Topic Modelling for Humans
- gensim
- topic-modeling
- information-retrieval
- machine-learning
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Topic Modelling for Humans
A very simple framework for state-of-the-art Natural Language Processing (NLP)
🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
Source code for Twitter's Recommendation Algorithm
An Open-Source Package for Knowledge Embedding (KE)
骆驼(Luotuo): Open Sourced Chinese Language Models. Developed by 陈启源 @ 华中师范大学 & 李鲁鲁 @ 商汤科技 & 冷子昂 @ 商汤科技
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.
Fast, Accurate, Lightweight Python library to make State of the Art Embedding
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org
Nomic Developer API SDK
Netease Youdao's open-source embedding and reranker models for RAG products.
NeurIPS 2025 Spotlight; ICLR2024 Spotlight; CVPR 2024; EMNLP 2024
This tool provides an efficient implementation of the continuous bag-of-words and skip-gram architectures for computing vector representations of words. These representations can be subsequently used in many natural language processing applications and for further research.
An Open-Source Package for Network Embedding (NE)
🦆 Contextually-keyed word vectors
A fast, efficient universal vector embedding utility package.
Fast vectorization, topic modeling, distances and GloVe word embeddings in R.
🚀 Catalyst is a C# Natural Language Processing library built for speed. Inspired by spaCy's design, it brings pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models.
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
A simple deep learning library for estimating a set of tags and extracting semantic feature vectors from given illustrations.
Cleora AI is a general-purpose open-source model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data. Created by Synerise.com team.
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.