GitLab

* these tests require non-multigpu env

* cleanup

* clarify

* nlp -> datasets

* More nlp -> datasets

* Woopsie

* More nlp -> datasets

* One last

* correct docs for bert generation

* upload

* fix to ensure that returned tensors after the tokenization is Long

* fix to ensure that returned tensors after the tokenization is Long
Co-authored-by: Ashwin Geet Dsa <adsa@grvingt-6.nancy.grid5000.fr>

* Add TF Funnel Transformer

* Proper dummy input

* Formatting

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

* Address review comments

* One review comment forgotten
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* add conversion script

* improve conversion script

* make style

* add tryout files

* fix

* update

* add causal bert

* better names

* add tokenizer file as well

* finish causal_bert

* fix small bugs

* improve generate

* change naming

* renaming

* renaming

* renaming

* remove leftover files

* clean files

* add fix tokenizer

* finalize

* correct slow test

* update docs

* small fixes

* fix link

* adapt check repo

* apply sams and sylvains recommendations

* fix import

* implement Lysandres recommendations

* fix logger warn

* add dataset for albert pretrain

* datacollator for albert pretrain

* naming, comprehension, file reading change

* data cleaning is no needed after this modification

* delete prints

* fix a bug

* file structure change

* add tests for albert datacollator

* remove random seed

* add back len and get item function

* sample file for testing and test code added

* format change for black

* more format change

* Style

* var assignment issue resolve

* add back wrongly deleted DataCollatorWithPadding in init file

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

1. Swapped missing_keys and unexpected_keys.

2. Copy&paste error caused these warnings to say "from TF 2.0" when it's actually "from PyTorch".

Batch encore plus and overflowing tokens fails when non existing overflowing tokens for a sequence (#6677)

* Patch and test

* Fix tests

Currently beam search returns inconsistent outputs - if hypos have different lengths we get eos, if they are the same - we don't.

This PR makes the output consistent.

Also why not also replace:

```
            if sent_lengths[i] < max_length:
                decoded[i, sent_lengths[i]] = eos_token_id
```
with:
```
            decoded[i, sent_lengths[i]] = eos_token_id
```
Shouldn't eos always be there? If the data gets truncated, the caller needs to user a larger `max_length`.

Please correct me if my logic is flawed.

* introduce TRANSFORMERS_VERBOSITY env var + test + test helpers

* cleanup

* remove helper function

* fix longformer

* allow position ids to not be initialized

* Should check if `torch` is available

* fixed samples_count error, distributed_concat arguments

* style

* Import torch at beginning of file
Co-authored-by: TevenLeScao <teven.lescao@gmail.com>

* neFLOs calculation, logging, and reloading (#1)

* testing distributed consecutive batches

* fixed AttributeError from DataParallel

* removed verbosity

* rotate with use_mtime=True

* removed print

* fixed interaction with gradient accumulation

* indent formatting

* distributed neflo counting

* fixed typo

* fixed typo

* mean distributed losses

* exporting log history

* moved a few functions

* floating_point_ops clarification for transformers with parameter-reuse

* code quality

* double import

* made flo estimation more task-agnostic

* only logging flos if computed

* code quality

* unused import

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

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

* Sylvain review

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

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

* Initial model

* Fix upsampling

* Add special cls token id and test

* Formatting

* Test and fist FunnelTokenizerFast

* Common tests

* Fix the check_repo script and document Funnel

* Doc fixes

* Add all models

* Write doc

* Fix test

* Initial model

* Fix upsampling

* Add special cls token id and test

* Formatting

* Test and fist FunnelTokenizerFast

* Common tests

* Fix the check_repo script and document Funnel

* Doc fixes

* Add all models

* Write doc

* Fix test

* Fix copyright

* Forgot some layers can be repeated

* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

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

* Address review comments

* Update src/transformers/modeling_funnel.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Address review comments

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

* Slow integration test

* Make small integration test

* Formatting

* Add checkpoint and separate classification head

* Formatting

* Expand list, fix link and add in pretrained models

* Styling

* Add the model in all summaries

* Typo fixes
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

* fixed trainer tr_loss memory leak

* detached returned training loss from computation graph in the Trainer class' training_step() method

* Revert "fixed trainer tr_loss memory leak"

This reverts commit 47226e4e

apologies for the tiny PRs, just sending those as I find them.

ParsBERT v2.0 is a fine-tuned and vocab-reconstructed version of ParsBERT, and it's able to be used in other scopes!

It includes these features:
- We added some unused-vocab for use in summarization and other scopes.
- We fine-tuned the model on vast styles of writing in the Persian language.