Create README.md
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language: en
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tags:
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- omnitab
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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-finetuned-wtq` (based on BART architecture) is initialized with `microsoft/tapex-large`, continuously pretrained on natural and synthetic data, and fine-tuned on [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions).
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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-finetuned-wtq")
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model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-finetuned-wtq")
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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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