When supplied by Keras deserialization, the config parameter to initializers
will be a dict. So intercept it and convert to PretrainedConfig object (and
store in instance attribute for get_config to get at it) before passing to the
actual initializer. To accomplish this, and repeat as little code as possible,
use a class decorator on TF*MainLayer classes.
* Rename and improve example
* Add test
* slightly faster test
* style
* This breaks remy prolly
* shorter test string
* no slow
* newdir structure
* New tree
* Style
* shorter
* docs
* clean
* Attempt future import
* more import hax
* add first copy past test to tf 2 generate
* add tf top_k_top_p_filter fn
* add generate function for TF
* add generate function for TF
* implemented generate for all models expect transfoXL
* implemented generate for all models expect transfoXL
* implemented generate for all models expect transfoXL
* make style
* change permission of test file to correct ones
* delete ipdb
* delete ipdb
* fix bug and finish simple gpt2 integration test
* clean test file
* clean test file
* make style
* make style
* make style
* make style
* change import style
* change import style
* make style
* make style
* add decorators
* add decorators
* fix tf ctrl bug dim => axis in TF
* make style
* make style
* refactored test file
* refactored test file
* take out test_torch_tf_conversion if nothing is defined
* take out test_torch_tf_conversion if nothing is defined
* remove useless files
* remove useless files
* fix conflicts
* fix conflicts
* fix conflicts
* fix conflicts
* fix conflicts
* solve conflicts
* solve conflicts
* fix conflicts
* fix conflicts
* merge conflicts
* delete ipdb
* exposed top_k_top_p_filtering fns
* delete weirdly created w! file
* add comment to test tf common modeling
* fix conflicts
* fix conflicts
* make style
* merge conflicts
* make style
* change tf.tensor.shape to shape_list(tensor)
* Pipeline doc initial commit
* pipeline abstraction
* Remove modelcard argument from pipeline
* Task-specific pipelines can be instantiated with no model or tokenizer
* All pipelines doc
* Create self-hosted.yml
* Update self-hosted.yml
* Update self-hosted.yml
* Update self-hosted.yml
* Update self-hosted.yml
* Update self-hosted.yml
* do not run slow tests, for now
* [ci] For comparison with circleci, let's also run CPU-tests
* [ci] reorganize
* clearer filenames
* [ci] Final tweaks before merging
* rm slow tests on circle ci
* Trigger CI
* On GPU this concurrency was way too high
* * Added support for Albert when fine-tuning for NER
* Added support for Albert in NER pipeline
* Added command-line options to examples/ner/run_ner.py to better control tokenization
* Added class AlbertForTokenClassification
* Changed output for NerPipeline to use .convert_ids_to_tokens(...) instead of .decode(...) to better reflect tokens
* Added ,
* Now passes style guide enforcement
* Changes from reviews.
* Code now passes style enforcement
* Added test for AlbertForTokenClassification
* Added test for AlbertForTokenClassification
* Usage: Sequence Classification & Question Answering
* Pipeline example
* Language modeling
* TensorFlow code for Sequence classification
* Custom TF/PT toggler in docs
* QA + LM for TensorFlow
* Finish Usage for both PyTorch and TensorFlow
* Addressing Julien's comments
* More assertive
* cleanup
* Favicon
- added favicon option in conf.py along with the favicon image
- udpated 🤗 logo. slightly smaller and should appear more consistent across editing programs (no more tongue on the outside of the mouth)
Co-authored-by: joshchagani <joshua@joshuachagani.com>
