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.

Next

Congrats, you learned how to predict entities with Flair and got an overview of different models!

Next, let’s discuss how to predict sentiment with Flair.