Add BROS (#23190)

* add Bros boilerplate * copy and pasted modeling_bros.py from official Bros repo * update copyright of bros files * copy tokenization_bros.py from official repo and update import path * copy tokenization_bros_fast.py from official repo and update import path * copy configuration_bros.py from official repo and update import path * remove trailing period in copyright line * copy and paste bros/__init__.py from official repo * save formatting * remove unused unnecessary pe_type argument - using only crel type * resolve import issue * remove unused model classes * remove unnecessary tests * remove unused classes * fix original code's bug - layer_module's argument order * clean up modeling auto * add bbox to prepare_config_and_inputs * set temporary value to hidden_size (32 is too low because of the of the Bros' positional embedding) * remove decoder test, update create_and_check* input arguemnts * add missing variable to model tests * do make fixup * update bros.mdx * add boilerate plate for no_head inference test * update BROS_PRETRAINED_MODEL_ARCHIVE_LIST (add naver-clova-ocr prefix) * add prepare_bros_batch_inputs function * update modeling_common to add bbox inputs in Bros Model Test * remove unnecessary model inference * add test case * add model_doc * add test case for token_classification * apply fixup * update modeling code * update BrosForTokenClassification loss calculation logic * revert logits preprocessing logic to make sure logits have original shape * - update class name * - add BrosSpadeOutput - update BrosConfig arguments * add boilerate plate for no_head inference test * add prepare_bros_batch_inputs function * add test case * add test case for token_classification * update modeling code * update BrosForTokenClassification loss calculation logic * revert logits preprocessing logic to make sure logits have original shape * apply masking on the fly * add BrosSpadeForTokenLinking * update class name put docstring to the beginning of the file * separate the logits calculation logic and loss calculation logic * update logic for loss calculation so that logits shape doesn't change when return * update typo * update prepare_config_and_inputs * update dummy node initialization * update last_hidden_states getting logic to consider when return_dict is False * update box first token mask param * bugfix: remove random attention mask generation * update keys to ignore on load missing * run make style and quality * apply make style and quality of other codes * update box_first_token_mask to bool type * update index.md * apply make style and quality * apply make fix-copies * pass check_repo * update bros model doc * docstring bugfix fix * add checkpoint for doc, tokenizer for doc * Update README.md * Update docs/source/en/model_doc/bros.md Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update bros.md * Update src/transformers/__init__.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update docs/source/en/model_doc/bros.md Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Apply suggestions from code review Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * apply suggestions from code review * apply suggestions from code review * revert test_processor_markuplm.py * Update test_processor_markuplm.py * apply suggestions from code review * apply suggestions from code review * apply suggestions from code review * update BrosSpadeELForTokenClassification head name to entity linker * add doc string for config params * update class, var names to more explicit and apply suggestions from code review * remove unnecessary keys to ignore * update relation extractor to be initialized with config * add bros processor * apply make style and quality * update bros.md * remove bros tokenizer, add bros processor that wraps bert tokenizer * revert change * apply make fix-copies * update processor code, update itc -> initial token, stc -> subsequent token * add type hint * remove unnecessary condition branches in embedding forward * fix auto tokenizer fail * update docstring for each classes * update bbox input dimension as standard 2 points and convert them to 4 points in forward pass * update bros docs * apply suggestions from code review : update Bros -> BROS in bros.md * 1. box prefix var -> bbox 2. update variable names to be more explicit * replace einsum with torch matmul * apply style and quality * remove unused argument * remove unused arguments * update docstrings * apply suggestions from code review: add BrosBboxEmbeddings, replace einsum with classical matrix operations * revert einsum update * update bros processor * apply suggestions from code review * add conversion script for bros * Apply suggestions from code review * fix readme * apply fix-copies --------- Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
2023-09-15 02:02:37 +09:00
parent 95fe0f5d80
commit 17fdd35481
28 changed files with 2453 additions and 8 deletions
--- a/README_ja.md
+++ b/README_ja.md
@@ -321,6 +321,7 @@ Flax、PyTorch、TensorFlowをcondaでインストールする方法は、それ
 1. **[BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom)** (BigScience workshop から) [BigScience Workshop](https://bigscience.huggingface.co/) から公開されました.
 1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (Alexa から) Adrian de Wynter and Daniel J. Perry から公開された研究論文: [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499)
 1. **[BridgeTower](https://huggingface.co/docs/transformers/model_doc/bridgetower)** (Harbin Institute of Technology/Microsoft Research Asia/Intel Labs から) released with the paper [BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning](https://arxiv.org/abs/2206.08657) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan.
+1. **[BROS](https://huggingface.co/docs/transformers/model_doc/bros)** (NAVER CLOVA から) Teakgyu Hong, Donghyun Kim, Mingi Ji, Wonseok Hwang, Daehyun Nam, Sungrae Park. から公開された研究論文 [BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents](https://arxiv.org/abs/2108.04539)
 1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (Google Research から) Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel から公開された研究論文: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626)
 1. **[CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert)** (Inria/Facebook/Sorbonne から) Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot から公開された研究論文: [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894)
 1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (Google Research から) Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting から公開された研究論文: [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874)
@@ -443,7 +444,7 @@ Flax、PyTorch、TensorFlowをcondaでインストールする方法は、それ
 1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (Google から) Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu から公開された研究論文: [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777)
 1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (Google から) Jason Phang, Yao Zhao, and Peter J. Liu から公開された研究論文: [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347)
 1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (Deepmind から) Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira から公開された研究論文: [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795)
-1. **[Persimmon](https://huggingface.co/docs/transformers/model_doc/persimmon)** (ADEPT から) Erich Elsen, Augustus Odena, Maxwell Nye, Sağnak Taşırlar, Tri Dao, Curtis Hawthorne, Deepak Moparthi, Arushi Somani. から公開された研究論文 [blog post](https://www.adept.ai/blog/persimmon-8b)
+1. **[Persimmon](https://huggingface.co/docs/transformers/main/model_doc/persimmon)** (ADEPT から) Erich Elsen, Augustus Odena, Maxwell Nye, Sağnak Taşırlar, Tri Dao, Curtis Hawthorne, Deepak Moparthi, Arushi Somani. から公開された研究論文 [blog post](https://www.adept.ai/blog/persimmon-8b)
 1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (VinAI Research から) Dat Quoc Nguyen and Anh Tuan Nguyen から公開された研究論文: [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/)
 1. **[Pix2Struct](https://huggingface.co/docs/transformers/model_doc/pix2struct)** (Google から) Kenton Lee, Mandar Joshi, Iulia Turc, Hexiang Hu, Fangyu Liu, Julian Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova. から公開された研究論文 [Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding](https://arxiv.org/abs/2210.03347)
 1. **[PLBart](https://huggingface.co/docs/transformers/model_doc/plbart)** (UCLA NLP から) Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang から公開された研究論文: [Unified Pre-training for Program Understanding and Generation](https://arxiv.org/abs/2103.06333)