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  1. README.md +52 -0
  2. config.json +38 -0
  3. merges.txt +0 -0
  4. pytorch_model.bin +3 -0
  5. special_tokens_map.json +1 -0
  6. tokenizer_config.json +67 -0
  7. vocab.json +0 -0
README.md ADDED
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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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+
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+ # OmniTab
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+
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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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+
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+ ## Description
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+
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+ `neulab/omnitab-large-16shot-finetuned-wtq-16shot` (based on BART architecture) is initialized with `microsoft/tapex-large`, continuously pretrained on natural and synthetic data (SQL2NL model trained in the 16-shot setting), and fine-tuned on [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) in the 16-shot setting.
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+
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+ ## Usage
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+
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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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+
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+ tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-16shot-finetuned-wtq-16shot")
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+ model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-16shot-finetuned-wtq-16shot")
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+
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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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+
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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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+
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+ outputs = model.generate(**encoding)
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+
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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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+
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+ ## Reference
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+
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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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+ ```
config.json ADDED
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+ "BartForConditionalGeneration"
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+ "model_type": "bart",
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+ "scale_embedding": false,
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+ "torch_dtype": "float32",
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+ "use_cache": true,
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+ "vocab_size": 50265,
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+ "no_repeat_ngram_size": 3
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+ }
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vocab.json ADDED
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