* 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".
For the tutorial of "How to generate text", the URL link was wrong (it was linked to the tutorial of "How to train a language model").
I fixed the URL.