The text embedding set trained by Jina AI.
## Quick Start The easiest way to starting using `jina-clip-v1` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/). ## Intended Usage & Model Info `jina-clip-v1` is an English, monolingual **multimodal (text-image) embedding model**. Traditional text embedding models, such as [jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en), excel in text-to-text retrieval but lack cross-modal retrieval capabilities. Conversely, CLIP-like models, such as [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32), align image embeddings with text embeddings but underperform in text-to-text retrieval due to their training methodology and context length limitations. `jina-clip-v1` is an innovative **multimodal embedding model**. Its text component achieves comparable performance to `jina-embeddings-v2-base-en` in text-to-text retrieval, while the overall model delivers state-of-the-art performance in cross-modal retrieval tasks. This makes it an ideal choice for multimodal retrieval-augmented generation (M-RAG) applications, allowing for both text-to-text and text-to-image searches with a single model. ## Data & Parameters `jina-clip-v1` [technical report]() coming soon. ## Usage You can use Jina CLIP directly from transformers package. ```python !pip install transformers einops timm pillow from transformers import AutoModel from numpy.linalg import norm cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b)) model = AutoModel.from_pretrained('jinaai/jina-clip-v1', trust_remote_code=True) sentences = ['How is the weather today?', 'What is the current weather like today?'] images = ['raindrop.jpg', 'sunny.jpg'] text_embeddings = model.encode_text(sentences) image_embeddings = model.encode_image(images) print(cos_sim(text_embeddings[0], text_embeddings[1])) # text embedding similarity print(cos_sim(text_embeddings[0], image_embeddings[0])) # text-image cross-modal similarity ``` **notice: our emperical study shows that text-text cosine similarity is normally larger than text-image cosine similarity!** If you want to merge two scores, we recommended 2 ways: 1. weighted average of text-text sim and text-image sim: ```python # pseudo code alpha = 0.6 # text search beta = 0.4 # cross-modal search combined_scores = alpha * sim(query, document) + beta * sim(text, image) ``` 2. apply z-score normalization before merging scores: ```python # pseudo code query_document_sim_mean = np.mean(cos_sim_query_documents) query_document_sim_std = np.std(cos_sim_query_documents) text_image_sim_mean = np.mean(cos_sim_text_images) text_image_sim_std = np.std(cos_sim_text_images) query_document_sim_normalized = (cos_sim_query_documents - query_document_sim_mean) / query_document_sim_std text_image_sim_normalized = (cos_sim_text_images - text_image_sim_mean) / text_image_sim_std # sum normalized scores ``` ## Performance ### Text-Image Retrieval | Name | Flickr Image Retr. R@1 | Flickr Image Retr. R@5 | Flickr Text Retr. R@1 | Flickr Text Retr. R@5 | |------------------|-------------------------|-------------------------|-----------------------|-----------------------| | ViT-B-32 | 0.597 | 0.8398 | 0.781 | 0.938 | | ViT-B-16 | 0.6216 | 0.8572 | 0.822 | 0.966 | | jina-clip | 0.6748 | 0.8902 | 0.811 | 0.965 | | Name | MSCOCO Image Retr. R@1 | MSCOCO Image Retr. R@5 | MSCOCO Text Retr. R@1 | MSCOCO Text Retr. R@5 | |------------------|-------------------------|-------------------------|-----------------------|-----------------------| | ViT-B-32 | 0.342 | 0.6001 | 0.5234 | 0.7634 | | ViT-B-16 | 0.3309 | 0.5842 | 0.5242 | 0.767 | | jina-clip | 0.4111 | 0.6644 | 0.5544 | 0.7904 | ### Text-Text Retrieval | Name | STS12 | STS15 | STS17 | STS13 | STS14 | STS16 | STS22 | STSBenchmark | SummEval | |-----------------------|--------|--------|--------|--------|--------|--------|--------|--------------|----------| | jina-embeddings-v2 | 0.7427 | 0.8755 | 0.8888 | 0.833 | 0.7917 | 0.836 | 0.6346 | 0.8404 | 0.3056 | | jina-clip | 0.7352 | 0.8746 | 0.8976 | 0.8323 | 0.7868 | 0.8377 | 0.6583 | 0.8493 | 0.3048 | | Name | ArguAna | FiQA2018 | NFCorpus | Quora | SCIDOCS | SciFact | TRECCOVID | |--------------------|---------|----------|----------|-------|---------|---------|-----------| | jina-embeddings-v2 | 0.4418 | 0.4158 | 0.3245 | 0.882 | 0.1986 | 0.6668 | 0.6591 | | jina-clip | 0.4933 | 0.3827 | 0.3352 | 0.8789| 0.2024 | 0.6734 | 0.7161 | ## Contact Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas. ## Citation If you find `jina-clip-v1` useful in your research, please cite the following paper: ```console TBD ```