GitLab

* model card for bert-base-NER

* add meta data up top
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* fix wandb/comet problems

* simplify

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

cc @Narsil @mfuntowicz @joeddav

see d99ed7ad

- TFAutoModelForCausalLM
- TFAutoModelForMaskedLM
- TFAutoModelForSeq2SeqLM

as per deprecation warning. No tests as it simply removes current
warnings from tests.

* Improving Pipelines by defaulting to framework='tf' when

pytorch seems unavailable.

* Actually changing the default resolution order to account for model
defaults

Adding a new tests for each pipeline to check that pipeline(task) works
too without manually adding the framework too.

* Remove wrong parameter.

* Same in Longformer

* Create README.md

* Update model_cards/sentence-transformers/LaBSE/README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* Create README.md

* Update model_cards/sarahlintang/IndoBERT/README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* Don't use `store_xxx` on optional bools

* Refine test

* Refine test

* Add eval_accumulation_step and clean distributed eval

* Add TPU test

* Add TPU stuff

* Fix arg name

* Fix Seq2SeqTrainer

* Fix total_size

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

* Doc and add test to TPU

* Add unit test

* Adapt name
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Add support for gpt2 batch inferencing

* add test

* remove typo
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>

* fix bert position ids in DPR convert script

* style

* Adding optional trial argument to model_init
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* use DDP no_sync when possible

* fix is_nlp_available addition mistake

* reformat trainer.py

* reformat trainer.py

* drop support for pytorch < 1.2

* return support for pytorch < 1.2

* Do not softmax when num_labels==1

* Update src/transformers/pipelines.py
Co-authored-by: Funtowicz Morgan <mfuntowicz@users.noreply.github.com>
Co-authored-by: Funtowicz Morgan <mfuntowicz@users.noreply.github.com>

* fix rag

* Update tokenizer save_pretrained
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>

Putting my name on a couple more issues to directly redirect them to me

* Add Documentation for GPT-1 Classification

* Add GPT-1 with Classification head

* Add tests for GPT-1 Classification

* Add GPT-1 For Classification to auto models

* Remove authorized missing keys, change checkpoint to openai-gpt

* Fix test

* fix generic text classification

* fix test

* Fix tests

Fix a bug that happends when subclassing Trainer and
overwriting evaluate() without calling prediciton_loop()

Very often splitting large files to smaller files can prevent tokenizer going out of memory in environment like Colab that does not have swap memory