First draft of model card

README.md

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+---
+language: en
+tags:
+- tapas
+- question-answering
+license: apache-2.0
+datasets:
+- wikisql
+---
+
+# TAPAS small model fine-tuned on WikiSQL (in a supervised fashion)
+
+his model has 2 versions which can be used. The default version corresponds to the `tapas_wikisql_sqa_inter_masklm_small_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
+This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training, and then fine-tuned in a chain on [SQA](https://www.microsoft.com/en-us/download/details.aspx?id=54253), and [WikiSQL](https://github.com/salesforce/WikiSQL). It uses relative position embeddings (i.e. resetting the position index at every cell of the table).
+
+The other (non-default) version which can be used is: 
+- `no_reset`, which corresponds to `tapas_wikisql_sqa_inter_masklm_small` (intermediate pre-training, absolute position embeddings). 
+
+Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by
+the Hugging Face team and contributors.
+
+## Model description
+
+TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion. 
+This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it
+can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
+was pretrained with two objectives:
+
+- Masked language modeling (MLM): taking a (flattened) table and associated context, the model randomly masks 15% of the words in 
+  the input, then runs the entire (partially masked) sequence through the model. The model then has to predict the masked words. 
+  This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, 
+  or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional 
+  representation of a table and associated text.
+- Intermediate pre-training: to encourage numerical reasoning on tables, the authors additionally pre-trained the model by creating 
+  a balanced dataset of millions of syntactically created training examples. Here, the model must predict (classify) whether a sentence 
+  is supported or refuted by the contents of a table. The training examples are created based on synthetic as well as counterfactual statements.
+
+This way, the model learns an inner representation of the English language used in tables and associated texts, which can then be used 
+to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed
+or refuted by the contents of a table. Fine-tuning is done by adding a cell selection head and aggregation head on top of the pre-trained model, and then jointly train these randomly initialized classification heads with the base model on SQA and WikiSQL. 
+
+
+## Intended uses & limitations
+
+You can use this model for answering questions related to a table.
+
+For code examples, we refer to the documentation of TAPAS on the HuggingFace website. 
+
+
+## Training procedure
+
+### Preprocessing
+
+The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
+then of the form:
+
+```
+[CLS] Question [SEP] Flattened table [SEP]
+```
+
+The authors did first convert the WikiSQL dataset into the format of SQA using automatic conversion scripts.
+
+### Fine-tuning
+
+The model was fine-tuned on 32 Cloud TPU v3 cores for 50,000 steps with maximum sequence length 512 and batch size of 512.
+In this setup, fine-tuning takes around 10 hours. The optimizer used is Adam with a learning rate of 6.17164e-5, and a warmup 
+ratio of 0.1424. See the [paper](https://arxiv.org/abs/2004.02349) for more details (tables 11 and 12). 
+
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{herzig2020tapas,
+      title={TAPAS: Weakly Supervised Table Parsing via Pre-training}, 
+      author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos},
+      year={2020},
+      eprint={2004.02349},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR}
+}
+```
+
+```bibtex
+@misc{eisenschlos2020understanding,
+      title={Understanding tables with intermediate pre-training}, 
+      author={Julian Martin Eisenschlos and Syrine Krichene and Thomas Müller},
+      year={2020},
+      eprint={2010.00571},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+```bibtex
+@article{DBLP:journals/corr/abs-1709-00103,
+  author    = {Victor Zhong and
+               Caiming Xiong and
+               Richard Socher},
+  title     = {Seq2SQL: Generating Structured Queries from Natural Language using
+               Reinforcement Learning},
+  journal   = {CoRR},
+  volume    = {abs/1709.00103},
+  year      = {2017},
+  url       = {http://arxiv.org/abs/1709.00103},
+  archivePrefix = {arXiv},
+  eprint    = {1709.00103},
+  timestamp = {Mon, 13 Aug 2018 16:48:41 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-1709-00103.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```