text_classification and Deep-Survey-Text-Classification

One project offers a set of deep learning text classification models, while the other surveys various deep neural network models for text classification, making them **complements** where the survey provides theoretical understanding that could guide the application of the models offered by the other project.

Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 25/25
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 22/25
Stars: 7,950
Forks: 2,546
Downloads:
Commits (30d): 0
Language: Python
License: MIT
Stars: 193
Forks: 59
Downloads:
Commits (30d): 0
Language: HTML
License: MIT
Stale 6m No Package No Dependents
Stale 6m No Package No Dependents

About text_classification

brightmart/text_classification

all kinds of text classification models and more with deep learning

This project offers tools to categorize written text, helping you automatically assign labels or topics to documents or sentences. You provide raw text, and it outputs classifications, even for cases where a single piece of text has multiple associated labels. This is for data scientists or NLP engineers who need to build or benchmark models for text understanding tasks.

text-categorization natural-language-processing content-tagging information-extraction document-analysis

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.

text-classification natural-language-processing machine-learning-research deep-learning-models data-categorization

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