---
title: tapex-large
canonical_url: "https://www.modelscope.cn/models/microsoft/tapex-large"
md_url: "https://www.modelscope.cn/models/microsoft/tapex-large.md"
repository: microsoft/tapex-large
last_updated: 2025-07-21
license: mit
pipeline_tag: table-question-answering
tasks:
  - table-question-answering
model_type:
  - bart
architectures:
  - BartForConditionalGeneration
parameters: 406.3M
tensor_type:
  - F32
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
inference_backends:
  - "deploy_task text/emb"
  - "vllm 0.9.2"
downloads: 169
stars: 0
tags:
  - tapex
  - table-question-answering
---

# tapex-large

> tapex-large - microsoft 在 ModelScope 开源的模型。TAPEX (large-sized model)

microsoft/tapex-large 是 ModelScope 魔搭社区上的 406.3M 参数table-question-answering模型，采用 mit 许可，可用 deploy_task text/emb、vllm 0.9.2 部署。

- **Repository**: microsoft/tapex-large
- **License**: mit
- **Tasks**: table-question-answering
- **Parameters**: 406.3M
- **Inference backends**: deploy_task text/emb, vllm 0.9.2
- **Tags**: tapex, table-question-answering
- **Downloads**: 169
- **Stars**: 0
- **Last updated**: 2025-07-21

Source: https://www.modelscope.cn/models/microsoft/tapex-large

---

# TAPEX (large-sized model) 

TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) 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).

## Model description

TAPEX (**Ta**ble **P**re-training via **Ex**ecution) 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.

## Intended Uses

⚠️ This model checkpoint is **ONLY** used for fine-tuining on downstream tasks, and you **CANNOT** use this model for simulating neural SQL execution, i.e., employ TAPEX to execute a SQL query on a given table. The one that can neurally execute SQL queries is at [here](https://huggingface.co/microsoft/tapex-large-sql-execution).
> This separation of two models for two kinds of intention is because of a known issue in BART large, and we recommend readers to see [this comment](https://github.com/huggingface/transformers/issues/15559#issuecomment-1062880564) for more details.

### How to Fine-tuning

Please find the fine-tuning script [here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/tapex).

### 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}
}
```