Tech Blog Beyond CLIP: How Jina-CLIP Advances Multimodal Search Learn how Jina-CLIP enhances OpenAI's CLIP with better retrieval accuracy and more diverse results through unified text-image embeddings.
Tech Blog Bridging Language Gaps in Multilingual Embeddings via Contrastive Learning Multilingual models often face a "language gap," where similar phrases in different languages don't align. We show how contrastive learning can bridge this gap, enhancing cross-language performance.
Tech Blog The What and Why of Text-Image Modality Gap in CLIP Models You can't just use a CLIP model to retrieve text and images and sort the results by score. Why? Because of the modality gap. What is it, and where does it come from?
Tech Blog Fine-tuning with Low Budget and High Expectations Fine-tuning is highly effective, economical, and eco-friendly; or at least that's what all the deep learning tweets will tell you. Exactly how much data & time do you need to get a good result?
Tech Blog How Much Do We Get by Finetuning CLIP? Jina AI Finetuner can bring performance improvements of up to 63% to pre-trained CLIP models. Here is how we did that.
