English NER in Flair (Ontonotes default model)

This is the 18-class NER model for English that ships with Flair.

F1-Score: 89.27 (Ontonotes)

Predicts 18 tags:

tag meaning
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 and LSTM-CRF.


Demo: How to use in Flair

Requires: Flair (pip install flair)

from flair.data import Sentence
from flair.models import SequenceTagger

# load tagger
tagger = SequenceTagger.load("flair/ner-english-ontonotes")

# make example sentence
sentence = Sentence("On September 1st George Washington won 1 dollar.")

# predict NER tags
tagger.predict(sentence)

# print sentence
print(sentence)

# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
    print(entity)

This yields the following output:

Span [2,3]: "September 1st"   [− Labels: DATE (0.8824)]
Span [4,5]: "George Washington"   [− Labels: PERSON (0.9604)]
Span [7,8]: "1 dollar"   [− 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: Script to train this model

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

# 1. load the corpus (Ontonotes does not ship with Flair, you need to download and reformat into a column format yourself)
corpus: Corpus = ColumnCorpus(
                "resources/tasks/onto-ner",
                column_format={0: "text", 1: "pos", 2: "upos", 3: "ner"},
                tag_to_bioes="ner",
            )

# 2. what tag do we want to predict?
tag_type = 'ner'

# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)

# 4. initialize each embedding we use
embedding_types = [

    # GloVe embeddings
    WordEmbeddings('en-crawl'),

    # contextual string embeddings, forward
    FlairEmbeddings('news-forward'),

    # contextual string embeddings, backward
    FlairEmbeddings('news-backward'),
]

# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)

# 5. initialize sequence tagger
from flair.models import SequenceTagger

tagger = SequenceTagger(hidden_size=256,
                        embeddings=embeddings,
                        tag_dictionary=tag_dictionary,
                        tag_type=tag_type)

# 6. initialize trainer
from flair.trainers import ModelTrainer

trainer = ModelTrainer(tagger, corpus)

# 7. run training
trainer.train('resources/taggers/ner-english-ontonotes',
              train_with_dev=True,
              max_epochs=150)

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.

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