Topic Modelling for Humans
- gensim
- topic-modeling
- information-retrieval
- machine-learning
Repository Index
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1–19 of 19 · Most stars
A very simple framework for state-of-the-art Natural Language Processing (NLP)
🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
- bert
- sentence-encoding
- deep-learning
- clip-model
Source code for Twitter's Recommendation Algorithm
An Open-Source Package for Knowledge Embedding (KE)
- knowledge-embedding
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
Fast, Accurate, Lightweight Python library to make State of the Art Embedding
- embeddings
- openai
- rag
- retrieval
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
- community-detection
- graph-clustering
- deepwalk
- networkx
Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org
- machine-learning
- knowledge-graph
- relational-learning
- representation-learning
Nomic Developer API SDK
- python
- clustering
- duplicate-detection
- embeddings
Netease Youdao's open-source embedding and reranker models for RAG products.
NeurIPS 2025 Spotlight; ICLR2024 Spotlight; CVPR 2024; EMNLP 2024
An Open-Source Package for Network Embedding (NE)
- network-embedding
🦆 Contextually-keyed word vectors
- spacy
- nlp
- natural-language-processing
- word2vec
A fast, efficient universal vector embedding utility package.
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