Instructions to use OFA-Sys/chinese-clip-vit-base-patch16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OFA-Sys/chinese-clip-vit-base-patch16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="OFA-Sys/chinese-clip-vit-base-patch16") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("OFA-Sys/chinese-clip-vit-base-patch16") model = AutoModelForZeroShotImageClassification.from_pretrained("OFA-Sys/chinese-clip-vit-base-patch16") - Notebooks
- Google Colab
- Kaggle
大佬咋训练的
#4 opened about 1 year ago
by
allenliuvip
Adding `safetensors` variant of this model and full `tokenizer.json`
1
#3 opened over 1 year ago
by
sethwen
使用该模型时跳出无法加载tokenizer的错误
1
#2 opened over 2 years ago
by
IF-chan
Adding `safetensors` variant of this model
#1 opened over 3 years ago
by
SFconvertbot