GitLab

* Start cleaning examples

* Fixup

* add bad words list

* make style

* add bad_words_tokens

* make style

* better naming

* make style

* fix typo

* fix conflicts

* add model size argument to summarization

* correct wrong import

* fix isort

* correct imports

* other isort make style

* make style

- Show that the last uploaded version was trained on more data (custom_license files)

Fix typo

* Create README.md

* Update README.md

* feat: add model card bert-imdb

* feat: add model card gpt2-imdb-pos

* feat: add model card gpt2-imdb

* Using loaded checkpoint with --do_predict

Without this fix, I'm getting near-random validation performance for a trained model, and the validation performance differs per validation run. I think this happens since the `model` variable isn't set with the loaded checkpoint, so I'm using a randomly initialized model. Looking at the model activations, they differ each time I run evaluation (but they don't with this fix).

* Update checkpoint loading

* Fixing model loading

Co-authored-by: Ioannis Douratsos <ioannisd@amazon.com>

* Update the NER TF script to remove the softmax and make the pad token label id to -1

* Reformat the quality and style
Co-authored-by: Julien Plu <julien.plu@adevinta.com>

* make decoder input ids optional for t5 training

* lm_lables should not be shifted in t5

* add tests

* finish shift right functionality for PT T5

* move shift right to correct class

* cleaner code

* replace -100 values with pad token id

* add assert statement

* remove unnecessary for loop

* make style

* Add clear description of how to train T5

* correct docstring in T5

* correct typo

* correct docstring format

* update t5 model docs

* implement collins feedback

* fix typo and add more explanation for sentinal tokens

* delete unnecessary todos

* force bleu

* fix wrong file name

* rename file

* different filenames for each example test

* test files should clean up after themselves

* test files should clean up after themselves

* do not force bleu

* correct typo

* fix isort

* Use tokenizer.num_added_tokens to count number of added special_tokens instead of hardcoded numbers.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* run_ner.py - Do not add a label to the labels_ids if word_tokens is empty.

This can happen when using bert-base-multilingual-cased with an input containing an unique space.
In this case, the tokenizer will output just an empty word_tokens thus leading to an non-consistent behavior
over the labels_ids tokens adding one more tokens than tokens vector.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* add t5 docs basis

* improve docs

* add t5 docs

* improve t5 docstring

* add t5 tokenizer docstring

* finish docstring

* make style

* add pretrained models

* correct typo

* make examples work

* finalize docs

T5-small in test


isort

For some reason Sphinx extremely dislikes this and crashes.