GitLab

* Fix minor typos

Fix minor typos in the docs.

* Update docs/source/preprocessing.rst

Clearer data structure description.
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Add MLflow integration class

Add integration code for MLflow in integrations.py along with the code
that checks that MLflow is installed.

* Add MLflowCallback import

Add import of MLflowCallback in trainer.py

* Handle model argument

Allow the callback to handle model argument and store model config items as hyperparameters.

* Log parameters to MLflow in batches

MLflow cannot log more than a hundred parameters at once.
Code added to split the parameters into batches of 100 items and log the batches one by one.

* Fix style

* Add docs on MLflow callback

* Fix issue with unfinished runs

The "fluent" api used in MLflow integration allows only one run to be active at any given moment. If the Trainer is disposed off and a new one is created, but the training is not finished, it will refuse to log the results when the next trainer is created.

* Add MLflow integration class

Add integration code for MLflow in integrations.py along with the code
that checks that MLflow is installed.

* Add MLflowCallback import

Add import of MLflowCallback in trainer.py

* Handle model argument

Allow the callback to handle model argument and store model config items as hyperparameters.

* Log parameters to MLflow in batches

MLflow cannot log more than a hundred parameters at once.
Code added to split the parameters into batches of 100 items and log the batches one by one.

* Fix style

* Add docs on MLflow callback

* Fix issue with unfinished runs

The "fluent" api used in MLflow integration allows only one run to be active at any given moment. If the Trainer is disposed off and a new one is created, but the training is not finished, it will refuse to log the results when the next trainer is created.

* distributed training

* fix

* fix formatting

* wording

* Add mixed precision evaluation

* use original flag

Minor typo fixes to the tokenizer summary

* fixing #8001

* make T5 tokenizer serialization more robust - style

#8030

Close #8030

* Create README.md

* Update README.md

* Create README.md

* Update model_cards/ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli/README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* Add Meta information for dataset identifier.
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* Make Seq2Seq Trainer more similar to Trainer

* fix typo

* fix seq2seq trainer

* remove from tests

* remove lock

* remove train files

* delete test files

* correct typo

* check at init

* make sure trainer is not slowed down on TPU

* correct isort

* remove use cache

* fix use cache

* add last use chache = false

* Create README.md

* Update model_cards/sachaarbonel/bert-italian-cased-finetuned-pos/README.md

* Apply suggestions from code review
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

Co-authored-by: Jan Christian Blaise Cruz <jcblaise@Blaises-MacBook-Pro.local>

* model card German Sentence Embeddings V2

- for German RoBERTa for Sentence Embeddings V2
- marked old as outdated

* small correction

* small improvement in description

* small spelling fix

* spelling fix

* add evaluation results

* spearman explanation

* add number of trials

Updating the run_squad training script to handle the "longformer" `model_type`. The longformer is trained in the same was as RoBERTa, so I've added the "longformer" `model_type` (that's the right hugginface name for the LongFormer model, right?) everywhere there was a "roberta" `model_type` reference. The longformer (like RoBERTa) doesn't use `token_type_ids` (as I understand from looking at the [longformer notebook](https://github.com/patil-suraj/Notebooks/blob/master/longformer_qa_training.ipynb), which is what gets updated after this change.

This fix might be related to [this issue](https://github.com/huggingface/transformers/issues/7249) with SQuAD training when using run_squad.py

* remove reformer pad_token_id

* fix pegasus

[tests|tokenizers] Refactoring pipelines test backbone - Small tokenizers improvements - General tests speedups (#7970)

* WIP refactoring pipeline tests - switching to fast tokenizers

* fix dialog pipeline and fill-mask

* refactoring pipeline tests backbone

* make large tests slow

* fix tests (tf Bart inactive for now)

* fix doc...

* clean up for merge

* fixing tests - remove bart from summarization until there is TF

* fix quality and RAG

* Add new translation pipeline tests - fix JAX tests

* only slow for dialog

* Fixing the missing TF-BART imports in modeling_tf_auto

* spin out pipeline tests in separate CI job

* adding pipeline test to CI YAML

* add slow pipeline tests

* speed up tf and pt join test to avoid redoing all the standalone pt and tf tests

* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

* Update src/transformers/pipelines.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/pipelines.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/testing_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* add require_torch and require_tf in is_pt_tf_cross_test
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Move NoLayerEmbedTokens

* TFWrappedEmbeddings

* Add comment

* Fix checkpoint loading in Trainer

* Fix typo

* add zero shot pipeline tags

* rm default and fix yaml format

* rm DS_Store

* add bart large default

* don't add more typos
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* add multiple multilingual examples

* improve multilingual examples for single-label
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* Only log total_flos at the end of training

* Fix test

* FillMaskPipeline: support passing top_k on __call__

Also move from topk to top_k

* migrate to new param name in tests

* Review from @sgugger