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Ononotes 5 NER with BERT

This is a simple test of NER with BERT. The repo is a placeholder for code to reuse. The code is basically BertTagger from Devlin et al.

Installation

Please install git-lfs before installing

git clone https://github.com/fractalego/bert_ner.git
cd bert_ner
virtualenv .env --python=/usr/bin/python3
pip install .

Example

An example can be found in the file predict.py

import os

from bert_ner.tagger import NERTagger

_path = os.path.dirname(__file__)

_filename = os.path.join(_path, '../data/ontonotes.model')

_text = "John Stephen Smith is working for Acme at the Gherkin in London."

if __name__ == '__main__':
    tagger = NERTagger(_filename)
    predictions = tagger.tag(_text)
    print(predictions)

With output

[('john stephen smith', 'PERSON', (0, 18)), ('acme', 'ORG', (34, 38)), ('gurkin', 'FAC', (46, 52)), ('london', 'GPE', (56, 62))]

Dataset

The Ontonotes 5 dataset can be downloaded from the LDC website.

Results

I measured the results both with and without BIOS tags. The BIOES tags don't seem to make much difference.

NO BIOES

Set Precision Recall F1
Dev 0.842 0.876 0.858
Test 0.842 0.866 0.853

With BIOES

Set Precision Recall F1
Dev 0.850 0.874 0.862
Test 0.847 0.859 0.853

Comments

This is just a placeholder repo for code to reuse. The results (85-86% F1) seem to be less impressive than the 89% that is reached in Devlin et al.
Please do not judge me on this.