GitLab

* 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.

my flake8 wasn't up-to-date enough `make quality` wasn't reporting the same things CI did - this PR adds the actual required version.

Thinking more about some of these minimal versions - CI will always install afresh and thus will always run the latest version. Is there a way to tell pip to always install the latest versions of certain dependencies on `pip install -i ".[dev]"`, rather than hardcoding the minimals which quickly become outdated?

* [gen utils] missing else case

1. `else` is missing - I hit that case while porting a model. Probably needs to assert there?
2. also the comment on top seems to be outdated (just vocab_size is being set there)

* typo

* Create README.md

* Add some custom prompts
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

unittest doesn't support pytest's super-handy `@pytest.mark.parametrize`, I researched and there are many proposed workarounds, most tedious at best. If we include https://pypi.org/project/parameterized/ in dev dependencies - it will provide a very easy to write parameterization in tests. Same as pytest's fixture, plus quite a few other ways. 

Example:
```
from parameterized import parameterized
@parameterized([
    (2, 2, 4),
    (2, 3, 8),
    (1, 9, 1),
    (0, 9, 0),
])
def test_pow(base, exponent, expected):
   assert_equal(math.pow(base, exponent), expected)
```
(extra `self`var if inside a test class)

To remind the pytest style is slightly different:
```
    @pytest.mark.parametrize("test_input,expected", [("3+5", 8), ("2+4", 6), ("6*9", 42)])
    def test_eval(test_input, expected):
```
More examples here: https://pypi.org/project/parameterized

May I suggest that it will make it much easier to write some types of tests?

* [docstring] missing arg

add the missing `tie_word_embeddings` entry

* cleanup

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

there is no var `decoder_input_ids`, but there is `input_ids` for decoder :)

* Add warning for gradient accumulation

* Formatting

cc @jplu

* feat: allow padding_text for any generative model

* docs(pipelines.py): correct typo

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

* feat: rename padding_text to prefix

* fix: cannot tokenize empty text

* fix: pass prefix arg to pipeline

* test: add prefix to text-generetation pipeline

* style: fix style

* style: clean code and variable name more explicit

* set arg docstring to optional
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* create model card for astroGPT

* Hotlink to actual image file
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* Create Readme.MD for KanBERTo

KanBERTo language model readme for Kannada language.

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