table transformer structure recognition
简介
核心亮点
- 精准识别文档中的表格边界与内部行列结构
- 将非结构化表格图像转化为可处理的结构化数据
- 是构建高质量文档 RAG 系统的关键预处理组件
- 采用 MIT 协议开源，适配多种企业级部署场景
使用方法
# 安装 Hugging Face transformers
pip install transformers torch# 使用 transformers 加载模型
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("microsoft/table-transformer-structure-recognition")
tokenizer = AutoTokenizer.from_pretrained("microsoft/table-transformer-structure-recognition")Hugging Face 下载
我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。
操作指引：在下载前，请先通过如下命令安装 huggingface_hub：
pip install -U huggingface_hub命令行下载
下载完整模型库
huggingface-cli download microsoft/table-transformer-structure-recognition下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）
huggingface-cli download microsoft/table-transformer-structure-recognition config.json --local-dir ./dirSDK 下载
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('microsoft/table-transformer-structure-recognition')Git 下载
请确保 lfs 已经被正确安装
git lfs install
git clone https://huggingface.co/microsoft/table-transformer-structure-recognition如果您希望跳过 lfs 大文件下载，可以使用如下命令
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/microsoft/table-transformer-structure-recognition模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。
PyTorch / Transformers 使用
安装 Transformers
pip install -U transformers torch模型加载和推理
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('microsoft/table-transformer-structure-recognition')
tokenizer = AutoTokenizer.from_pretrained('microsoft/table-transformer-structure-recognition')模型下载
我们推荐使用命令行或者 ModelScope SDK 来进行模型的下载。
操作指引：在下载前，请先通过如下命令安装 ModelScope：
pip install modelscope命令行下载
下载完整模型库
modelscope download --model microsoft/table-transformer-structure-recognition下载单个文件到指定本地文件夹（以下载 README.md 到当前路径下 dir 目录为例）
modelscope download --model microsoft/table-transformer-structure-recognition README.md --local_dir ./dirSDK 下载
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('microsoft/table-transformer-structure-recognition')Git 下载
请确保 lfs 已经被正确安装
git lfs install
git clone https://www.modelscope.cn/microsoft/table-transformer-structure-recognition.git如果您希望跳过 lfs 大文件下载，可以使用如下命令
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/microsoft/table-transformer-structure-recognition.gitModelScope 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 快速开发
下载并安装 ModelScope library
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html模型加载和推理
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
p = pipeline('text-generation', 'microsoft/table-transformer-structure-recognition')完整文档
---
license: mit
widget:
- src: https://documentation.tricentis.com/tosca/1420/en/content/tbox/images/table.png
example_title: Table
---
Table Transformer (fine-tuned for Table Structure Recognition)
Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents by Smock et al. and first released in this repository.
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, 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 the structure (like rows, columns) in tables. See the documentation for more info.
