Add REALM (#13292)

* REALM initial commit * Retriever OK (Update new_gelu). * Encoder prediction score OK * Encoder pretrained model OK * Update retriever comments * Update docs, tests, and imports * Prune unused models * Make embedder as a module `RealmEmbedder` * Add RealmRetrieverOutput * Update tokenization * Pass all tests in test_modeling_realm.py * Prune RealmModel * Update docs * Add training test. * Remove completed TODO * Style & Quality * Prune `RealmModel` * Fixup * Changes: 1. Remove RealmTokenizerFast 2. Update docstrings 3. Add a method to RealmTokenizer to handle candidates tokenization. * Fix up * Style * Add tokenization tests * Update `from_pretrained` tests * Apply suggestions * Style & Quality * Copy BERT model * Fix comment to avoid docstring copying * Make RealmBertModel private * Fix bug * Style * Basic QA * Save * Complete reader logits * Add searcher * Complete searcher & reader * Move block records init to constructor * Fix training bug * Add some outputs to RealmReader * Add finetuned checkpoint variable names parsing * Fix bug * Update REALM config * Add RealmForOpenQA * Update convert_tfrecord logits * Fix bugs * Complete imports * Update docs * Update naming * Add brute-force searcher * Pass realm model tests * Style * Exclude RealmReader from common tests * Fix * Fix * convert docs * up * up * more make style * up * upload * up * Fix * Update src/transformers/__init__.py * adapt testing * change modeling code * fix test * up * up * up * correct more * make retriever work * update * make style * finish main structure * Resolve merge conflict * Make everything work * Style * Fixup * Fixup * Update training test * fix retriever * remove hardcoded path * Fix * Fix modeling test * Update model links * Initial retrieval test * Fix modeling test * Complete retrieval tests * Fix * style * Fix tests * Fix docstring example * Minor fix of retrieval test * Update license headers and docs * Apply suggestions from code review * Style * Apply suggestions from code review * Add an example to RealmEmbedder * Fix Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
2022-01-18 20:24:13 +08:00
parent b25067d807
commit 22454ae492
20 changed files with 3624 additions and 0 deletions
--- a/README_zh-hans.md
+++ b/README_zh-hans.md
@@ -294,6 +294,7 @@ conda install -c huggingface transformers
 1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (来自 VinAI Research) 伴随论文 [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) 由 Dat Quoc Nguyen and Anh Tuan Nguyen 发布。
 1. **[ProphetNet](https://huggingface.co/docs/transformers/model_doc/prophetnet)** (来自 Microsoft Research) 伴随论文 [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) 由 Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou 发布。
 1. **[QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert)** (来自 NVIDIA) 伴随论文 [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) 由 Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius 发布。
+1. **[REALM](https://huggingface.co/transformers/master/model_doc/realm.html)** (来自 Google Research) 伴随论文 [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) 由 Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang 发布。
 1. **[Reformer](https://huggingface.co/docs/transformers/model_doc/reformer)** (来自 Google Research) 伴随论文 [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) 由 Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya 发布。
 1. **[RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert)** (来自 Google Research) 伴随论文 [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/pdf/2010.12821.pdf) 由 Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder 发布。
 1. **[RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta)** (来自 Facebook), 伴随论文 [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) 由 Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov 发布。