilbert notebook to community notebooks
* remove references to old API in docstring - update data processors
* style
* fix tests - better type checking error messages
* better type checking
* include awesome fix by @LysandreJik for #5310
* updated doc and examples
* Added links to more community notebooks
Added links to 3 more community notebooks from the git repo: https://github.com/abhimishra91/transformers-tutorials
Different Transformers models are fine tuned on Dataset using PyTorch
* Update README.md
* Update README.md
* Update README.md
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Adding optimizations block from ONNXRuntime.
* Turn off external data format by default for PyTorch export.
* Correct the way use_external_format is passed through the cmdline args.
* 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.
I found there are two grammar errors or typo issues in the explanation of the encoding properties.
The original sentences:
If your was made of multiple \"parts\" such as (question, context), then this would be a vector with for each token the segment it belongs to
If your has been truncated into multiple subparts because of a length limit (for BERT for example the sequence length is limited to 512), this will contain all the remaining overflowing parts.
I think "input" should be inserted after the phrase "If your".
