gmastrapas
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docs: update usage in README
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README.md
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@@ -48,14 +48,19 @@ Jina CLIP V1 [technical report]() coming soon.
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You can use Jina CLIP directly from transformers package.
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```python
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!pip install transformers
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from transformers import AutoModel
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from numpy.linalg import norm
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cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
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text_embeddings = model.encode_text(['How is the weather today?', 'What is the current weather like today?'])
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image_embeddings = model.encode_image(['raindrop.png'])
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print(cos_sim(text_embeddings[0], text_embeddings[1])) # text embedding similarity
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print(cos_sim(text_embeddings[0], image_embeddings[0])) # text-image cross-modal similarity
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```
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You can use Jina CLIP directly from transformers package.
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```python
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!pip install transformers einops timm
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from transformers import AutoModel, AutoTokenizer, AutoImageProcessor
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from numpy.linalg import norm
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cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
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tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-clip-v1', trust_remote_code=True)
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image_processor = AutoImageProcessor.from_pretrained('jinaai/jina-clip-v1', trust_remote_code=True)
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model = AutoModel.from_pretrained('jinaai/jina-clip-v1', trust_remote_code=True)
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text_embeddings = model.encode_text(['How is the weather today?', 'What is the current weather like today?'])
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image_embeddings = model.encode_image(['raindrop.png'])
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print(cos_sim(text_embeddings[0], text_embeddings[1])) # text embedding similarity
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print(cos_sim(text_embeddings[0], image_embeddings[0])) # text-image cross-modal similarity
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```
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