Summarization
Transformers
TensorFlow
English
clip
zero-shot-image-classification
Eval Results (legacy)
Instructions to use ydshieh/clip-vit-base-patch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ydshieh/clip-vit-base-patch32 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" 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("summarization", model="ydshieh/clip-vit-base-patch32")# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("ydshieh/clip-vit-base-patch32") model = AutoModelForZeroShotImageClassification.from_pretrained("ydshieh/clip-vit-base-patch32") - Notebooks
- Google Colab
- Kaggle
Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator
#1
by autoevaluator HF Staff - opened
Beep boop, I am a bot from Hugging Face's automatic model evaluator π! We've added a new verifyToken field to your evaluation results to verify that they are produced by the model evaluator. Accept this PR to ensure that your results remain listed as verified on the Hub leaderboard.