GitLab

* Result of black 23.1

* Update target to Python 3.7

* Switch flake8 to ruff

* Configure isort

* Configure isort

* Apply isort with line limit

* Put the right black version

* adapt black in check copies

* Fix copies

* biogpt initial commit

* updated init

* fix faster decoding with use_cache

* 1. fix input_ids and input_embeds with correct device
2. added _keys_to_ignore_on_load_missing
3. updated prepare_inputs_for_generation

* add activation_dropout and scale_embedding

* replace fsmt attention with bart attention

* added test

* run make fix-copies

* doc init and fix build

* updated README with proper information

* 1. added tips to docs
2. updated BioGptTokenizer func

* 1. added tokenizer test
2. refactor tokenizer

* make fixup

* add biogpt fairseq to hf converter

* updated layer names more
similar to original checkpoints

* config update doc string and set defaults

* added "#copied" from bart model and
updated doc strings

* enable model_input_names in tokenizer

* 1.  positionalembedding depending on attention_mask
2. added attention mask to prepare for generation

* added test to verify past and generation

* BioGptLMHeadModel -> BioGptForCausalLM

* fix typo

* tokenization and test
Copyright and updated assertion

* updated Copyright and
one func at time in line

* Copyright updates and
minor doc fix

* replace assertion with ValueError

* rm extra space

* added code syntax

* revert cmnt position change

* add tokenizer to auto

* updated doc string

* tokenizer doc string update

* biogpt hub model update to microsoft/biogpt

* make fixup

* rm cmnt to fix flake8 5.0.4 vs 6 error

* New doc styler

* Fix issue with args at the start

* Code sample fixes

* Style code examples in MDX

* Fix more patterns

* Typo

* Typo

* More patterns

* Do without black for now

* Get more info in error

* Docstring style

* Re-enable check

* Quality

* Fix add_end_docstring decorator

* Fix docstring

* Convert docstrings of all configurations and tokenizers

* Processors and fixes

* Last modeling files and fixes to models

* Pipeline modules

* Utils files

* Data submodule

* All the other files

* Style

* Missing examples

* Style again

* Fix copies

* Say bye bye to rst docstrings forever

* First third

* Styling and fix mistake

* Quality

* All the rest

* Treat %s and %d

* typo

* Missing )

* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Put models in subfolders

* Styling

* Fix imports in tests

* More fixes in test imports

* Sneaky hidden imports

* Fix imports in doc files

* More sneaky imports

* Finish fixing tests

* Fix examples

* Fix path for copies

* More fixes for examples

* Fix dummy files

* More fixes for example

* More model import fixes

* Is this why you're unhappy GitHub?

* Fix imports in conver command

* Fix doc errors and typos across the board

* Fix a typo

* Fix the CI

* Fix more typos

* Fix CI

* More fixes

* Fix CI

* More fixes

* More fixes

* Important files

* Styling them all

* Revert "Styling them all"

This reverts commit 7d029395fdae8513b8281cbc2a6c239f8093503e.

* Syling them for realsies

* Fix syntax error

* Fix benchmark_utils

* More fixes

* Fix modeling auto and script

* Remove new line

* Fixes

* More fixes

* Fix more files

* Style

* Add FSMT

* More fixes

* More fixes

* More fixes

* More fixes

* Fixes

* More fixes

* More fixes

* Last fixes

* Make sphinx happy

* splitting fast and slow tokenizers [WIP]

* [WIP] splitting sentencepiece and tokenizers dependencies

* update dummy objects

* add name_or_path to models and tokenizers

* prefix added to file names

* prefix

* styling + quality

* spliting all the tokenizer files - sorting sentencepiece based ones

* update tokenizer version up to 0.9.0

* remove hard dependency on sentencepiece 🎉

* and removed hard dependency on tokenizers 🎉



* update conversion script

* update missing models

* fixing tests

* move test_tokenization_fast to main tokenization tests - fix bugs

* bump up tokenizers

* fix bert_generation

* update ad fix several tokenizers

* keep sentencepiece in deps for now

* fix funnel and deberta tests

* fix fsmt

* fix marian tests

* fix layoutlm

* fix squeezebert and gpt2

* fix T5 tokenization

* fix xlnet tests

* style

* fix mbart

* bump up tokenizers to 0.9.2

* fix model tests

* fix tf models

* fix seq2seq examples

* fix tests without sentencepiece

* fix slow => fast  conversion without sentencepiece

* update auto and bert generation tests

* fix mbart tests

* fix auto and common test without tokenizers

* fix tests without tokenizers

* clean up tests lighten up when tokenizers + sentencepiece are both off

* style quality and tests fixing

* add sentencepiece to doc/examples reqs

* leave sentencepiece on for now

* style quality split hebert and fix pegasus

* WIP Herbert fast

* add sample_text_no_unicode and fix hebert tokenization

* skip FSMT example test for now

* fix style

* fix fsmt in example tests

* update following Lysandre and Sylvain's comments

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

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

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

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

Adding Fast tokenizers for SentencePiece based tokenizers - Breaking: remove Transfo-XL fast tokenizer (#7141)

