From 4e134ddf6f3b0065014c45e6c864cc7cf4bf34ad Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Tue, 15 Dec 2020 13:26:06 +0000 Subject: [PATCH] Add first draft of model card --- README.md | 105 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 105 insertions(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 0000000..0481bb2 --- /dev/null +++ b/README.md @@ -0,0 +1,105 @@ +--- +language: en +tags: +- tapas +- question-answering +license: apache-2.0 +datasets: +- sqa +--- + +# TAPAS base model fine-tuned on Sequential Question Answering (SQA) + +This model has 2 versions which can be used. The default version corresponds to the `tapas_sqa_inter_masklm_base_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 on [SQA](https://www.microsoft.com/en-us/download/details.aspx?id=54253). 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_sqa_inter_masklm_base` (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 on top of the pre-trained model, and then jointly +train this randomly initialized classification head with the base model on SQA. + + +## Intended uses & limitations + +You can use this model for answering questions related to a table in a conversational set-up. + +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] +``` + +### Fine-tuning + +The model was fine-tuned on 32 Cloud TPU v3 cores for 200,000 steps with maximum sequence length 512 and batch size of 128. +In this setup, fine-tuning takes around 20 hours. The optimizer used is Adam with a learning rate of 1.25e-5, and a warmup ratio +of 0.2. An inductive bias is added such that the model only selects cells of the same column. This is reflected by the +`select_one_column` parameter of `TapasConfig`. See also table 12 of the [original paper](https://arxiv.org/abs/2004.02349). + + +### 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 +@InProceedings{iyyer2017search-based, +author = {Iyyer, Mohit and Yih, Scott Wen-tau and Chang, Ming-Wei}, +title = {Search-based Neural Structured Learning for Sequential Question Answering}, +booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics}, +year = {2017}, +month = {July}, +abstract = {Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We collect a dataset of 6,066 question sequences that inquire about semi-structured tables from Wikipedia, with 17,553 question-answer pairs in total. To solve this sequential question answering task, we propose a novel dynamic neural semantic parsing framework trained using a weakly supervised reward-guided search. Our model effectively leverages the sequential context to outperform state-of-the-art QA systems that are designed to answer highly complex questions.}, +publisher = {Association for Computational Linguistics}, +url = {https://www.microsoft.com/en-us/research/publication/search-based-neural-structured-learning-sequential-question-answering/}, +} +``` \ No newline at end of file