NeuroNER

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NeuroNER is an AI Agents & Automation tool that performs named-entity recognition (NER) using neural networks. It offers an easy-to-use interface and delivers state-of-the-art results for information extraction and text analysis.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is NeuroNER?

NeuroNER is a powerful program designed for named-entity recognition (NER) using advanced neural networks. It provides an easy-to-use interface, making it accessible for various NER tasks while delivering state-of-the-art results. The tool supports both command-line and Python interpreter usage, allowing flexibility for developers and researchers. Users can train models from scratch or leverage pre-trained models, and it supports popular dataset formats like CoNLL-2003 and BRAT. NeuroNER is built on Python 3 and TensorFlow, with optional integration for BRAT as a web-based annotation tool. It also includes features for sharing pre-trained models and monitoring training progress with TensorBoard, making it a comprehensive solution for text analysis and information extraction.

Best used for

Ideal for developers and data scientists who need to accurately identify and extract named entities from text, process large datasets in CoNLL-2003 or BRAT formats, and leverage or train neural network models for text analysis. Especially valuable for researchers and practitioners working with information extraction from diverse textual sources.

Common actions

perform named-entity recognition

extract information from text

train neural networks

analyze text data

open-sourceworkflowsautomated workflowlow-code/no-code"AI Agents"deepfakeface swappinggithub copilotcollaboration

Capabilities

Key features

Named-entity recognition
Neural network engine
CoNLL-2003 format support
BRAT format support
Pre-trained model usage
TensorBoard integration
Model sharing

Target Audience

developerdata scientist

Integrations

tensorflowspacybrat

Pricing & Plans

Open Source

Free

FAQs

What are the system requirements for NeuroNER?

NeuroNER requires Python 3 (specifically Python 3.6 64-bit or later on Windows) and TensorFlow 1.0+. For GPU support, your system must meet TensorFlow's GPU requirements. BRAT is an optional installation for annotation and viewing predictions.

Can I use NeuroNER with my own datasets?

Yes, NeuroNER supports adding new datasets in either CoNLL-2003 or BRAT format. You need to organize your dataset files into specific folders (train, valid, test, deploy) with appropriate naming conventions for the tool to process them.

How can I share a model I've trained with NeuroNER?

NeuroNER provides a script, `prepare_pretrained_model.py`, to facilitate sharing your trained models. You can specify parameters like output folder, epoch number, and model name. There's also an option to remove dataset-specific token mappings for broader applicability.

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Data & Analytics › Data Cleaning & Prep Data & Analytics › Statistical & Scientific

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