Tapas Large Finetuned Tabfact

🚀 TAPAS large model fine-tuned on Tabular Fact Checking (TabFact)

This model is a fine-tuned version of TAPAS large, designed for tabular fact checking. It can classify whether a sentence is supported or refuted by the contents of a table.

🚀 Quick Start

This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the tapas_tabfact_inter_masklm_large_reset checkpoint of the original Github repository. This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training, and then fine-tuned on TabFact. It uses relative position embeddings by default (i.e. resetting the position index at every cell of the table).

The other (non-default) version which can be used is the one with absolute position embeddings:

• no_reset, which corresponds to tapas_tabfact_inter_masklm_large

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.

✨ Features

• Two Versions: Offers a default version with relative position embeddings and a non - default version with absolute position embeddings.
• Self - Supervised Pretraining: Pretrained on raw tables and associated texts without human labeling, using masked language modeling and intermediate pre - training.
• Fine - Tuned for Tabular Fact Checking: Fine - tuned on the TabFact dataset for classifying sentence support or refutation based on table contents.

📚 Documentation

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 classification head on top of the pre - trained model, and then jointly train this randomly initialized classification head with the base model on TabFact.

Intended uses & limitations

You can use this model for classifying whether a sentence is supported or refuted by the contents of 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] Sentence [SEP] Flattened table [SEP]

Fine - tuning

The model was fine - tuned on 32 Cloud TPU v3 cores for 80,000 steps with maximum sequence length 512 and batch size of 512. In this setup, fine - tuning takes around 14 hours. The optimizer used is Adam with a learning rate of 2e - 5, and a warmup ratio of 0.05. See the paper for more details (appendix A2).

BibTeX entry and citation info

@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}
}

@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}
}

@inproceedings{2019TabFactA,
  title={TabFact : A Large-scale Dataset for Table-based Fact Verification},
  author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},
  booktitle = {International Conference on Learning Representations (ICLR)},
  address = {Addis Ababa, Ethiopia},
  month = {April},
  year = {2020}
}

📄 License

This model is licensed under the apache - 2.0 license.