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--- |
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language: en |
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tags: |
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- table-question-answering |
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datasets: |
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- wikisql |
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--- |
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# ReasTAP |
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ReasTAP is a table reasoning model proposed in the EMNLP 2022 paper [ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples](https://arxiv.org/pdf/2210.12374.pdf). The original Github repository is [https://github.com/Yale-LILY/ReasTAP](https://github.com/Yale-LILY/ReasTAP). |
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## Description |
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`Yale-LILY/reastap-large-finetuned-wikisql` is initialized with `Yale-LILY/reastap-large` and finetuned on [WikiSQL](https://huggingface.co/datasets/wikisql). |
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## Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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import pandas as pd |
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tokenizer = AutoTokenizer.from_pretrained("Yale-LILY/reastap-large-finetuned-wikisql") |
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model = AutoModelForSeq2SeqLM.from_pretrained("Yale-LILY/reastap-large-finetuned-wikisql") |
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data = { |
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"year": [1896, 1900, 1904, 2004, 2008, 2012], |
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"city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] |
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} |
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table = pd.DataFrame.from_dict(data) |
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query = "In which year did beijing host the Olympic Games?" |
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encoding = tokenizer(table=table, query=query, return_tensors="pt") |
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outputs = model.generate(**encoding) |
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print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) |
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# [' 2008'] |
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``` |
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## Reference |
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```bibtex |
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@inproceedings{zhao-etal-2022-reastap, |
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title = "{R}eas{TAP}: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples", |
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author = "Zhao, Yilun and |
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Nan, Linyong and |
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Qi, Zhenting and |
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Zhang, Rui and |
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Radev, Dragomir", |
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booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", |
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month = dec, |
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year = "2022", |
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address = "Abu Dhabi, United Arab Emirates", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2022.emnlp-main.615", |
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pages = "9006--9018", |
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abstract = "Reasoning over tabular data requires both table structure understanding and a broad set of table reasoning skills. Current models with table-specific architectures and pre-training methods perform well on understanding table structures, but they still struggle with tasks that require various table reasoning skills. In this work, we develop ReasTAP to show that high-level table reasoning skills can be injected into models during pre-training without a complex table-specific architecture design. We define 7 table reasoning skills, such as numerical operation, temporal comparison, and conjunction. Each reasoning skill is associated with one example generator, which synthesizes questions over semi-structured tables according to the sampled templates. We model the table pre-training task as a sequence generation task and pre-train ReasTAP to generate precise answers of the synthetic examples. ReasTAP is evaluated on four benchmarks covering three downstream tasks including 1) WikiSQL-Weak and WikiTQ for Table Question Answering, 2) TabFact for Table Fact Verification, and 3) LogicNLG for Faithful Table-to-Text Generation. Experimental results demonstrate that ReasTAP achieves new state-of-the-art results on all of them and delivers a significant improvement under low-resource setting. Our code is publicly available at https://github.com/Yale-LILY/ReasTAP.", |
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} |
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``` |
