Multi-Label-Text-Classification and LibMultiLabel

Multi-Label-Text-Classification

Established

LibMultiLabel

Emerging

Maintenance 0/25

Adoption 10/25

Maturity 16/25

Community 25/25

Maintenance 10/25

Adoption 5/25

Maturity 16/25

Community 18/25

Stars: 561

Forks: 142

Downloads: —

Commits (30d): 0

Language: Python

License: Apache-2.0

Stars: 13

Forks: 16

Downloads: —

Commits (30d): 0

Language: Python

License: MIT

Stale 6m No Package No Dependents

No Package No Dependents

About Multi-Label-Text-Classification

RandolphVI/Multi-Label-Text-Classification

About Muti-Label Text Classification Based on Neural Network.

This project helps you automatically categorize text documents or short texts by assigning multiple relevant labels to each. You input a collection of text documents (like news articles, product reviews, or scientific papers) and define a set of possible labels. The output is each text tagged with all applicable labels. This is ideal for analysts, content managers, or researchers who need to organize and analyze large volumes of text data with multiple descriptive categories.

text-categorization content-tagging document-indexing information-retrieval qualitative-data-analysis

About LibMultiLabel

ntumlgroup/LibMultiLabel

A library for multi-class and multi-label classification

This helps data scientists, machine learning engineers, and researchers categorize items into one or more predefined groups. You feed in raw text or other data, and it outputs predictions for which categories your items belong to, along with tools to evaluate how well the system performs. This is ideal for those who need to automatically tag or classify large datasets.

data-classification machine-learning-workflow text-categorization predictive-modeling data-tagging

Related comparisons

Multi-Label-Text-Classification and classifier-multi-label

Scores updated daily from GitHub, PyPI, and npm data. How scores work