* Partial TF port for ESM model
* Add ESM-TF tests
* Add the various imports for TF-ESM
* TF weight conversion almost ready
* Stop ignoring the decoder weights in PT
* Add tests and lots of fixes
* fix-copies
* Fix imports, add model docs
* Add get_vocab() to tokenizer
* Fix vocab links for pretrained files
* Allow multiple inputs with a sep
* Use EOS as SEP token because ESM vocab lacks SEP
* Correctly return special tokens mask from ESM tokenizer
* make fixup
* Stop testing unsupported embedding resizing
* Handle TF bias correctly
* Skip all models with slow tokenizers in the token classification test
* Fixing the batch/unbatcher of pipelines to accomodate the `None` being
passed around.
* Fixing pipeline bug caused by slow tokenizer being different.
* Update src/transformers/models/esm/modeling_tf_esm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update src/transformers/models/esm/modeling_tf_esm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update src/transformers/models/esm/modeling_tf_esm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update set_input_embeddings and the copyright notices
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Rework pipeline tests
* Try to fix Flax tests
* Try to put it before
* Use a new decorator instead
* Remove ignore marker since it doesn't work
* Filter pipeline tests
* Woopsie
* Use the fitlered list
* Clean up and fake modif
* Remove init
* Revert fake modif
* Update pipeline word heuristic to work with whitespace in token offsets
This change checks for whitespace in the input string at either the
character preceding the token or in the first character of the token.
This works with tokenizers that return offsets excluding whitespace
between words or with offsets including whitespace.
fixes#18111
starting
* Use smaller model, ensure expected tokenization
* Re-run CI (please squash)
* Attention mask is important in the case of batching...
* Improve the fix.
* Making the sentence different enough that they exhibit different
predictions.
