TextClassification-Keras and Deep-Survey-Text-Classification
About TextClassification-Keras
ShawnyXiao/TextClassification-Keras
Text classification models implemented in Keras, including: FastText, TextCNN, TextRNN, TextBiRNN, TextAttBiRNN, HAN, RCNN, RCNNVariant, etc.
This helps classify text documents into predefined categories, such as spam detection, sentiment analysis, or topic labeling. You provide raw text data, and it outputs labels indicating what each piece of text is about. This is ideal for data scientists or machine learning engineers who need to build and evaluate robust text classification systems for various business applications.
About Deep-Survey-Text-Classification
bicepjai/Deep-Survey-Text-Classification
The project surveys 16+ Natural Language Processing (NLP) research papers that propose novel Deep Neural Network Models for Text Classification, based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). It also implements each of the models using Tensorflow and Keras.
This project offers a comprehensive overview and practical implementations of various deep learning models designed for text classification tasks. It takes raw text documents as input and categorizes them into predefined labels, helping you organize and understand large volumes of text data. This is intended for machine learning engineers or researchers who need to compare and apply different neural network architectures for text classification.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work