1 --- 2 language: en 3 tags: 4 - tapas 5 - table-question-answering 6 license: apache-2.0 7 datasets: 8 - wtq 9 --- 10 11 # TAPAS tiny model fine-tuned on WikiTable Questions (WTQ) 12 13 This model has 2 versions which can be used. The default version corresponds to the tapas_wtq_wikisql_sqa_inter_masklm_tiny_reset checkpoint of the [original Github repository](https://github.com/google-research/tapas). 14 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), [WikiSQL](https://github.com/salesforce/WikiSQL) and finally [WTQ](https://github.com/ppasupat/WikiTableQuestions). It uses relative position embeddings (i.e. resetting the position index at every cell of the table). 15 16 The other (non-default) version which can be used is:  17 - no_reset, which corresponds to tapas_wtq_wikisql_sqa_inter_masklm_tiny (intermediate pre-training, absolute position embeddings).  18 19 Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by 20 the Hugging Face team and contributors. 21 22 ## Results 23 24 Size | Reset | Dev Accuracy | Link 25 -------- | --------| -------- | ---- 26 LARGE | noreset | 0.5062 | [tapas-large-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-large-finetuned-wtq/tree/no_reset) 27 LARGE | reset | 0.5097 | [tapas-large-finetuned-wtq](https://huggingface.co/google/tapas-large-finetuned-wtq/tree/main) 28 BASE | noreset | 0.4525 | [tapas-base-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-base-finetuned-wtq/tree/no_reset) 29 BASE | reset | 0.4638 | [tapas-base-finetuned-wtq](https://huggingface.co/google/tapas-base-finetuned-wtq/tree/main) 30 MEDIUM | noreset | 0.4324 | [tapas-medium-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-medium-finetuned-wtq/tree/no_reset) 31 MEDIUM | reset | 0.4324 | [tapas-medium-finetuned-wtq](https://huggingface.co/google/tapas-medium-finetuned-wtq/tree/main) 32 SMALL | noreset | 0.3681 | [tapas-small-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-small-finetuned-wtq/tree/no_reset) 33 SMALL | reset | 0.3762 | [tapas-small-finetuned-wtq](https://huggingface.co/google/tapas-small-finetuned-wtq/tree/main) 34 MINI | noreset | 0.2783 | [tapas-mini-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-mini-finetuned-wtq/tree/no_reset) 35 MINI | reset | 0.2854 | [tapas-mini-finetuned-wtq](https://huggingface.co/google/tapas-mini-finetuned-wtq/tree/main) 36 **TINY** | **noreset** | **0.0823** | [tapas-tiny-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-tiny-finetuned-wtq/tree/no_reset) 37 **TINY** | **reset** | **0.1039** | [tapas-tiny-finetuned-wtq](https://huggingface.co/google/tapas-tiny-finetuned-wtq/tree/main) 38 39 ## Model description 40 41 TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion.  42 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 43 can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it 44 was pretrained with two objectives: 45 46 - Masked language modeling (MLM): taking a (flattened) table and associated context, the model randomly masks 15% of the words in  47  the input, then runs the entire (partially masked) sequence through the model. The model then has to predict the masked words.  48  This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other,  49  or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional  50  representation of a table and associated text. 51 - Intermediate pre-training: to encourage numerical reasoning on tables, the authors additionally pre-trained the model by creating  52  a balanced dataset of millions of syntactically created training examples. Here, the model must predict (classify) whether a sentence  53  is supported or refuted by the contents of a table. The training examples are created based on synthetic as well as counterfactual statements. 54 55 This way, the model learns an inner representation of the English language used in tables and associated texts, which can then be used  56 to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed 57 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, WikiSQL and finally WTQ.  58 59 60 ## Intended uses & limitations 61 62 You can use this model for answering questions related to a table. 63 64 For code examples, we refer to the documentation of TAPAS on the HuggingFace website.  65 66 67 ## Training procedure 68 69 ### Preprocessing 70 71 The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are 72 then of the form: 73 74  75 [CLS] Question [SEP] Flattened table [SEP] 76  77 78 The authors did first convert the WTQ dataset into the format of SQA using automatic conversion scripts. 79 80 ### Fine-tuning 81 82 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. 83 In this setup, fine-tuning takes around 10 hours. The optimizer used is Adam with a learning rate of 1.93581e-5, and a warmup  84 ratio of 0.128960. An inductive bias is added such that the model only selects cells of the same column. This is reflected by the  85 select_one_column parameter of TapasConfig. See the [paper](https://arxiv.org/abs/2004.02349) for more details (tables 11 and 86 12).  87 88 89 ### BibTeX entry and citation info 90 91 bibtex 92 @misc{herzig2020tapas, 93  title={TAPAS: Weakly Supervised Table Parsing via Pre-training},  94  author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos}, 95  year={2020}, 96  eprint={2004.02349}, 97  archivePrefix={arXiv}, 98  primaryClass={cs.IR} 99 } 100  101 102 bibtex 103 @misc{eisenschlos2020understanding, 104  title={Understanding tables with intermediate pre-training},  105  author={Julian Martin Eisenschlos and Syrine Krichene and Thomas Müller}, 106  year={2020}, 107  eprint={2010.00571}, 108  archivePrefix={arXiv}, 109  primaryClass={cs.CL} 110 } 111  112 113 bibtex 114 @article{DBLP:journals/corr/PasupatL15, 115  author = {Panupong Pasupat and 116  Percy Liang}, 117  title = {Compositional Semantic Parsing on Semi-Structured Tables}, 118  journal = {CoRR}, 119  volume = {abs/1508.00305}, 120  year = {2015}, 121  url = {http://arxiv.org/abs/1508.00305}, 122  archivePrefix = {arXiv}, 123  eprint = {1508.00305}, 124  timestamp = {Mon, 13 Aug 2018 16:47:37 +0200}, 125  biburl = {https://dblp.org/rec/journals/corr/PasupatL15.bib}, 126  bibsource = {dblp computer science bibliography, https://dblp.org} 127 } 128