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