Repository Index

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

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

• python
• search
• nlp
• semantic-search

Translated100+ Chinese Word Vectors

• chinese
• chinese-word-segmentation
• embeddings
• word-embeddings

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

Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.

• machine-learning
• convolutional-networks
• embedding-vectors
• embeddings

Fast State-of-the-Art Static Embeddings

• embeddings
• machine-learning
• model2vec
• nlp

Pre-trained word vectors of 30+ languages

• vector
• word2vec
• language
• fasttext

Sent2Vec encoder and training code from the paper "Skip-Thought Vectors"

WeMM-Embedding is a family of universal multimodal embedding models by the WeChat Vision Team at Tencent, supporting multimodal understanding and retrieval.

• embedding-models
• multimodal
• multimodal-llm

Code for 'LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders'

🦆 Contextually-keyed word vectors

• spacy
• nlp
• natural-language-processing
• word2vec

A fast, efficient universal vector embedding utility package.

• python
• natural-language-processing
• nlp
• machine-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