Image-to-Text
Transformers
Safetensors
mineru_diffusion
feature-extraction
ocr
document-understanding
vision-language-model
multimodal
trust-remote-code
mineru
custom_code
Eval Results
Instructions to use opendatalab/MinerU-Diffusion-V1-0320-2.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use opendatalab/MinerU-Diffusion-V1-0320-2.5B with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="opendatalab/MinerU-Diffusion-V1-0320-2.5B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("opendatalab/MinerU-Diffusion-V1-0320-2.5B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add ParseBench evaluation results
#2
by boyang-runllama - opened
This PR ensures your model shows up at https://huggingface.co/datasets/llamaindex/ParseBench.
This is based on the new evaluation results feature: https://huggingface.co/docs/hub/eval-results.
Note: this includes per-dimension performance across all 5 ParseBench dimensions (text_content, text_formatting, layout, chart, table) along with the overall mean score.