Instructions to use RISys-Lab/ReasonCLIP-L14-336-S2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RISys-Lab/ReasonCLIP-L14-336-S2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="RISys-Lab/ReasonCLIP-L14-336-S2") 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("RISys-Lab/ReasonCLIP-L14-336-S2") model = AutoModelForZeroShotImageClassification.from_pretrained("RISys-Lab/ReasonCLIP-L14-336-S2") - Notebooks
- Google Colab
- Kaggle
ReasonCLIP Bot commited on
Commit ·
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Parent(s): 86a574e
Add method figure to model card
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README.md
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- Training stage: Stage 2
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- Training data: [ReasonLite-42M](https://huggingface.co/datasets/RISys-Lab/ReasonCLIPLite-42M) and [ReasonPro-16M](https://huggingface.co/datasets/RISys-Lab/ReasonCLIPPro-16M)
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## Usage
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```python
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- Training stage: Stage 2
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- Training data: [ReasonLite-42M](https://huggingface.co/datasets/RISys-Lab/ReasonCLIPLite-42M) and [ReasonPro-16M](https://huggingface.co/datasets/RISys-Lab/ReasonCLIPPro-16M)
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## Method
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## Usage
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```python
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