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Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.
| Date | Stars |
|---|---|
| 2026-07-24 | 1723 |
| 2026-07-25 | 1723 |
| 2026-07-28 | 1723 |
| 2026-07-30 | 1723 |
| 2026-07-31 | 1724 |
| 2026-08-06 | 1724 |
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# NeuroNER
[](https://travis-ci.org/Franck-Dernoncourt/NeuroNER)
NeuroNER is a program that performs named-entity recognition (NER). Website: [neuroner.com](http://neuroner.com).
This page gives step-by-step instructions to install and use NeuroNER.
## Table of Contents
<!-- toc -->
- [Requirements](#requirements)
- [Installation](#installation)
- [Using NeuroNER](#using-neuroner)
* [Adding a new dataset](#adding-a-new-dataset)
* [Using a pretrained model](#using-a-pretrained-model)
* [Sharing a pretrained model](#sharing-a-pretrained-model)
* [Using TensorBoard](#using-tensorboard)
- [Citation](#citation)
<!-- tocstop -->
## Requirements
NeuroNER relies on Python 3, TensorFlow 1.0+, and optionally on BRAT:
- Python 3: NeuroNER does not work with Python 2.x. On Windows, it has to be Python 3.6 64-bit or later.
- TensorFlow is a library for machine learning. NeuroNER uses it for its NER engine, which is based on neural networks. Official website: [https://www.tensorflow.org](https://www.tensorflow.org)
- BRAT (optional) is a web-based annotation tool. It only needs to be installed if you wish to conveniently create annotations or view the predictions made by NeuroNER. Official website: [http://brat.nlplab.org](http://brat.nlplab.org)
## Installation
For GPU support, [GPU requirements for Tensorflow](https://www.tensorflow.org/install/) must be satisfied. If your system does not meet these requirements, you should use the CPU version. To install neuroner:
```
# For CPU support (no GPU support):
pip3 install pyneuroner[cpu]
# For GPU support:
pip3 install pyneuroner[gpu]
```
You will also need to download some support packages.
1. The English language module for Spacy:
```
# Download the SpaCy English module
python -m spacy download en
```
2. Download word embeddings from http://neuroner.com/data/word_vectors/glove.6B.100d.zip, unzip them to the folder `./data/word_vectors`
```
# Get word embeddings
wget -P data/word_vectors http://neuroner.com/data/word_vectors/glove.6B.100d.zip
unzip data/word_vectors/glove.6B.100d.zip -d data/word_vectors/
```
3. Load sample datasets. These can be loaded by calling the `neuromodel.fetch_data()` function from a Python interpreter or with the `--fetch_data` argument at the command line.
```
# Load a dataset from the command line
neuroner --fetch_data=conll2003
neuroner --fetch_data=example_unannotated_texts
neuroner --fetch_data=i2b2_2014_deid
```
```
# Load a dataset from a Python interpreter
from neuroner import neuromodel
neuromodel.fetch_data('conll2003')
neuromodel.fetch_data('example_unannotated_texts')
neuromodel.fetch_data('i2b2_2014_deid')
```
4. Load a pretrained model. The models can be loaded by calling the `neuromodel.fetch_model()` function from a Python interpreter or with the `--fetch_trained_models` argument at the command line.
```
# Load a pre-trained model from the command line
neuroner --fetch_trained_model=conll_2003_en
neuroner --fetch_trained_model=i2b2_2014_glove_spacy_bioes
neuroner --fetch_trained_model=i2b2_2014_glove_stanford_bioes
neuroner --fetch_trained_model=mimic_glove_spacy_bioes
neuroner --fetch_trained_model=mimic_glove_stanford_bioes
```
```
# Load a pre-trained model from a Python interpreter
from neuroner import neuromodel
neuromodel.fetch_model('conll_2003_en')
neuromodel.fetch_model('i2b2_2014_glove_spacy_bioes')
neuromodel.fetch_model('i2b2_2014_glove_stanford_bioes')
neuromodel.fetch_model('mimic_glove_spacy_bioes')
neuromodel.fetch_model('mimic_glove_stanford_bioes')
```
### Installing BRAT (optional)
BRAT is a tool that can be used to create, change or view the BRAT-style annotations. For installation and usage instructions, see the [BRAT website](http://brat.nlplab.org/installation.html).
### Installing Perl (platform dependent)
Perl is required because the official CoNLL-2003 evaluation script is written in this language: http:Excerpt of 11,025 characters
Read on GitHub
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John Giorgi · @abridgeai
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:608c9deef0d33292, topic:nlp, topic:named-entity-recognition, desc:named entity recognition
matched fp:608c9deef0d33292, topic:deep-learning, topic:tensorflow