GitLab

* Fix some writing issues in the docs

* Run code quality check

* Adding HuggingArtists to Community Notebooks

* Adding HuggingArtists to Community Notebooks

* Adding HuggingArtists to Community Notebooks

* docs: add HuggingArtists to community notebooks
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Squash all commits of modeling_detr_v7 branch into one

* Improve docs

* Fix tests

* Style

* Improve docs some more and fix most tests

* Fix slow tests of ViT, DeiT and DETR

* Improve replacement of batch norm

* Restructure timm backbone forward

* Make DetrForSegmentation support any timm backbone

* Fix name of output

* Address most comments by @LysandreJik

* Give better names for variables

* Conditional imports + timm in setup.py

* Address additional comments by @sgugger

* Make style, add require_timm and require_vision to testsé

* Remove train_backbone attribute of DetrConfig, add methods to freeze/unfreeze backbone

* Add png files to fixtures

* Fix type hint

* Add timm to workflows

* Add `BatchNorm2d` to the weight initialization

* Fix retain_grad test

* Replace model checkpoints by Facebook namespace

* Fix name of checkpoint in test

* Add user-friendly message when scipy is not available

* Address most comments by @patrickvonplaten

* Remove return_intermediate_layers attribute of DetrConfig and simplify Joiner

* Better initialization

* Scipy is necessary to get sklearn metrics

* Rename TimmBackbone to DetrTimmConvEncoder and rename DetrJoiner to DetrConvModel

* Make style

* Improve docs and add 2 community notebooks
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

* Improve docs of DeiT and ViT, add community notebook

* Add gitignore for test_samples

* Add notebook with Trainer
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Rebase with master

* Minor bug fix in docs

* Copy files from adding_luke_v2 and improve docs

* change the default value of use_entity_aware_attention to True

* remove word_hidden_states

* fix head models

* fix tests

* fix the conversion script

* add integration tests for the pretrained large model

* improve docstring

* Improve docs, make style

* fix _init_weights for pytorch 1.8

* improve docs

* fix tokenizer to construct entity sequence with [MASK] entity when entities=None

* Make fix-copies

* Make style & quality

* Bug fixes

* Add LukeTokenizer to init

* Address most comments by @patil-suraj and @LysandreJik

* rename _compute_extended_attention_mask to get_extended_attention_mask

* add comments to LukeSelfAttention

* fix the documentation of the tokenizer

* address comments by @patil-suraj, @LysandreJik, and @sgugger

* improve docs

* Make style, quality and fix-copies

* Improve docs

* fix docs

* add "entity_span_classification" task

* update example code for LukeForEntitySpanClassification

* improve docs

* improve docs

* improve the code example in luke.rst

* rename the classification layer in LukeForEntityClassification from typing to classifier

* add bias to the classifier in LukeForEntitySpanClassification

* update docs to use fine-tuned hub models in code examples of the head models

* update the example sentences

* Make style & quality

* Add require_torch to tokenizer tests

* Add require_torch to tokenizer tests

* Address comments by @sgugger and add community notebooks

* Make fix-copies
Co-authored-by: Ikuya Yamada <ikuya@ikuya.net>

* Add Wav2Vec Inference notebook

* Update docs/source/community.md
Co-authored-by: Suraj Patil <surajp815@gmail.com>

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

* Add new community notebook - wav2vec2 with GPT

* Update:community.md, new nb add
* feat: notebook of wav2vec xlsr ctc decoding with gpt logit adjustment
* Update: Wav2vec2 CTC decoding with gpt2 adjustment

* Update docs/source/community.md
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Update:community.md, new nb add

* feat: updated grammar on  nb description

* Update: Train summarizer for BlenderBotSmall

* doc: update W&B related doc

* doc(wandb): mention report_to

* doc(wandb): commit suggestion
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* doc(wandb): fix typo

* doc(wandb): remove WANDB_DISABLED
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fix: Makes small typo corrections & standardises glossary

