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--- |
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language: en |
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tags: |
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- tapex |
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- table-question-answering |
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datasets: |
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- wikitablequestions |
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--- |
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# OmniTab |
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OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab). |
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## Description |
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`neulab/omnitab-large-1024shot` (based on BART architecture) is initialized with `microsoft/tapex-large` and continuously pretrained on natural and synthetic data (SQL2NL model trained in the 1024-shot setting). |
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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("neulab/omnitab-large-1024shot") |
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model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-1024shot") |
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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{jiang-etal-2022-omnitab, |
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title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering", |
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author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu", |
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booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", |
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month = jul, |
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year = "2022", |
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} |
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``` |
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