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correct spell error: 'nether'

* Add new GLUE example with no Trainer.

* Style

* Address review comments

Fixes an issue in `text-classification` where MNLI eval/test datasets are not being preprocessed. (#10621)

* Fix MNLI tests

* Linter fix

* Hotfix fairscale FSDP

* Evaluation works

* Save on process zero

* batch 1

* this is tpu

* deebert attempt

* the rest

* reverted changes of logging and saving metrics

* added max_sample arguments

* fixed code

* white space diff

* reformetting code

* reformatted code

* fix sharded ddp enum

* test fixes

* stronger validation + apex breaks other tests

* offline mode start

* add specific values

* fix fallback

* add test

* better values check and range

* test that actually works

* document the offline mode

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* more strict check

* cleaner test

* pt-only test

* style
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

This reverts commit f3660613.

* add encode labels function to tokenizer

* start adding finetuning

* init dropout

* upload

* correct convert script

* apply changes

* fix second typo

* make first dummy training run

* adapt convert script

* push confg for comparison

* remove conf

* finish training

* adapt data collator

* add research folder

* update according to fairseq feedback

* some minor corrections

* refactor masking indices a bit

* some minor changes

* clean tokenizer

* finish clean-up

* remove previous logic

* update run script

* correct training

* finish changes

* finish model

* correct bug

* fix training a bit more

* add some tests

* finish gradient checkpointing

* finish example

* correct gradient checkpointing

* improve tokenization method

* revert changes in tokenizer

* revert general change

* adapt fine-tuning

* update

* save intermediate test

* Update README.md

* finish finetuning

* delete conversion script

* Update src/transformers/models/wav2vec2/configuration_wav2vec2.py

* Update src/transformers/models/wav2vec2/processing_wav2vec2.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* finish wav2vec2 script

* finish wav2vec2 fine-tuning

* finalize test

* correct test

* adapt tests

* finish

* remove test file
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* updated logging and saving metrics

* space removal

This PR restores the original functionality that for some reason was modified.

Fixes: https://github.com/huggingface/transformers/issues/10381

@sgugger

* refactors

* typo

* Ass support for ZeRO-2/3 and ZeRO-offload in fairscale

* Quality

* Rework from review comments

* Add doc

* Apply suggestions from code review
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* Address review comments
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

[PretrainedFeatureExtractor] + Wav2Vec2FeatureExtractor, Wav2Vec2Processor, Wav2Vec2Tokenizer (#10324)

* push to show

* small improvement

* small improvement

* Update src/transformers/feature_extraction_utils.py

* Update src/transformers/feature_extraction_utils.py

* implement base

* add common tests

* make all tests pass for wav2vec2

* make padding work & add more tests

* finalize feature extractor utils

* add call method to feature extraction

* finalize feature processor

* finish tokenizer

* finish general processor design

* finish tests

* typo

* remove bogus file

* finish docstring

* add docs

* finish docs

* small fix

* correct docs

* save intermediate

* load changes

* apply changes

* apply changes to doc

* change tests

* apply surajs recommend

* final changes

* Apply suggestions from code review

* fix typo

* fix import

* correct docstring

* handle get_last_lr() before first step()

* abstract away the lr getting logic

* cleanup

* add test

* move to utils

* make logging and saving trainer built-in

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

* implement gradient_accumulation_steps support in DeepSpeed integration

* typo

* cleanup

* cleanup

* add zero-shot distillation script

* readme wordsmithing

* clean up code

* add multi-gpu teacher inference
plus tidying up more code

* add use_fast_tokenizer arg

* update results in readme

* more readme wordsmithing

* style

* Add handle to readme
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* fix code block

* add error+docs about distributed & tpu

* add @sgugger format requests

* xla -> tpu

* support fp16 for teacher preds

* no checkpoint by default

* add demo colab link

* add model sharing prompt + model link

* correct resulting acc of example
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* memory tracker metrics

* go back to eval for somewhat consistency

* handle no-gpu case

* deal with stackable eval calls

* restore callback order

* style

* simplify the API

* add test

* docs

* consistently use eval_ prefix

* improve docs

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

* rename method

* style
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fix invalid port

* missing requirements

* move old s2s scripts to legacy

* add the tests back

* proper rename

* restore

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Stas Bekman <stas@stason.org>
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fix run_seq2seq.py; porting DeepSpeed tests to it

* unrefactor

* defensive programming

* defensive programming 2

* port the rest of the trainer tests

* style

* a cleaner scripts dir finder

* cleanup

* fix rouge metrics and task specific params

* fix typo

* round metrics

* typo

* remove task_specific_params

* init devices/setup explicitly

* docs + test

* simplify

* cleanup

* cleanup

* cleanup

* correct the required dist setup

* derive local_rank from env LOCAL_RANK

* remove xnli_compute_metrics, add load_dataset, load_metric, set_seed,metric.compute,load_metric

* fix

* fix

* fix

* push

* fix

* everything works

* fix init

* fix

* special treatment for sepconv1d

* style

* 🙏🏽

* add doc and cleanup


* fix doc

* fix doc again

* fix doc again

* Apply suggestions from code review

* make style

* Proposal that should work

* Remove needless code

* Fix test

* Apply suggestions from code review

* remove xnli_compute_metrics, add load_dataset, load_metric, set_seed,metric.compute,load_metric

* amend README

* removed data_args.task_name and replaced with task_name = "xnli"; use split function to load train and validation dataset separately; remove __post_init__; remove flag --task_name from README.

* removed dict task_to_keys, use str "xnli" instead of variable task_name, change preprocess_function to use examples["premise"], examples["hypothesis"] directly, remove sentence1_key and sentence2_key, change compute_metrics function to cater only to accuracy metric, add condition for train_langauge is None when using dataset.load_dataset()

* removed `torch.distributed.barrier()` and `import torch` as `from_pretrained` is able to do the work; amend README

* free up memory at the end of train

* rework tests

* consistent formatting

* correction