Tagging entities

This tutorials shows you how to do named entity recognition, showcases various NER models, and provides a full list of all NER models in Flair.

Tagging entities with our standard model

Our standard model uses Flair embeddings and was trained over the English CoNLL-03 task and can recognize 4 different entity types. It offers a good tradeoff between accuracy and speed.

As example, let’s use the sentence “George Washington went to Washington.”:

from flair.nn import Classifier
from flair.data import Sentence

# load the model
tagger = Classifier.load('ner')

# make a sentence
sentence = Sentence('George Washington went to Washington.')

# predict NER tags
tagger.predict(sentence)

# print sentence with predicted tags
print(sentence)

This should print:

Sentence: "George Washington went to Washington ." → ["George Washington"/PER, "Washington"/LOC]

The printout tells us that two entities are labeled in this sentence: “George Washington” as PER (person) and “Washington” as LOC (location).

Tagging entities with our best model

Our best 4-class model is trained using a very large transformer. Use it if accuracy is the most important to you, and speed/memory not so much.

from flair.data import Sentence
from flair.nn import Classifier

# make a sentence
sentence = Sentence('George Washington went to Washington.')

# load the NER tagger
tagger = Classifier.load('ner-large')

# run NER over sentence
tagger.predict(sentence)

# print the sentence with all annotations
print(sentence)

As you can see, it’s the same code, just with ‘ner-large’ as model instead of ‘ner’. This model also works with most languages.

Note

If you want the fastest model we ship, you can also try ‘ner-fast’.

Tagging entities in non-English text

We also have NER models for text in other languages.

Tagging a German sentence

To tag a German sentence, just load the appropriate model:

# load model
tagger = Classifier.load('de-ner-large')

# make German sentence
sentence = Sentence('George Washington ging nach Washington.')

# predict NER tags
tagger.predict(sentence)

# print sentence with predicted tags
print(sentence)

This should print:

Sentence: "George Washington ging nach Washington ." → ["George Washington"/PER, "Washington"/LOC]

Tagging an Arabic sentence

Flair also works for languages that write from right to left. To tag an Arabic sentence, just load the appropriate model:

# load model
tagger = Classifier.load('ar-ner')

# make Arabic sentence
sentence = Sentence("احب برلين")

# predict NER tags
tagger.predict(sentence)

# print sentence with predicted tags
print(sentence)

This should print:

Sentence[2]: "احب برلين" → ["برلين"/LOC]

Tagging Entities with 18 Classes

We also ship models that distinguish between more than just 4 classes. For instance, use our ontonotes models to classify 18 different types of entities.

from flair.data import Sentence
from flair.nn import Classifier

# make a sentence
sentence = Sentence('On September 1st George won 1 dollar while watching Game of Thrones.')

# load the NER tagger
tagger = Classifier.load('ner-ontonotes-large')

# run NER over sentence
tagger.predict(sentence)

# print the sentence with all annotations
print(sentence)

This should print:

Sentence[13]: "On September 1st George won 1 dollar while watching Game of Thrones." → ["September 1st"/DATE, "George"/PERSON, "1 dollar"/MONEY, "Game of Thrones"/WORK_OF_ART]

Finding for instance that “Game of Thrones” is a work of art and that “September 1st” is a date.

Biomedical Data

For biomedical data, we offer the hunflair models that detect 5 different types of biomedical entities.

from flair.data import Sentence
from flair.nn import Classifier

# make a sentence
sentence = Sentence('Behavioral abnormalities in the Fmr1 KO2 Mouse Model of Fragile X Syndrome.')

# load the NER tagger
tagger = Classifier.load('bioner')

# run NER over sentence
tagger.predict(sentence)

# print the sentence with all annotations
print(sentence)

This should print:

Sentence[13]: "Behavioral abnormalities in the Fmr1 KO2 Mouse Model of Fragile X Syndrome." → ["Behavioral abnormalities"/Disease, "Fmr1"/Gene, "Mouse"/Species, "Fragile X Syndrome"/Disease]

Thus finding entities of classes “Species”, “Disease” and “Gene” in this text.

List of NER Models

We end this section with a list of all models we currently ship with Flair.

ID	Task	Language	Training Dataset	Accuracy	Contributor / Notes
‘ner’	NER (4-class)	English	Conll-03	93.03 (F1)
‘ner-fast’	NER (4-class)	English	Conll-03	92.75 (F1)	(fast model)
‘ner-large’	NER (4-class)	English / Multilingual	Conll-03	94.09 (F1)	(large model)
‘ner-pooled’	NER (4-class)	English	Conll-03	93.24 (F1)	(memory inefficient)
‘ner-ontonotes’	NER (18-class)	English	Ontonotes	89.06 (F1)
‘ner-ontonotes-fast’	NER (18-class)	English	Ontonotes	89.27 (F1)	(fast model)
‘ner-ontonotes-large’	NER (18-class)	English / Multilingual	Ontonotes	90.93 (F1)	(large model)
‘ar-ner’	NER (4-class)	Arabic	AQMAR & ANERcorp (curated)	86.66 (F1)
‘da-ner’	NER (4-class)	Danish	Danish NER dataset		AmaliePauli
‘de-ner’	NER (4-class)	German	Conll-03	87.94 (F1)
‘de-ner-large’	NER (4-class)	German / Multilingual	Conll-03	92.31 (F1)
‘de-ner-germeval’	NER (4-class)	German	Germeval	84.90 (F1)
‘de-ner-legal’	NER (legal text)	German	LER dataset	96.35 (F1)
‘fr-ner’	NER (4-class)	French	WikiNER (aij-wikiner-fr-wp3)	95.57 (F1)	mhham
‘es-ner-large’	NER (4-class)	Spanish	CoNLL-03	90.54 (F1)	mhham
‘nl-ner’	NER (4-class)	Dutch	CoNLL 2002	92.58 (F1)
‘nl-ner-large’	NER (4-class)	Dutch	Conll-03	95.25 (F1)
‘nl-ner-rnn’	NER (4-class)	Dutch	CoNLL 2002	90.79 (F1)
‘ner-ukrainian’	NER (4-class)	Ukrainian	NER-UK dataset	86.05 (F1)	dchaplinsky

You choose which pre-trained model you load by passing the appropriate string to the Classifier.load() method.

A full list of our current and community-contributed models can be browsed on the model hub.