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
Download textverifier_stage3.pth from ATH-MaaS/TextVerifier-CJK-CLIP: direct link, hf CLI and curl.
- Browser
- Download file 2.44 GB
-
https://huggingface.co/ATH-MaaS/TextVerifier-CJK-CLIP/resolve/main/textverifier_stage3.pth
- Command line
-
hf download hf://ATH-MaaS/TextVerifier-CJK-CLIP/textverifier_stage3.pth
-
curl -L -o textverifier_stage3.pth https://huggingface.co/ATH-MaaS/TextVerifier-CJK-CLIP/resolve/main/textverifier_stage3.pth
2.44 GB
- Xet hash:
- 0fb01da111ae58380917ccc3f54b826b848e5e26e54ff6cc4b8f0cff5ab0b304
- Size of remote file:
- 2.44 GB
- SHA256:
- ee6860f93debc240bfdef5572cd2ce9359c84e0c8e2552a4047919ba59780ef3
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