inspirehep/magpie
Deep neural network framework for multi-label text classification
This tool helps researchers and librarians automatically organize scientific papers by assigning relevant subject categories and keywords to their abstracts. You provide a collection of text documents and their associated labels, and it learns to classify new, unlabeled documents. It is most useful for individuals managing large archives of scientific literature.
688 stars. No commits in the last 6 months.
Use this if you need to automatically categorize scientific abstracts or other short texts into predefined subjects and extract keywords.
Not ideal if you don't have a large collection of already-labeled documents to teach the system how to categorize.
Stars
688
Forks
190
Language
Python
License
MIT
Category
Last pushed
Jan 31, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/inspirehep/magpie"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
castorini/hedwig
PyTorch deep learning models for document classification
AnubhavGupta3377/Text-Classification-Models-Pytorch
Implementation of State-of-the-art Text Classification Models in Pytorch
kk7nc/Text_Classification
Text Classification Algorithms: A Survey
InseeFrLab/torchTextClassifiers
A unified framework for text classification in PyTorch.
davidberenstein1957/classy-classification
This repository contains an easy and intuitive approach to few-shot classification using...