Image-to-Text
Transformers
PyTorch
TensorBoard
English
mplug_owl2
feature-extraction
image-quality-assessment
document-quality
mplug-owl2
vision-language
document-analysis
color-quality
IQA
custom_code
Instructions to use mapo80/DeQA-Doc-Color with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mapo80/DeQA-Doc-Color 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-Color", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mapo80/DeQA-Doc-Color", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from mapo80/DeQA-Doc-Color: direct link, hf CLI and curl.
- Browser
- Download file 500 kB
-
https://huggingface.co/mapo80/DeQA-Doc-Color/resolve/main/tokenizer.model
- Command line
-
hf download hf://mapo80/DeQA-Doc-Color/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/mapo80/DeQA-Doc-Color/resolve/main/tokenizer.model
500 kB
- Xet hash:
- 91bf184ab12793d0754344f9095332759432e666320cc6c07f637af50e36db6f
- Size of remote file:
- 500 kB
- SHA256:
- 9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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