* Add initial files for depth estimation pipelines
* Add test file for depth estimation pipeline
* Update model mapping names
* Add updates for depth estimation output
* Add generic test
* Hopefully fixing the tests.
* Check if test passes
* Add make fixup and make fix-copies changes after rebase with main
* Rebase with main
* Fixing up depth pipeline.
* This is not used anymore.
* Fixing the test. `Image` is a module `Image.Image` is the type.
* Update docs/source/en/main_classes/pipelines.mdx
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* fixed typo for SQuAD
* Fixed the preprocess_validation_function function for the labels to reflect the remaining truncated instances
* Rolled back the trainer_seq2seq_qa.py for UnboundLocalError: local variable 'metrics' referenced before assignment
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* First draft
* Fix more things
* Improve more things
* Remove some head models
* Fix more things
* Add missing layers
* Remove tokenizer
* Fix more things
* Fix copied from statements
* Make all tests pass
* Remove print statements
* Remove files
* Fix README and docs
* Add integration test and fix organization
* Add tips
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Make tests faster, improve docs
* Fix doc tests
* Add model to toctree
* Add docs
* Add note about creating new checkpoint
* Remove is_decoder
* Make tests smaller, add docs
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Generate config on the file
* Fake modif for all test launch
* Upload more artifacts
* Typo and quality
* Try converting th yml to txt
* Leave my long lines alone yaml
* Debug prints
* Debug prints v2
* Try without sorting
* Was it really working before?
* Typo
* Use a parameter
* Use a parameter?
* Typo
* Here is some JSON
* Another try
* Learning to read...
* Check default is used
* Does this work?
* With continuation
* WiP
* Use a parameter for test list
* Other fake modif
* With the comma
* Name the test step so it doesn't blow up
* Just one example modification
* Final steps
* Add nightlies
* Move config generator
* Add trigger for nightlies
* Better workflow
* Rebase on recent changes
* Fix config creation
* Fake modif in an example
* Now fake modif in one config file
* Fix install step in custom tokenizers test
* Fix generated config
* Better fix hopefully
* Finally test modif in setup
* final cleanup
* implemented TFCvtModel and TFCvtForImageClassification and modified relevant files, added an exception in convert_tf_weight_name_to_pt_weight_name, added quick testing file to compare with pytorch model
* added docstring + testing file in transformers testing suite
* added test in testing file, modified docs to pass repo-consistency, passed formatting test
* refactoring + passing all test
* small refacto, removing unwanted comments
* improved testing config
* corrected import error
* modified acces to pretrained model archive list, to pass tf_test
* corrected import structure in init files
* modified testing for keras_fit with cpu
* correcting PR issues + Refactoring
* Refactoring : improving readability and reducing the number of permutations
* corrected momentum value + cls_token initialization
* removed from_pt as weights were added to the hub
* Update tests/models/cvt/test_modeling_tf_cvt.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* added test
* correct embedding init
* some changes in blenderbot (incomplete)
* update blenderbot (diff to be used as reference)
* update blenderbot_small
* update LED
* update marian
* update T5 and remove TFWrappedEmbeddings
* nullcontext() -> ContextManagers()
* fix embedding init
* fix device mismatch
* make fixup
* added slow tests
- added slow tests on `bnb` models to make sure generate works correctly
* replace with `self.device`
* revert force device assign
* Update src/transformers/generation_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* set the warning in `generate` instead of `sample`
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* decouples xlm_prophet from prophet and adds copy patterns that pass the copy check
* adds copy patterns to copied docstrings too
* restores autodoc for XLMProphetNetModel
* removes all-casing in a bunch of places to ensure that the model is compatible with all checkpoints on the hub
* adds missing model to main init
* adds autodocs to make document checker happy
* adds missing pretrained model import
* adds missing pretrained model import to main init
* adds XLMProphetNetPreTrainedModel to the dummy pt objects
* removes examples from the source-doc file since docstrings contain them already
* adds a missing new line to make check_repo happy
* cast positions dtype in XGLMModel
* Get the correct dtype at init time
* Get the correct dtype at init time
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* update feature extractor params
* update attention mask handling
* fix doc and pipeline test
* add warning when skipping test
* add whisper translation and transcription test
* fix build doc test
* Fixed a non-working hyperlink in the README.md file
The hyperlink to the community notebooks was outdated.
* Fixing missing double slash in hyperlink
The momentum value for PyTorch and TensorFlow batch normalization layers is not equivalent. The TensorFlow value should be (1 - pytorch_momentum) in order to ensure the correct updates are applied to the running mean and running variance calculations. We wouldn't observe a difference loading a pretrained model and performing inference, but evaluation outputs would change after some training steps.
