đ English NER in Flair (Ontonotes default model)
This is an 18 - class NER model for English provided by Flair, which can effectively identify various named entities in English text.
This is the 18 - class NER model for English that ships with Flair.
F1 - Score: 89.27 (Ontonotes)
Predicts 18 tags:
Property |
Details |
CARDINAL |
cardinal value |
DATE |
date value |
EVENT |
event name |
FAC |
building name |
GPE |
geo - political entity |
LANGUAGE |
language name |
LAW |
law name |
LOC |
location name |
MONEY |
money name |
NORP |
affiliation |
ORDINAL |
ordinal value |
ORG |
organization name |
PERCENT |
percent value |
PERSON |
person name |
PRODUCT |
product name |
QUANTITY |
quantity value |
TIME |
time value |
WORK_OF_ART |
name of work of art |
Based on [Flair embeddings](https://www.aclweb.org/anthology/C18 - 1139/) and LSTM - CRF.
đ Quick Start
Prerequisites
â ī¸ Important Note
This project requires Flair to be installed. You can install it using the following command:
pip install flair
đģ Usage Examples
Basic Usage
from flair.data import Sentence
from flair.models import SequenceTagger
tagger = SequenceTagger.load("flair/ner-english-ontonotes")
sentence = Sentence("On September 1st George Washington won 1 dollar.")
tagger.predict(sentence)
print(sentence)
print('The following NER tags are found:')
for entity in sentence.get_spans('ner'):
print(entity)
This yields the following output:
Span [2,3]: "September 1st" [â Labels: DATE (0.8824)]
Span [4,5]: "George Washington" [â Labels: PERSON (0.9604)]
Span [7,8]: "1 dollar" [â Labels: MONEY (0.9837)]
So, the entities "September 1st" (labeled as a date), "George Washington" (labeled as a person) and "1 dollar" (labeled as a money) are found in the sentence "On September 1st George Washington won 1 dollar".
Training
The following Flair script was used to train this model:
from flair.data import Corpus
from flair.datasets import ColumnCorpus
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
corpus: Corpus = ColumnCorpus(
"resources/tasks/onto-ner",
column_format={0: "text", 1: "pos", 2: "upos", 3: "ner"},
tag_to_bioes="ner",
)
tag_type = 'ner'
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
embedding_types = [
WordEmbeddings('en-crawl'),
FlairEmbeddings('news-forward'),
FlairEmbeddings('news-backward'),
]
embeddings = StackedEmbeddings(embeddings=embedding_types)
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
trainer.train('resources/taggers/ner-english-ontonotes',
train_with_dev=True,
max_epochs=150)
đ Documentation
Cite
Please cite the following paper when using this model.
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
Issues?
The Flair issue tracker is available here.