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Add TAPEX (#16473)

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* Add TapexTokenizer

* Improve docstrings and provide option to provide answer

* Remove option for pretokenized inputs

* Add TAPEX to README

* Fix copies

* Remove option for pretokenized inputs

* Initial commit: add tapex fine-tuning examples on both table-based question answering and table-based fact verification.

* - Draft a README file for running the script and introducing some background.
- Remove unused code lines in tabfact script.
- Disable the deafult `pad_to_max_length` option which is memory-consuming.

* * Support `as_target_tokenizer` function for TapexTokenizer.
* Fix the do_lower_case behaviour of TapexTokenizer.
* Add unit tests for target scenarios and cased/uncased scenarios for both source and target.

* * Replace the label BartTokenizer with TapexTokenizer's as_target_tokenizer function.
* Fix typos in tapex example README.

* * fix the evaluation script - remove the property `task_name`

* * Make the label space more clear for tabfact tasks

* * Using a new fine-tuning script for tapex-base on tabfact.

* * Remove the lowercase code outside the tokenizer - we use the tokenizer to control whether do_lower_case
* Guarantee the hyper-parameter can be run without out-of-memory on 16GB card and report the new reproduced number on wikisql

* * Remove the default tokenizer_name option.
* Provide evaluation command.

* * Support for WikiTableQuestion dataset.

* Fix a typo in README.

* * Fix the datasets's key name in WikiTableQuestions

* Run make fixup and move test to folder

* Fix quality

* Apply suggestions from code review

* Apply suggestions from code review

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

* Apply suggestions from code review

* Apply suggestions from code review

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

* Apply some more suggestions from code review

* Improve docstrings

* Overwrite failing test

* Improve comment in example scripts

* Fix rebase

* Add TAPEX to Auto mapping

* Add TAPEX to auto config mappings

* Put TAPEX higher than BART in auto mapping

* Add TAPEX to doc tests

Co-authored-by: Niels Rogge <nielsrogge@Nielss-MBP.localdomain>
Co-authored-by: SivilTaram <qianlxc@outlook.com>
Co-authored-by: Niels Rogge <nielsrogge@nielss-mbp.home>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
This commit is contained in:
NielsRogge
2022-04-08 10:57:51 +02:00
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parent 33cb21150c
commit 4ef0abb738
24 changed files with 4850 additions and 1 deletions
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@@ -297,6 +297,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
1. **[T5](https://huggingface.co/docs/transformers/model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
1. **[TAPEX](https://huggingface.co/docs/transformers/main/model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
1. **[Transformer-XL](https://huggingface.co/docs/transformers/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.
1. **[TrOCR](https://huggingface.co/docs/transformers/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.
1. **[UniSpeech](https://huggingface.co/docs/transformers/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.