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--- |
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tags: |
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- feature-extraction |
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- sentence-similarity |
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- mteb |
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language: en |
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inference: false |
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license: apache-2.0 |
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--- |
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<!-- TODO: add evaluation results here --> |
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<br><br> |
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<p align="center"> |
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<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px"> |
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</p> |
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<p align="center"> |
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<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b> |
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</p> |
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## Quick Start |
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The easiest way to starting using `jina-clip-v1` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/). |
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## Intended Usage & Model Info |
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### `jina-clip-v1` Overview |
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### `jina-clip-v1` Overview |
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`jina-clip-v1` is an English, monolingual **multimodal (text-image) embedding model**. |
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Traditional text embedding models, such as [jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en), |
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excel in text-to-text retrieval but lack cross-modal retrieval capabilities. |
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Conversely, CLIP-like models, such as [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32), |
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align image embeddings with text embeddings but underperform in text-to-text retrieval due to their training methodology and context length limitations. |
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`jina-clip-v1` is an innovative **multimodal embedding model**. |
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Its text component achieves comparable performance to `jina-embeddings-v2-base-en` in text-to-text retrieval, |
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while the overall model delivers state-of-the-art performance in cross-modal retrieval tasks. |
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This makes it an ideal choice for multimodal retrieval-augmented generation (M-RAG) applications, |
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allowing for both text-to-text and text-to-image searches with a single model. |
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## Data & Parameters |
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Jina CLIP V1 [technical report]() coming soon. |
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## Usage |
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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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model = AutoModel.from_pretrained('jinaai/jina-clip-v1') |
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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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## Contact |
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Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas. |
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## Citation |
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If you find Jina CLIP useful in your research, please cite the following paper: |
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```console |
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TBD |
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``` |
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