Tapex Large Finetuned Wtq

🚀 TAPEX (large-sized model)

TAPEX is a pre - training approach that endows existing models with table reasoning skills. It was proposed in a research paper and can be used for complex table question - answering tasks.

✨ Features

• Table Reasoning Empowerment: TAPEX enables existing models to acquire table reasoning skills through a pre - training approach.
• Based on BART Architecture: It builds on the BART architecture, a transformer encoder - decoder (seq2seq) model with a bidirectional encoder and an autoregressive decoder.
• Fine - tuned on Dataset: This tapex - base model is fine - tuned on the WikiTableQuestions dataset.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import TapexTokenizer, BartForConditionalGeneration
import pandas as pd

tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-large-finetuned-wtq")
model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-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)

# tapex accepts uncased input since it is pre-trained on the uncased corpus
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))
# [' 2008.0']

📚 Documentation

Model description

TAPEX (Table Pre - training via Execution) is a conceptually simple and empirically powerful pre - training approach to empower existing models with table reasoning skills. TAPEX realizes table pre - training by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesizing executable SQL queries.

TAPEX is based on the BART architecture, the transformer encoder - decoder (seq2seq) model with a bidirectional (BERT - like) encoder and an autoregressive (GPT - like) decoder.

This model is the tapex - base model fine - tuned on the WikiTableQuestions dataset.

Intended Uses

You can use the model for table question answering on complex questions. Some solveable questions are shown below (corresponding tables now shown):

How to Eval

Please find the eval script here.

BibTeX entry and citation info

@inproceedings{
    liu2022tapex,
    title={{TAPEX}: Table Pre-training via Learning a Neural {SQL} Executor},
    author={Qian Liu and Bei Chen and Jiaqi Guo and Morteza Ziyadi and Zeqi Lin and Weizhu Chen and Jian-Guang Lou},
    booktitle={International Conference on Learning Representations},
    year={2022},
    url={https://openreview.net/forum?id=O50443AsCP}
}

📄 License

This project is licensed under the MIT license.