Repository Index

State-of-the-Art Embeddings, Retrieval, and Reranking

Topic Modelling for Humans

• gensim
• topic-modeling
• information-retrieval
• machine-learning

A very simple framework for state-of-the-art Natural Language Processing (NLP)

• pytorch
• nlp
• named-entity-recognition
• sequence-labeling

💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows

• python
• search
• nlp
• semantic-search

🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP

• bert
• sentence-encoding
• deep-learning
• clip-model

Retrieval and Retrieval-augmented LLMs

• embeddings
• information-retrieval
• llm
• sentence-embeddings

Source code for Twitter's Recommendation Algorithm

ImageBind One Embedding Space to Bind Them All

Train and evaluate embeddings for similarity search with PyTorch. Includes contrastive losses, hard example mining, samplers, and retrieval metrics.

• metric-learning
• deep-learning
• computer-vision
• machine-learning

repository for Publicly Available Clinical BERT Embeddings

Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.

• deep-learning
• protein-sequences
• protein-structure
• semi-supervised-learning

A simple deep learning library for estimating a set of tags and extracting semantic feature vectors from given illustrations.

Pathology Language and Image Pre-Training (PLIP) is the first vision and language foundation model for Pathology AI (Nature Medicine). PLIP is a large-scale pre-trained model that can be used to extract visual and language features from pathology images and text description. The model is a fine-tuned version of the original CLIP model.

• artificial-intelligence
• clip
• pathology
• vision-and-language

UCE is a zero-shot foundation model for single-cell gene expression data

gtfs2vec - Learning GTFS Embeddings for comparing PublicTransport Offer in Microregions