donut base finetuned docvqa
简介
核心亮点
- 端到端识别，无需依赖外部 OCR 插件
- 专注于文档问答，精准提取图像内关键信息
- 推理链路短，部署成本低且响应速度快
- 采用 Apache-2.0 协议，企业级应用无压力
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("Xenova/donut-base-finetuned-docvqa")
tokenizer = AutoTokenizer.from_pretrained("Xenova/donut-base-finetuned-docvqa")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 Xenova/donut-base-finetuned-docvqa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 Xenova/donut-base-finetuned-docvqa config.json --local-dir ./dirSDK 下载
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('Xenova/donut-base-finetuned-docvqa')Git 下载
请确保 lfs 已经被正确安装
git lfs install
git clone https://huggingface.co/Xenova/donut-base-finetuned-docvqa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/Xenova/donut-base-finetuned-docvqa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('Xenova/donut-base-finetuned-docvqa')
tokenizer = AutoTokenizer.from_pretrained('Xenova/donut-base-finetuned-docvqa')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 Xenova/donut-base-finetuned-docvqa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 Xenova/donut-base-finetuned-docvqa README.md --local_dir ./dirSDK 下载
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('Xenova/donut-base-finetuned-docvqa')Git 下载
请确保 lfs 已经被正确安装
git lfs install
git clone https://www.modelscope.cn/Xenova/donut-base-finetuned-docvqa.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/Xenova/donut-base-finetuned-docvqa.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', 'Xenova/donut-base-finetuned-docvqa')完整文档
---
base_model: naver-clova-ix/donut-base-finetuned-docvqa
library_name: transformers.js
pipeline_tag: document-question-answering
tags:
- donut
- image-to-text
- vision
- donut-swin
---
https://huggingface.co/naver-clova-ix/donut-base-finetuned-docvqa with ONNX weights to be compatible with Transformers.js.
Usage (Transformers.js)
If you haven't already, you can install the Transformers.js JavaScript library from NPM using:
npm i @huggingface/transformersExample: Answer questions about a document with Xenova/donut-base-finetuned-docvqa.
import { pipeline } from '@huggingface/transformers';
// Create a document question answering pipeline
const qa_pipeline = await pipeline('document-question-answering', 'Xenova/donut-base-finetuned-docvqa');
// Generate an answer for a given image and question
const image = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/invoice.png';
const question = 'What is the invoice number?';
const output = await qa_pipeline(image, question);
// [{ answer: 'us-001' }]
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).
