Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Gensim | 15,180 | 2,371 | 766 | 18 days ago | 93 | August 24, 2023 | 405 | lgpl-2.1 | Python | |
Topic Modelling for Humans | ||||||||||
Magnitude | 1,542 | 2 years ago | 37 | mit | Python | |||||
A fast, efficient universal vector embedding utility package. | ||||||||||
Sense2vec | 1,486 | 6 | 7 | a year ago | 24 | April 19, 2021 | 20 | mit | Python | |
🦆 Contextually-keyed word vectors | ||||||||||
Nlp In Practice | 861 | 3 years ago | 1 | Jupyter Notebook | ||||||
Starter code to solve real world text data problems. Includes: Gensim Word2Vec, phrase embeddings, Text Classification with Logistic Regression, word count with pyspark, simple text preprocessing, pre-trained embeddings and more. | ||||||||||
Polish Nlp Resources | 267 | 5 months ago | 1 | lgpl-3.0 | ||||||
Pre-trained models and language resources for Natural Language Processing in Polish | ||||||||||
Ml Projects | 243 | 3 years ago | n,ull | |||||||
ML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python | ||||||||||
Gemsec | 234 | a year ago | gpl-3.0 | Python | ||||||
The TensorFlow reference implementation of 'GEMSEC: Graph Embedding with Self Clustering' (ASONAM 2019). | ||||||||||
Concise Concepts | 222 | 10 months ago | 35 | January 13, 2023 | 6 | mit | Python | |||
This repository contains an easy and intuitive approach to few-shot NER using most similar expansion over spaCy embeddings. Now with entity scoring. | ||||||||||
Rosetta | 206 | 14 | 1 | a year ago | 8 | August 18, 2015 | 16 | other | Jupyter Notebook | |
Tools, wrappers, etc... for data science with a concentration on text processing | ||||||||||
Splitter | 203 | a year ago | 1 | gpl-3.0 | Python | |||||
A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019). |