* [WIP] SP tokenizers

* fixing tests for T5

* WIP tokenizers

* serialization

* update T5

* WIP T5 tokenization

* slow to fast conversion script

* Refactoring to move tokenzier implementations inside transformers

* Adding gpt - refactoring - quality

* WIP adding several tokenizers to the fast world

* WIP Roberta - moving implementations

* update to dev4 switch file loading to in-memory loading

* Updating and fixing

* advancing on the tokenizers - updating do_lower_case

* style and quality

* moving forward with tokenizers conversion and tests

* MBart, T5

* dumping the fast version of transformer XL

* Adding to autotokenizers + style/quality

* update init and space_between_special_tokens

* style and quality

* bump up tokenizers version

* add protobuf

* fix pickle Bert JP with Mecab

* fix newly added tokenizers

* style and quality

* fix bert japanese

* fix funnel

* limite tokenizer warning to one occurence

* clean up file

* fix new tokenizers

* fast tokenizers deep tests

* WIP adding all the special fast tests on the new fast tokenizers

* quick fix

* adding more fast tokenizers in the fast tests

* all tokenizers in fast version tested

* Adding BertGenerationFast

* bump up setup.py for CI

* remove BertGenerationFast (too early)

* bump up tokenizers version

* Clean old docstrings

* Typo

* Update following Lysandre comments
Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com>

* Clean up model documentation

* Formatting

* Preparation work

* Long lines

* Main work on rst files

* Cleanup all config files

* Syntax fix

* Clean all tokenizers

* Work on first models

* Models beginning

* FaluBERT

* All PyTorch models

* All models

* Long lines again

* Fixes

* More fixes

* Update docs/source/model_doc/bert.rst
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update docs/source/model_doc/electra.rst
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Last fixes
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Add BERTweet and PhoBERT models

* Update modeling_auto.py

Re-add `bart` to LM_MAPPING

* Update tokenization_auto.py

Re-add `from .configuration_mobilebert import MobileBertConfig`
not sure why it's replaced by `from transformers.configuration_mobilebert import MobileBertConfig`

* Add BERTweet and PhoBERT to pretrained_models.rst

* Update tokenization_auto.py

Remove BertweetTokenizer and PhobertTokenizer out of tokenization_auto.py (they are currently not supported by AutoTokenizer.

* Update BertweetTokenizer - without nltk

* Update model card for BERTweet

* PhoBERT - with Auto mode - without import fastBPE

* PhoBERT - with Auto mode - without import fastBPE

* BERTweet - with Auto mode - without import fastBPE

* Add PhoBERT and BERTweet to TF modeling auto

* Improve Docstrings for PhobertTokenizer and BertweetTokenizer

* Update PhoBERT and BERTweet model cards

* Fixed a merge conflict in tokenization_auto

* Used black to reformat BERTweet- and PhoBERT-related files

* Used isort to reformat BERTweet- and PhoBERT-related files

* Reformatted BERTweet- and PhoBERT-related files based on flake8

* Updated test files

* Updated test files

* Updated tf test files

* Updated tf test files

* Updated tf test files

* Updated tf test files

* Update commits from huggingface

* Delete unnecessary files

* Add tokenizers to auto and init files

* Add test files for tokenizers

* Revised model cards

* Update save_vocabulary function in BertweetTokenizer and PhobertTokenizer and test files

* Revised test files

* Update orders of Phobert and Bertweet tokenizers in auto tokenization file

* remove the implied defaults to :obj:`None`

* fix bug in the original

* replace to :obj:`True`, :obj:`False`

* Logging

* Style

* hf_logging > utils.logging

* Address @thomwolf's comments

* Update test

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

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

* remove references to old API in docstring - update data processors

* style

* fix tests - better type checking error messages

* better type checking

* include awesome fix by @LysandreJik for #5310

* updated doc and examples

* First pass on utility classes and python tokenizers

* finishing cleanup pass

* style and quality

* Fix tests

* Updating following @mfuntowicz comment

* style and quality

* Fix Roberta

* fix batch_size/seq_length inBatchEncoding

* add alignement methods + tests

* Fix OpenAI and Transfo-XL tokenizers

* adding trim_offsets=True default for GPT2 et RoBERTa

* style and quality

* fix tests

* add_prefix_space in roberta

* bump up tokenizers to rc7

* style

* unfortunately tensorfow does like these - removing shape/seq_len for now

* Update src/transformers/tokenization_utils.py
Co-Authored-By: Stefan Schweter <stefan@schweter.it>

* Adding doc and docstrings

* making flake8 happy
Co-authored-by: Stefan Schweter <stefan@schweter.it>

* XLM-R now passes common tests + Integration tests

* Correct mask index

* Model input names

* Style

* Remove text preprocessing

* Unneccessary import

* All Tokenizers

BertTokenizer + few fixes
RobertaTokenizer
OpenAIGPTTokenizer + Fixes
GPT2Tokenizer + fixes
TransfoXLTokenizer
Correct rst for TransformerXL
XLMTokenizer + fixes
XLNet Tokenizer + Style
DistilBERT + Fix XLNet RST
CTRLTokenizer
CamemBERT Tokenizer
FlaubertTokenizer
XLMRobertaTokenizer
cleanup

* cleanup

This should stabilize formatting.

This prevents transformers from being importable simply because the CWD
is the root of the git repository, while not being importable from other
directories. That led to inconsistent behavior, especially in examples.

Once you fetch this commit, in your dev environment, you must run:

    $ pip uninstall transformers
    $ pip install -e .

This is the result of:

    $ isort --recursive examples templates transformers utils hubconf.py setup.py

This is the result of:

    $ black --line-length 119 examples templates transformers utils hubconf.py setup.py

There's a lot of fairly long lines in the project. As a consequence, I'm
picking the longest widely accepted line length, 119 characters.

This is also Thomas' preference, because it allows for explicit variable
names, to make the code easier to understand.

configuration/modeling/tokenization: add various fine-tuned XLM-RoBERTa models for English, German, Spanish and Dutch (CoNLL datasets)