Create README.md

README.md

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+---
+language: en
+tags:
+- omnitab
+datasets:
+- wikitablequestions
+---
+
+# OmniTab
+
+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).
+
+## Description
+
+`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).
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import pandas as pd
+
+tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-finetuned-wtq")
+model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-finetuned-wtq")
+
+data = {
+    "year": [1896, 1900, 1904, 2004, 2008, 2012],
+    "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
+}
+table = pd.DataFrame.from_dict(data)
+
+query = "In which year did beijing host the Olympic Games?"
+encoding = tokenizer(table=table, query=query, return_tensors="pt")
+
+outputs = model.generate(**encoding)
+
+print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
+# [' 2008']
+```
+
+## Reference
+
+```bibtex
+@inproceedings{jiang-etal-2022-omnitab,
+  title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering",
+  author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu",
+  booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
+  month = jul,
+  year = "2022",
+}
+```