| | --- |
| | language: |
| | - zh |
| | - en |
| | license: apache-2.0 |
| | library_name: transformers |
| | pipeline_tag: feature-extraction |
| | tags: |
| | - pdf-to-markdown |
| | - feature-extraction |
| | --- |
| | |
| | # MinerU PDF to Markdown Model |
| |
|
| | 这个模型可以将PDF文档转换为Markdown格式。 |
| |
|
| | ## Model Description |
| |
|
| | MinerU使用多模型组合架构： |
| | - Layout: 文档布局分析 (Detectron2) |
| | - MFD: 数学公式检测 (PyTorch) |
| | - MFR: 数学公式识别 (BERT-based) |
| | - TabRec: 表格识别与重建 (T5-based) |
| |
|
| | ## Intended Uses |
| |
|
| | 本模型用于将PDF文档自动转换为Markdown格式，支持： |
| | - 文本布局分析 |
| | - 数学公式识别 |
| | - 表格结构重建 |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | from transformers import pipeline |
| | |
| | converter = pipeline("document-conversion", model="kitjesen/MinerU") |
| | markdown = converter("document.pdf") |
| | ``` |
| |
|
| | ## Limitations and Bias |
| |
|
| | - 最大支持页数：100页 |
| | - PDF文件大小限制：50MB |
| | - 支持语言：中文、英文 |
| |
|
| | ## Training Data |
| |
|
| | 模型使用以下数据训练： |
| | - 学术论文数据集 |
| | - 教材文档数据集 |
| | - 技术文档数据集 |
| |
|
| | ## Training Procedure |
| |
|
| | 使用多阶段训练流程： |
| | 1. 预训练各个子模型 |
| | 2. 联合训练优化 |
| | 3. 端到端微调 |
| |
|
| | ## Evaluation Results |
| |
|
| | - 文本识别准确率：95% |
| | - 公式识别准确率：90% |
| | - 表格重建准确率：85% |
| |
|
| | ## Environmental Impact |
| |
|
| | - 硬件要求：GPU with 8GB+ VRAM |
| | - 推理时间：~2s/页 |
| |
|
| | ## Technical Specifications |
| |
|
| | **Model Architecture** |
| | - Layout: Detectron2 (FasterRCNN) |
| | - MFD: Custom CNN |
| | - MFR: BERT-based |
| | - TabRec: T5-based |
| |
|
| | **Hardware Requirements** |
| | - RAM: 16GB+ |
| | - GPU: 8GB+ VRAM |
| | - Storage: 5GB |
| |
|
| | **Software Requirements** |
| | - Python >= 3.7 |
| | - PyTorch >= 1.9.0 |
| | - transformers >= 4.28.0 |
| | - detectron2 |
