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outptus -> outputs in example of BertForPreTraining

* Create README.md

Model card for huBERT.

* Update README.md

lowercase h

* Update model_cards/SZTAKI-HLT/hubert-base-cc/README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

Fix range of possible score, add inference .

Co-authored-by: zolekode <pascal.zoleko@fau.de>

Add language meta attribute

Add language meta attribute

* Create README.md

model card for  akhooli/xlm-r-large-arabic-sent

* Update model_cards/akhooli/xlm-r-large-arabic-sent/README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* finish xlm-roberta

* finish docs

* expose XLMRobertaForCausalLM

* Create README.md

* Update README.md

* Add cache_dir to save features TextDataset

This is in case the dataset is in a RO filesystem, for which is the case
in tests (GKE TPU tests).

* style

* fix generate for GPT2 Double Head

* fix gpt2 double head model

* fix  bart / t5

* also add for no beam search

* fix no beam search

* fix encoder decoder

* simplify t5

* simplify t5

* fix t5 tests

* fix BART

* fix transfo-xl

* fix conflict

* integrating sylvains and sams comments

* fix tf past_decoder_key_values

* fix enc dec test

Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Add logging doc

* Foamtting

* Update docs/source/main_classes/logging.rst

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

we had it added for one job, please add it to all pytest jobs - we need the output of what tests were run to debug the codecov issue. thank you!

* Update ONNX notebook to include section on quantization.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* Addressing ONNX team comments

* Split the run_hp_search by backend

* Unused import

* Introduce HPO checkpointing for PBT

* Moved checkpoint saving

* Fixed checkpoint subdir pass

* Fixed style

* Enable/disable checkpointing, check conditions for various tune schedulers incl. PBT

* Adjust number of GPUs to number of jobs

* Avoid mode pickling in ray

* Move hp search to integrations

* Only access loss tensor every logging_steps

* tensor.item() was being called every step. This must not be done
for XLA:TPU tensors as it's terrible for performance causing TPU<>CPU
communication at each step. On RoBERTa MLM for example, it reduces step
time by 30%, should be larger for smaller step time models/tasks.
* Train batch size was not correct in case a user uses the
`per_gpu_train_batch_size` flag
* Avg reduce loss accross eval shards

* Fix style (#6803)

* t5 model should make decoder_attention_mask (#6800)

* [s2s] Test hub configs in self-scheduled CI (#6809)

* [s2s] round runtime in run_eval (#6798)

* Pegasus finetune script: add --adafactor (#6811)

* [bart] rename self-attention -> attention (#6708)

* [tests] fix typos in inputs (#6818)

* Fixed open in colab link (#6825)

* Add model card for singbert lite. Update widget for singbert and singbert-large. (#6827)

* BR_BERTo model card (#6793)

* clearly indicate shuffle=False (#6312)

* Clarify shuffle

* clarify shuffle
Co-authored-by: Kevin Canwen Xu <canwenxu@126.com>

* [s2s README] Add more dataset download instructions (#6737)

* Style

* Patch logging issue

* Set default logging level to `WARNING` instead of `INFO`

* TF Flaubert w/ pre-norm (#6841)

* Dataset and DataCollator for BERT Next Sentence Prediction (NSP) task (#6644)

* add datacollator and dataset for next sentence prediction task

* bug fix (numbers of special tokens & truncate sequences)

* bug fix (+ dict inputs support for data collator)

* add padding for nsp data collator; renamed cached files to avoid conflict.

* add test for nsp data collator

* Style
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

* Fix in Adafactor docstrings (#6845)

* Fix resuming training for Windows (#6847)

* Only access loss tensor every logging_steps

* tensor.item() was being called every step. This must not be done
for XLA:TPU tensors as it's terrible for performance causing TPU<>CPU
communication at each step. On RoBERTa MLM for example, it reduces step
time by 30%, should be larger for smaller step time models/tasks.
* Train batch size was not correct in case a user uses the
`per_gpu_train_batch_size` flag
* Avg reduce loss accross eval shards

* comments
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Thomas Ashish Cherian <6967017+PandaWhoCodes@users.noreply.github.com>
Co-authored-by: Zane Lim <zyuanlim@gmail.com>
Co-authored-by: Rodolfo De Nadai <rdenadai@gmail.com>
Co-authored-by: xujiaze13 <37360975+xujiaze13@users.noreply.github.com>
Co-authored-by: Kevin Canwen Xu <canwenxu@126.com>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Huang Lianzhe <hlz@pku.edu.cn>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* add datacollator and dataset for next sentence prediction task

* bug fix (numbers of special tokens & truncate sequences)

* bug fix (+ dict inputs support for data collator)

* add padding for nsp data collator; renamed cached files to avoid conflict.

* add test for nsp data collator

* Style
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>