Omnitab Large Finetuned Wtq

🚀 OmniTab

OmniTab是一個基於表格的問答模型，該模型在論文OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering中被提出。原始的GitHub倉庫為 https://github.com/jzbjyb/OmniTab。它旨在解決基於表格的問答任務，利用自然和合成數據進行預訓練，以實現少樣本學習下的高效問答。

🚀 快速開始

neulab/omnitab-large-finetuned-wtq（基於BART架構）以 neulab/omnitab-large 為初始模型，並在 WikiTableQuestions 數據集上進行了微調。

💻 使用示例

基礎用法

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']

📚 詳細文檔

引用信息

@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",
}

模型相關信息