GitLab

* init bigbird

* model.__init__ working, conversion script ready, config updated

* add conversion script

* BigBirdEmbeddings working :)

* slightly update conversion script

* BigBirdAttention working :) ; some bug in layer.output.dense

* add debugger-notebook

* forward() working for BigBirdModel :) ; replaced gelu with gelu_fast

* tf code adapted to torch till rand_attn in bigbird_block_sparse_attention ; till now everything working :)

* BigBirdModel working in block-sparse attention mode :)

* add BigBirdForPreTraining

* small fix

* add tokenizer for BigBirdModel

* fix config & hence modeling

* fix base prefix

* init testing

* init tokenizer test

* pos_embed must be absolute, attn_type=original_full when add_cross_attn=True , nsp loss is optional in BigBirdForPreTraining, add assert statements

* remove position_embedding_type arg

* complete normal tests

* add comments to block sparse attention

* add attn_probs for sliding & global tokens

* create fn for block sparse attn mask creation

* add special tests

* restore pos embed arg

* minor fix

* attn probs update

* make big bird fully gpu friendly

* fix tests

* remove pruning

* correct tokenzier & minor fixes

* update conversion script , remove norm_type

* tokenizer-inference test add

* remove extra comments

* add docs

* save intermediate

* finish trivia_qa conversion

* small update to forward

* correct qa and layer

* better error message

* BigBird QA ready

* fix rebased

* add triva-qa debugger notebook

* qa setup

* fixed till embeddings

* some issue in q/k/v_layer

* fix bug in conversion-script

* fixed till self-attn

* qa fixed except layer norm

* add qa end2end test

* fix gradient ckpting ; other qa test

* speed-up big bird a bit

* hub_id=google

* clean up

* make quality

* speed up einsum with bmm

* finish perf improvements for big bird

* remove wav2vec2 tok

* fix tokenizer

* include docs

* correct docs

* add helper to auto pad block size

* make style

* remove fast tokenizer for now

* fix some

* add pad test

* finish

* fix some bugs

* fix another bug

* fix buffer tokens

* fix comment and merge from master

* add comments

* make style

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

* Fix typos

* fix some more suggestions

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

* fix copies

* another path
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* update

* update nit suggestions

* make style
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Fixes in the templates

* Define in all cases

* Dimensionality -> Dimension
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

Fixes https://github.com/huggingface/transformers/security/dependabot/examples/research_projects/lxmert/requirements.txt/Pygments/open

@LysandreJik

* fix cpu mem metrics; reformat runtime metric

* adjust dependency

* extend docs

* soft dependency

* cleanup

* fix the runtime metric issue

* restore

* move docs, cross reference from 2 places, improve

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

* Initial commit

* Another bunch of updates

* make style quliaty + delete debug arg from bash script

* Use compue_metrics func

* Do a few fixes

* Add copyright

* Fix typos

A new argument `length_column_name` has been added to
`TrainingArguments`, with default value `"length"`. If this column
exists and `group_by_length` is `True`, the train sampler will use
it for grouping rather than computing it before training starts.

This is an optimization that allows the user to prepare data for fast
processing, preventing sequential access to the dataset as described in
issue #10909.

* Add NER example with accelerate library

* This commit contains the first (yet really unfinished)
version of a script for showing how to train HuggingFace model
with their new accelerate library.

* Fix metric calculation

* make style quality

* mv ner_no_trainer to token-classification dir

* Delete --debug flag from running script

* hf_datasets -> raw_datasets

* Make a few slight adjustments

* Add an informative comment + rewrite a help comment

* Change header

* Fix a few things

* Enforce to use fast tokenizers only

* DataCollatorWithPadding -> DataCollatorForTokenClassification

* Change bash script: python3 -> accelerate launch

* make style

* Add a few missing things (see below)

* Add a max-lenghth padding to predictions and labels to
enable accelerate gather functionality

* Add PyTorch no trainer example to the example README.md

* Remove --do-train from args as being redundant for now

* DataCollatorWithPadding -> DataCollatorForTokenClassification

* Remove some obsolete args.do_train conditions from the script

* Delete --do_train from bash running script

* Delete use_slow_tokenizer from args

* Add unintentionally removed flag --label_all_tokens

* Delete --debug flag from running script

* Instantiate model only once in pipeline

* Remove documentation of deprecated method

* Add FutureWarning

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

Co-authored-by: WybeKoper <WybeKoper@users.noreply.github.com>

* Add ImageFeatureExtractionMixin

* Add dummy vision objects

* Add require_vision

* Add tests

* Fix test

this PR fixes https://github.com/huggingface/transformers/security/dependabot/examples/research_projects/lxmert/requirements.txt/PyYAML/open

* Rename NLP library to Datasets library

* Update github template

* Fix styling

* Added embeddings layer

* Added layoutlm layers, main model, maskedlm and token classification classes

* Added model classes to tf auto models

* Added model to PT to TF conversion script

* Added model to doc README

* Added tests

* Removed unused imports

* Added layoutlm model, test, and doc for sequence classification, and fix imports in __init__.py

* Made tests pass!

* Fixed typos in imports and docs

* Fixed a typo in embeddings layer

* Removed imports

* Fixed formatting issues, imports, tests

* Added layoutlm layers, main model, maskedlm and token classification classes

* Added model classes to tf auto models

* Added model to PT to TF conversion script

* Removed unused imports

* Added layoutlm model, test, and doc for sequence classification, and fix imports in __init__.py

* Made tests pass!

* Fixed typos in imports and docs

* Removed imports

* Fixed small formatting issues

* Removed duplicates import from main __init__.py

* Chnaged deafult arg to true for adding  pooling layer to tf layoutlm

* Fixed formatting issues

* Style

* Added copied from to classes copied from bert

* Fixed doc strings examples to work with layoutlm inputs

* Removed PyTorch reference in doc strings example

* Added integration tests

* Cleaned up initialization file

* Updated model checkpoint identifiers

* Fixed imports
Co-authored-by: Amir Tahmasbi <amir@ehsai.ca>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

Use the correct variable (raw_datasets) instead of the module (datasets)
where appropriate.

* Remove version warning in pretrained BART models

* Put it at the base model

* Removes overflowing bad word IDs

* Raise warning

Co-authored-by: Eliza <eliza@habanero.tiger.com.pl>

the orignal code in line 246 is
```
tokenizer: Optional["PreTrainedTokenizerBase"] = None,
```

it should be
```
tokenizer: Optional[PreTrainedTokenizerBase] = None,
```

* rewrote is_sagemaker_model_parallel_available

* added is_sagemaker_model_parallel_available to SageMakerTrainer

* removed unnecessary mp_parameters as TrainingArguments

* make style happy

* added mp_parameters again to parse mp-specific args.

* added predict stage

* added test keyword in exception message

* removed example specific saving predictions

* fixed f-string error

* removed extra line
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* import refactor

* fix the fallback

* added finished documentation

* changed version from 1.6 to 1.6.0 for distributed

* updated versions

* updated urls