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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.

:::hint 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.

IDTaskLanguageTraining DatasetAccuracyContributor / Notes
'ner'NER (4-class)EnglishConll-0393.03 (F1)
'ner-fast'NER (4-class)EnglishConll-0392.75 (F1)(fast model)
'ner-large'NER (4-class)English / MultilingualConll-0394.09 (F1)(large model)
'ner-pooled'NER (4-class)EnglishConll-0393.24 (F1)(memory inefficient)
'ner-ontonotes'NER (18-class)EnglishOntonotes89.06 (F1)
'ner-ontonotes-fast'NER (18-class)EnglishOntonotes89.27 (F1)(fast model)
'ner-ontonotes-large'NER (18-class)English / MultilingualOntonotes90.93 (F1)(large model)
'ar-ner'NER (4-class)ArabicAQMAR & ANERcorp (curated)86.66 (F1)
'da-ner'NER (4-class)DanishDanish NER datasetAmaliePauli
'de-ner'NER (4-class)GermanConll-0387.94 (F1)
'de-ner-large'NER (4-class)German / MultilingualConll-0392.31 (F1)
'de-ner-germeval'NER (4-class)GermanGermeval84.90 (F1)
'de-ner-legal'NER (legal text)GermanLER dataset96.35 (F1)
'fr-ner'NER (4-class)FrenchWikiNER (aij-wikiner-fr-wp3)95.57 (F1)mhham
'es-ner-large'NER (4-class)SpanishCoNLL-0390.54 (F1)mhham
'nl-ner'NER (4-class)DutchCoNLL 200292.58 (F1)
'nl-ner-large'NER (4-class)DutchConll-0395.25 (F1)
'nl-ner-rnn'NER (4-class)DutchCoNLL 200290.79 (F1)
'ner-ukrainian'NER (4-class)UkrainianNER-UK dataset86.05 (F1)dchaplinsky

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

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