[Umt5] Add google's umt5 to transformers (#24477)
* add tokenization template * update conversion script * update modeling code * update * update convert checkpoint * update modeling * revert changes on convert script * new conversion script for new format * correct position bias * cleaning a bit * Credit co authors Co-authored-by: agemagician <ahmed.elnaggar@tum.de> Co-authored-by: stefan-it <> * styling * Add docq * fix copies * add co author * Other Author * Merge branch 'main' of https://github.com/huggingface/transformers into add-umt5 * add testing * nit * Update docs/source/en/model_doc/umt5.mdx Co-authored-by: Stefan Schweter <stefan@schweter.it> * fix t5 * actual fix? * revert wrong changes * remove * update test * more fixes * revert some changes * add SPIECE_UNDERLINE * add a commone xample * upfate * fix copies * revert changes on t5 conversion script * revert bytefallback changes since there was no addition yet * fixup * fixup * ingore umt5 cutom testing folder * fix readmes * revertT5 changes * same outputs * fixup * update example * Apply suggestions from code review * style * draft addition of all new files * current update * fix attention and stuff * finish refactoring * auto config * fixup * more nits * add umt5 to init * use md format * Update README.md Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * revert changes on mt5 * revert mt4 changes * update test * more fixes * add to mapping * fix-copies * fix copies * foix retain grad * fix some tests * nits * done * Update src/transformers/models/umt5/modeling_umt5.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update docs/source/en/model_doc/umt5.md * Update src/transformers/models/umt5/__init__.py * Update docs/source/en/model_doc/umt5.md Co-authored-by: Stefan Schweter <stefan@schweter.it> * Update src/transformers/models/umt5/modeling_umt5.py * update conversion script + use google checkpoints * nits * update test and modelling * stash slow convert * update fixupd * don't change slow --------- Co-authored-by: stefan-it <> Co-authored-by: Stefan Schweter <stefan@schweter.it> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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@@ -173,6 +173,7 @@ Die Bibliothek enthält derzeit JAX-, PyTorch- und TensorFlow-Implementierungen,
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1. **[Transformer-XL](model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
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1. **[TrOCR](model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
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1. **[UL2](model_doc/ul2)** (from Google Research) released with the paper [Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131v1) by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler
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1. **[UMT5](model_doc/umt5)** (from Google Research) released with the paper [UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining](https://openreview.net/forum?id=kXwdL1cWOAi) by Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant.
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1. **[UniSpeech](model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
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1. **[UniSpeechSat](model_doc/unispeech-sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
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1. **[VAN](model_doc/van)** (from Tsinghua University and Nankai University) released with the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
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