* feat: Adds introduction & links to transformer flashcards

* feat: Adds attribution & adjustments requested in #8949

* feat: Adds flashcards to community.md

* refactor: Removes flashcards from glossary

* Add LayoutLMForSequenceClassification and integration tests

Improve docs

Add LayoutLM notebook to list of community notebooks

* Make style & quality

* Address comments by @sgugger, @patrickvonplaten and @LysandreJik

* Fix rebase with master

* Reformat in one line

* Improve code examples as requested by @patrickvonplaten
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

https://github.com/huggingface/transformers/pull/3177

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

* Created using Colaboratory

* mbart-training examples add

* link add

* Update description
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* First commit: adding all files from tapas_v3

* Fix multiple bugs including soft dependency and new structure of the library

* Improve testing by adding torch_device to inputs and adding dependency on scatter

* Use Python 3 inheritance rather than Python 2

* First draft model cards of base sized models

* Remove model cards as they are already on the hub

* Fix multiple bugs with integration tests

* All model integration tests pass

* Remove print statement

* Add test for convert_logits_to_predictions method of TapasTokenizer

* Incorporate suggestions by Google authors

* Fix remaining tests

* Change position embeddings sizes to 512 instead of 1024

* Comment out positional embedding sizes

* Update PRETRAINED_VOCAB_FILES_MAP and PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

* Added more model names

* Fix truncation when no max length is specified

* Disable torchscript test

* Make style & make quality

* Quality

* Address CI needs

* Test the Masked LM model

* Fix the masked LM model

* Truncate when overflowing

* More much needed docs improvements

* Fix some URLs

* Some more docs improvements

* Test PyTorch scatter

* Set to slow + minify

* Calm flake8 down

* First commit: adding all files from tapas_v3

* Fix multiple bugs including soft dependency and new structure of the library

* Improve testing by adding torch_device to inputs and adding dependency on scatter

* Use Python 3 inheritance rather than Python 2

* First draft model cards of base sized models

* Remove model cards as they are already on the hub

* Fix multiple bugs with integration tests

* All model integration tests pass

* Remove print statement

* Add test for convert_logits_to_predictions method of TapasTokenizer

* Incorporate suggestions by Google authors

* Fix remaining tests

* Change position embeddings sizes to 512 instead of 1024

* Comment out positional embedding sizes

* Update PRETRAINED_VOCAB_FILES_MAP and PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

* Added more model names

* Fix truncation when no max length is specified

* Disable torchscript test

* Make style & make quality

* Quality

* Address CI needs

* Test the Masked LM model

* Fix the masked LM model

* Truncate when overflowing

* More much needed docs improvements

* Fix some URLs

* Some more docs improvements

* Add add_pooling_layer argument to TapasModel

Fix comments by @sgugger and @patrickvonplaten

* Fix issue in docs + fix style and quality

* Clean up conversion script and add task parameter to TapasConfig

* Revert the task parameter of TapasConfig

Some minor fixes

* Improve conversion script and add test for absolute position embeddings

* Improve conversion script and add test for absolute position embeddings

* Fix bug with reset_position_index_per_cell arg of the conversion cli

* Add notebooks to the examples directory and fix style and quality

* Apply suggestions from code review

* Move from `nielsr/` to `google/` namespace

* Apply Sylvain's comments
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
Co-authored-by: Rogge Niels <niels.rogge@howest.be>
Co-authored-by: LysandreJik <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: sgugger <sylvain.gugger@gmail.com>

* Add copyright everywhere missing

* Style

Looking at the current community notebooks, it seems that few are targeted for absolute beginners and even fewer are written with TensorFlow. This notebook describes absolutely everything a beginner would need to know, including how to save/load their model and use it for new predictions (this is often omitted in tutorials)
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* added qg evaluation notebook

* Update notebooks/README.md
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* t5 t5 community notebook added

* author link updated

* t5 t5 community notebook added

* author link updated

* new colab link updated
Co-authored-by: harris <muhammad.harris@visionx.io>

* added multilabel classification using distilbert notebook to community notebooks

* added multilabel classification using distilbert notebook to community notebooks

Co-authored-by: eliska <3648991+elisans@users.noreply.github.com>

Signed-off-by: lordtt13 <thakurtanmay72@yahoo.com>