Text embedding models struggle with capturing subtle linguistic nuances like word order, directional relationships, temporal sequences, causal connections, comparisons, and negation. Understanding these challenges is key to improving model performance.

Learn how Jina-CLIP enhances OpenAI's CLIP with better retrieval accuracy and more diverse results through unified text-image embeddings.

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.

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?

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?

Jina AI Finetuner can bring performance improvements of up to 63% to pre-trained CLIP models. Here is how we did that.