---
title: table-transformer-structure-recognition-v1.1-all
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/table-transformer-structure-recognition-v1.1-all"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/table-transformer-structure-recognition-v1.1-all.md"
repository: AI-ModelScope/table-transformer-structure-recognition-v1.1-all
last_updated: 2024-10-30
license: mit
pipeline_tag: document-understanding
tasks:
  - document-understanding
model_type:
  - table-transformer
architectures:
  - TableTransformerForObjectDetection
parameters: 28.8M
tensor_type:
  - F32
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 116
stars: 0
---

# table-transformer-structure-recognition-v1.1-all

> table-transformer-structure-recognition-v1.1-all - AI-ModelScope 在 ModelScope 开源的模型。Table Transformer (pre-trained for Table Structure Recognition)

AI-ModelScope/table-transformer-structure-recognition-v1.1-all 是 ModelScope 魔搭社区上的 28.8M 参数document-understanding模型，采用 mit 许可。

- **Repository**: AI-ModelScope/table-transformer-structure-recognition-v1.1-all
- **License**: mit
- **Tasks**: document-understanding
- **Parameters**: 28.8M
- **Downloads**: 116
- **Stars**: 0
- **Last updated**: 2024-10-30

Source: https://www.modelscope.cn/models/AI-ModelScope/table-transformer-structure-recognition-v1.1-all

---

# Table Transformer (pre-trained for Table Structure Recognition) 

Table Transformer (TATR) model trained on PubTables1M and FinTabNet.c. It was introduced in the paper [Aligning benchmark datasets for table structure recognition](https://arxiv.org/abs/2303.00716) by Smock et al. and first released in [this repository](https://github.com/microsoft/table-transformer). 

Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.

## Model description

The Table Transformer is equivalent to [DETR](https://huggingface.co/docs/transformers/model_doc/detr), a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention.

## Usage

You can use the raw model for detecting tables in documents. See the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/table-transformer) for more info.