Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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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. | ||||||||||
Text_mining_resources | 511 | a year ago | ||||||||
Resources for learning about Text Mining and Natural Language Processing | ||||||||||
Rmdl | 409 | 1 | a year ago | 7 | July 01, 2020 | 2 | gpl-3.0 | Python | ||
RMDL: Random Multimodel Deep Learning for Classification | ||||||||||
Artificial Adversary | 317 | 1 | 6 years ago | 3 | August 29, 2018 | 7 | mit | Python | ||
🗣️ Tool to generate adversarial text examples and test machine learning models against them | ||||||||||
Pyss3 | 307 | 8 months ago | 29 | January 30, 2021 | 5 | mit | Python | |||
A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI :octocat:) | ||||||||||
Nlp Labelling | 257 | 2 years ago | n,ull | gpl-3.0 | JavaScript | |||||
Labelling platform for text using weak supervision. | ||||||||||
Hdltex | 252 | 5 months ago | 5 | April 20, 2018 | 7 | mit | Python | |||
HDLTex: Hierarchical Deep Learning for Text Classification | ||||||||||
Fake_news_detection | 251 | 2 years ago | 10 | mit | Jupyter Notebook | |||||
Fake News Detection in Python | ||||||||||
Cnn Text Classification Keras | 204 | 6 years ago | 1 | Python | ||||||
Text Classification by Convolutional Neural Network in Keras | ||||||||||
Shallowlearn | 196 | 7 years ago | 5 | December 30, 2016 | 17 | lgpl-3.0 | Python | |||
An experiment about re-implementing supervised learning models based on shallow neural network approaches (e.g. fastText) with some additional exclusive features and nice API. Written in Python and fully compatible with Scikit-learn. |