Franck-Dernoncourt/NeuroNER
Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.
This program helps anyone working with large volumes of text to automatically identify and categorize key pieces of information, like names, organizations, or dates. You input raw text documents, and it outputs the same text with these important entities highlighted and labeled. It's ideal for researchers, data analysts, or content managers who need to extract specific data from unstructured text.
1,720 stars. No commits in the last 6 months.
Use this if you need to automatically find and classify specific types of information, such as names of people, places, or medical terms, within large collections of text documents.
Not ideal if you primarily need to understand the sentiment or overall topic of a text, rather than extracting specific data points.
Stars
1,720
Forks
473
Language
Python
License
MIT
Category
Last pushed
Mar 24, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/Franck-Dernoncourt/NeuroNER"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
Related tools
chakki-works/seqeval
A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
Hironsan/anago
Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on.
jbesomi/texthero
Text preprocessing, representation and visualization from zero to hero.
hamelsmu/ktext
Utilities for preprocessing text for deep learning with Keras
asahi417/tner
Language model fine-tuning on NER with an easy interface and cross-domain evaluation. "T-NER: An...