1 repo
Awesome GitHub RepositoriesMulti-Modal Tokenizers
Systems that convert diverse data types into unified numerical sequences for latent space processing.
Distinguishing note: Focuses on the alignment of text and visual tokens into a shared latent space, distinct from standard NLP tokenization.
Explore 1 awesome GitHub repository matching artificial intelligence & ml · Multi-Modal Tokenizers. Refine with filters or upvote what's useful.
Awesome Multi-Modal Tokenizers GitHub Repositories
Find the best repos with AI.We'll search the best matching repositories with AI.
- openai/CLIP
openai/CLIP
32,614View on GitHubCLIP is a neural network architecture designed to map visual and textual data into a shared latent vector space. By utilizing transformer-based feature extraction and multi-modal tokenization, the system aligns images and natural language strings, enabling cross-modal similarity analysis and semantic classification. The project functions as a zero-shot classification engine, identifying image content by calculating the cosine similarity between visual features and arbitrary text labels without requiring task-specific retraining. Beyond inference, it serves as a research toolkit for evaluating
Converts natural language strings into numerical sequences that align with visual features within a unified latent representation space.
Jupyter Notebookdeep-learningmachine-learning
