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Put all models in the constants (#9170)

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* Put all models in the constants

* Add Google AI mention in the main README
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Sylvain Gugger 2020-12-17 11:23:21 -05:00 committed by GitHub
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README.md
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@ -222,7 +222,7 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
ultilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
1. **[SqueezeBert](https://huggingface.co/transformers/model_doc/squeezebert.html)** released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
1. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (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. **[TAPAS](https://huggingface.co/transformers/master/model_doc/tapas.html)** 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. **[TAPAS](https://huggingface.co/transformers/master/model_doc/tapas.html)** (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. **[Transformer-XL](https://huggingface.co/transformers/model_doc/transformerxl.html)** (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. **[XLM](https://huggingface.co/transformers/model_doc/xlm.html)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
1. **[XLM-ProphetNet](https://huggingface.co/transformers/model_doc/xlmprophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.

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docs/source/index.rst
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@ -176,9 +176,9 @@ and conversion utilities for the following models:
30. :doc:`T5 <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.
31. `TAPAS <https://huggingface.co/transformers/master/model_doc/tapas.html>`__ 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.
31. `TAPAS <https://huggingface.co/transformers/master/model_doc/tapas.html>`__ (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.
32. :doc:`Transformer-XL <model_doc/transformerxl>` (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.

67
src/transformers/models/tapas/tokenization_tapas.py
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@ -55,52 +55,41 @@ VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
# large models
"google/tapas-large-finetuned-sqa": "https://huggingface.co/google/tapas-large-finetuned-sqa/resolve/main/vocab.txt",
"google/tapas-large-finetuned-wtq": "https://huggingface.co/google/tapas-large-finetuned-wtq/resolve/main/vocab.txt",
"google/tapas-large-finetuned-wikisql-supervised": "https://huggingface.co/google/tapas-large-finetuned-wikisql-supervised/resolve/main/vocab.txt",
"google/tapas-large-finetuned-tabfact": "https://huggingface.co/google/tapas-large-finetuned-tabfact/resolve/main/vocab.txt",
# base models
"google/tapas-base-finetuned-sqa": "https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/vocab.txt",
"google/tapas-base-finetuned-wtq": "https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/vocab.txt",
"google/tapas-base-finetuned-wikisql-supervised": "https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/vocab.txt",
"google/tapas-base-finetuned-tabfact": "https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/vocab.txt",
# medium models
"google/tapas-medium-finetuned-sqa": "https://huggingface.co/google/tapas-medium-finetuned-sqa/resolve/main/vocab.txt",
"google/tapas-medium-finetuned-wtq": "https://huggingface.co/google/tapas-medium-finetuned-wtq/resolve/main/vocab.txt",
"google/tapas-medium-finetuned-wikisql-supervised": "https://huggingface.co/google/tapas-medium-finetuned-wikisql-supervised/resolve/main/vocab.txt",
"google/tapas-medium-finetuned-tabfact": "https://huggingface.co/google/tapas-medium-finetuned-tabfact/resolve/main/vocab.txt",
# small models
"google/tapas-small-finetuned-sqa": "https://huggingface.co/google/tapas-small-finetuned-sqa/resolve/main/vocab.txt",
"google/tapas-small-finetuned-wtq": "https://huggingface.co/google/tapas-small-finetuned-wtq/resolve/main/vocab.txt",
"google/tapas-small-finetuned-wikisql-supervised": "https://huggingface.co/google/tapas-small-finetuned-wikisql-supervised/resolve/main/vocab.txt",
"google/tapas-small-finetuned-tabfact": "https://huggingface.co/google/tapas-small-finetuned-tabfact/resolve/main/vocab.txt",
# tiny models
"google/tapas-tiny-finetuned-sqa": "https://huggingface.co/google/tapas-tiny-finetuned-sqa/resolve/main/vocab.txt",
"google/tapas-tiny-finetuned-wtq": "https://huggingface.co/google/tapas-tiny-finetuned-wtq/resolve/main/vocab.txt",
"google/tapas-tiny-finetuned-wikisql-supervised": "https://huggingface.co/google/tapas-tiny-finetuned-wikisql-supervised/resolve/main/vocab.txt",
"google/tapas-tiny-finetuned-tabfact": "https://huggingface.co/google/tapas-tiny-finetuned-tabfact/resolve/main/vocab.txt",
# mini models
"google/tapas-mini-finetuned-sqa": "https://huggingface.co/google/tapas-mini-finetuned-sqa/resolve/main/vocab.txt",
"google/tapas-mini-finetuned-wtq": "https://huggingface.co/google/tapas-mini-finetuned-wtq/resolve/main/vocab.txt",
"google/tapas-mini-finetuned-wikisql-supervised": "https://huggingface.co/google/tapas-mini-finetuned-wikisql-supervised/resolve/main/vocab.txt",
"google/tapas-mini-finetuned-tabfact": "https://huggingface.co/google/tapas-mini-finetuned-tabfact/resolve/main/vocab.txt",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
# large models
"google/tapas-large-finetuned-sqa": 512,
"google/tapas-large-finetuned-wtq": 512,
"google/tapas-large-finetuned-wikisql-supervised": 512,
"google/tapas-large-finetuned-tabfact": 512,
# base models
"google/tapas-base-finetuned-sqa": 512,
"google/tapas-base-finetuned-wtq": 512,
"google/tapas-base-finetuned-wikisql-supervised": 512,
"google/tapas-base-finetuned-tabfact": 512,
# medium models
"google/tapas-medium-finetuned-sqa": 512,
"google/tapas-medium-finetuned-wtq": 512,
"google/tapas-medium-finetuned-wikisql-supervised": 512,
"google/tapas-medium-finetuned-tabfact": 512,
# small models
"google/tapas-base-finetuned-sqa": 512,
"google/tapas-base-finetuned-wtq": 512,
"google/tapas-base-finetuned-wikisql-supervised": 512,
"google/tapas-base-finetuned-tabfact": 512,
# tiny models
"google/tapas-tiny-finetuned-sqa": 512,
"google/tapas-tiny-finetuned-wtq": 512,
"google/tapas-tiny-finetuned-wikisql-supervised": 512,
"google/tapas-tiny-finetuned-tabfact": 512,
# mini models
"google/tapas-mini-finetuned-sqa": 512,
"google/tapas-mini-finetuned-wtq": 512,
"google/tapas-mini-finetuned-wikisql-supervised": 512,
"google/tapas-mini-finetuned-tabfact": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"google/tapas-base-finetuned-sqa": {"do_lower_case": True},
"google/tapas-base-finetuned-wtq": {"do_lower_case": True},
"google/tapas-base-finetuned-wikisql-supervised": {"do_lower_case": True},
"google/tapas-base-finetuned-tabfact": {"do_lower_case": True},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {name: 512 for name in PRETRAINED_VOCAB_FILES_MAP.keys()}
PRETRAINED_INIT_CONFIGURATION = {name: {"do_lower_case": True} for name in PRETRAINED_VOCAB_FILES_MAP.keys()}
class TapasTruncationStrategy(ExplicitEnum):