tapex large finetuned tabfact

Providermicrosoft
Categorytable-question-answering
Licensemit
Downloads163
Stars0

Overview

The TaPEX Large model, fine-tuned on the TabFact dataset, is a specialized transformer designed specifically for table-based question answering and fact verification. Unlike general-purpose LLMs that often struggle with structural data, TaPEX treats tables as first-class citizens, allowing it to reason across rows and columns to verify if a given claim is supported by the provided tabular data. For developers, this means higher accuracy in data-validation pipelines and reduced hallucination when querying structured datasets. It integrates well into RAG workflows where tabular evidence must be strictly verified before being presented to the end-user, offering a more reliable alternative to prompting a general model to 'read' a CSV or HTML table.

Highlights

  • Optimized for tabular fact verification and question answering
  • Reduced hallucinations compared to general-purpose LLMs
  • Efficiently handles structured data reasoning and evidence retrieval
  • MIT licensed for flexible commercial and private integration

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("microsoft/tapex-large-finetuned-tabfact")
tokenizer = AutoTokenizer.from_pretrained("microsoft/tapex-large-finetuned-tabfact")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download microsoft/tapex-large-finetuned-tabfact

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download microsoft/tapex-large-finetuned-tabfact config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('microsoft/tapex-large-finetuned-tabfact')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/microsoft/tapex-large-finetuned-tabfact

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/microsoft/tapex-large-finetuned-tabfact

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('microsoft/tapex-large-finetuned-tabfact')
tokenizer = AutoTokenizer.from_pretrained('microsoft/tapex-large-finetuned-tabfact')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance 
pip install modelscope

CLI Download

Download the full repository

Download the full repository 
modelscope download --model microsoft/tapex-large-finetuned-tabfact

Download a single file to a local folder (e.g. README.md into ./dir)

Download a single file to a local folder (e.g. README.md into ./dir) 
modelscope download --model microsoft/tapex-large-finetuned-tabfact README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('microsoft/tapex-large-finetuned-tabfact')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://www.modelscope.cn/microsoft/tapex-large-finetuned-tabfact.git

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/microsoft/tapex-large-finetuned-tabfact.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

Install the ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

Load the model and run inference 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'microsoft/tapex-large-finetuned-tabfact')

Full Documentation

来源: HuggingFace

---
language: en
tags:

  • tapex

  • table-question-answering

datasets:
  • tab_fact

license: mit
---

TAPEX (large-sized model)

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.

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-encoder (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 Tabfact dataset.

Intended Uses

You can use the model for table fact verficiation.

How to Use

Here is how to use this model in transformers:

python
from transformers import TapexTokenizer, BartForSequenceClassification
import pandas as pd

tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-large-finetuned-tabfact")
model = BartForSequenceClassification.from_pretrained("microsoft/tapex-large-finetuned-tabfact")


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 = "beijing hosts the olympic games in 2012"
encoding = tokenizer(table=table, query=query, return_tensors="pt")

outputs = model(**encoding)
output_id = int(outputs.logits[0].argmax(dim=0))
print(model.config.id2label[output_id])
Refused

How to Eval

Please find the eval script here.

BibTeX entry and citation info

bibtex
@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}
}
View official source
Join our Telegram