Topic Modelling for Humans
- gensim
- topic-modeling
- information-retrieval
- machine-learning
Repository Index
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💡 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
Source code for Twitter's Recommendation Algorithm
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
PyTorch code and models for V-JEPA self-supervised learning from video.
Implementation and experiments of graph embedding algorithms.
- deepwalk
- node2vec
- line
- sdne
Open-source inference server and production cluster for all the models your agent needs.
Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
- machine-learning
- convolutional-networks
- embedding-vectors
- embeddings
A python package to analyze and compare voices with deep learning
Siamese and triplet networks with online pair/triplet mining in PyTorch
- machine-learning
- deep-learning
- siamese-network
- triplet-loss
The code used to train and run inference with the ColVision models, e.g. ColPali, ColQwen2, and ColSmol.
- colpali
- information-retrieval
- retrieval-augmented-generation
- vision-language-model
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
- community-detection
- graph-clustering
- deepwalk
- networkx
Fast State-of-the-Art Static Embeddings
- embeddings
- machine-learning
- model2vec
- nlp
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.
Learning Heterogeneous Tactile Representation for General Dexterous Manipulation
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