Tapex Base

🚀 TAPEX (base-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 applied to various table - related tasks.

🚀 Quick Start

TAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian - Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table - Pretraining).

✨ Features

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.

📚 Documentation

Intended Uses

You can use the raw model for simulating neural SQL execution, i.e., employ TAPEX to execute a SQL query on a given table. However, the model is mostly meant to be fine - tuned on a supervised dataset. Currently TAPEX can be fine - tuned to tackle table question answering tasks and table fact verification tasks. See the model hub to look for fine - tuned versions on a task that interests you.

💻 Usage Examples

Basic Usage

Here is how to use this model in transformers:

from transformers import TapexTokenizer, BartForConditionalGeneration
import pandas as pd

tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-base")
model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-base")

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 = "select year where city = beijing"
encoding = tokenizer(table=table, query=query, return_tensors="pt")

outputs = model.generate(**encoding)

print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
# ['2008']

Advanced Usage

Please find the fine - tuning script here.

📄 License

@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}
}