Image-to-Text
Transformers
PyTorch
English
mplug_owl2
feature-extraction
image-quality-assessment
document-quality
mplug-owl2
vision-language
document-analysis
IQA
custom_code
Instructions to use mapo80/DeQA-Doc-Overall with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mapo80/DeQA-Doc-Overall 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="mapo80/DeQA-Doc-Overall", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mapo80/DeQA-Doc-Overall", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ebd9a21fab51ff963506f712185ca64c1fa7519bfe1872372d5e1748778e3523
- Size of remote file:
- 6.72 kB
- SHA256:
- 51d805460b6a7dbf539c3f7c60513e90d4d3c9098e378b5fe48a816654060a98
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