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Unverified Commit 6d2e864d authored by Sylvain Gugger's avatar Sylvain Gugger Committed by GitHub
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Put all models in the constants (#9170) 

* Put all models in the constants

* Add Google AI mention in the main README
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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. ...@@ -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. 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. **[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. **[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. **[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](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. 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: ...@@ -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 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 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. 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 31. `TAPAS <https://huggingface.co/transformers/master/model_doc/tapas.html>`__ (from Google AI) released with the
Supervised Table Parsing via Pre-training <https://arxiv.org/abs/2004.02349>`__ by Jonathan Herzig, Paweł Krzysztof paper `TAPAS: Weakly Supervised Table Parsing via Pre-training <https://arxiv.org/abs/2004.02349>`__ by Jonathan
Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos. 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: 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*, 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. Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
......
src/transformers/models/tapas/tokenization_tapas.py
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...@@ -55,52 +55,41 @@ VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"} ...@@ -55,52 +55,41 @@ VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = { PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": { "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-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-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-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", "google/tapas-base-finetuned-tabfact": "https://huggingface.co/google/tapas-base-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 # medium models
"google/tapas-medium-finetuned-sqa": 512, "google/tapas-medium-finetuned-sqa": "https://huggingface.co/google/tapas-medium-finetuned-sqa/resolve/main/vocab.txt",
"google/tapas-medium-finetuned-wtq": 512, "google/tapas-medium-finetuned-wtq": "https://huggingface.co/google/tapas-medium-finetuned-wtq/resolve/main/vocab.txt",
"google/tapas-medium-finetuned-wikisql-supervised": 512, "google/tapas-medium-finetuned-wikisql-supervised": "https://huggingface.co/google/tapas-medium-finetuned-wikisql-supervised/resolve/main/vocab.txt",
"google/tapas-medium-finetuned-tabfact": 512, "google/tapas-medium-finetuned-tabfact": "https://huggingface.co/google/tapas-medium-finetuned-tabfact/resolve/main/vocab.txt",
# small models # small models
"google/tapas-base-finetuned-sqa": 512, "google/tapas-small-finetuned-sqa": "https://huggingface.co/google/tapas-small-finetuned-sqa/resolve/main/vocab.txt",
"google/tapas-base-finetuned-wtq": 512, "google/tapas-small-finetuned-wtq": "https://huggingface.co/google/tapas-small-finetuned-wtq/resolve/main/vocab.txt",
"google/tapas-base-finetuned-wikisql-supervised": 512, "google/tapas-small-finetuned-wikisql-supervised": "https://huggingface.co/google/tapas-small-finetuned-wikisql-supervised/resolve/main/vocab.txt",
"google/tapas-base-finetuned-tabfact": 512, "google/tapas-small-finetuned-tabfact": "https://huggingface.co/google/tapas-small-finetuned-tabfact/resolve/main/vocab.txt",
# tiny models # tiny models
"google/tapas-tiny-finetuned-sqa": 512, "google/tapas-tiny-finetuned-sqa": "https://huggingface.co/google/tapas-tiny-finetuned-sqa/resolve/main/vocab.txt",
"google/tapas-tiny-finetuned-wtq": 512, "google/tapas-tiny-finetuned-wtq": "https://huggingface.co/google/tapas-tiny-finetuned-wtq/resolve/main/vocab.txt",
"google/tapas-tiny-finetuned-wikisql-supervised": 512, "google/tapas-tiny-finetuned-wikisql-supervised": "https://huggingface.co/google/tapas-tiny-finetuned-wikisql-supervised/resolve/main/vocab.txt",
"google/tapas-tiny-finetuned-tabfact": 512, "google/tapas-tiny-finetuned-tabfact": "https://huggingface.co/google/tapas-tiny-finetuned-tabfact/resolve/main/vocab.txt",
# mini models # mini models
"google/tapas-mini-finetuned-sqa": 512, "google/tapas-mini-finetuned-sqa": "https://huggingface.co/google/tapas-mini-finetuned-sqa/resolve/main/vocab.txt",
"google/tapas-mini-finetuned-wtq": 512, "google/tapas-mini-finetuned-wtq": "https://huggingface.co/google/tapas-mini-finetuned-wtq/resolve/main/vocab.txt",
"google/tapas-mini-finetuned-wikisql-supervised": 512, "google/tapas-mini-finetuned-wikisql-supervised": "https://huggingface.co/google/tapas-mini-finetuned-wikisql-supervised/resolve/main/vocab.txt",
"google/tapas-mini-finetuned-tabfact": 512, "google/tapas-mini-finetuned-tabfact": "https://huggingface.co/google/tapas-mini-finetuned-tabfact/resolve/main/vocab.txt",
}
} }
PRETRAINED_INIT_CONFIGURATION = { PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {name: 512 for name in PRETRAINED_VOCAB_FILES_MAP.keys()}
"google/tapas-base-finetuned-sqa": {"do_lower_case": True}, PRETRAINED_INIT_CONFIGURATION = {name: {"do_lower_case": True} for name in PRETRAINED_VOCAB_FILES_MAP.keys()}
"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},
}
class TapasTruncationStrategy(ExplicitEnum): class TapasTruncationStrategy(ExplicitEnum):
......
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