Image Classification
PyTorch
timm
Transformers
text-rendering
text-to-image
chinese
cjk
glyph
clip
vit
deberta-v2
ids
quality-assurance
reward-model
Instructions to use ATH-MaaS/TextVerifier-CJK-CLIP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use ATH-MaaS/TextVerifier-CJK-CLIP with timm:
import timm model = timm.create_model("hf-hub:ATH-MaaS/TextVerifier-CJK-CLIP", pretrained=True) - Transformers
How to use ATH-MaaS/TextVerifier-CJK-CLIP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ATH-MaaS/TextVerifier-CJK-CLIP") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ATH-MaaS/TextVerifier-CJK-CLIP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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