Tagging parts-of-speech#

This tutorials shows you how to do part-of-speech tagging in Flair, showcases univeral and language-specific models, and gives a list of all PoS models in Flair.

Language-specific parts-of-speech (PoS)#

Syntax is fundamentally language-specific, so each language has different fine-grained parts-of-speech. Flair offers models for many languages:

… in English#

For English, we offer several models trained over Ontonotes.

Use like this:

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

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

# make a sentence
sentence = Sentence('Dirk went to the store.')

# predict NER tags
tagger.predict(sentence)

# print sentence with predicted tags
print(sentence)

This should print:

Sentence[6]: "Dirk went to the store." → ["Dirk"/NNP, "went"/VBD, "to"/IN, "the"/DT, "store"/NN, "."/.]

This printout tells us for instance that “Dirk” is a proper noun (tag: NNP), and “went” is a past tense verb (tag: VBD).

Note

To better understand what each tag means, consult the tag specification of the Penn Treebank.

… in German#

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

# load the model
tagger = Classifier.load('de-pos')

# make a sentence
sentence = Sentence('Dort hatte er einen Hut gekauft.')

# predict NER tags
tagger.predict(sentence)

# print sentence with predicted tags
print(sentence)

This should print:

Sentence[7]: "Dort hatte er einen Hut gekauft." → ["Dort"/ADV, "hatte"/VAFIN, "er"/PPER, "einen"/ART, "Hut"/NN, "gekauft"/VVPP, "."/$.]

… in Ukrainian#

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

# load the model
tagger = Classifier.load('pos-ukrainian')

# make a sentence
sentence = Sentence("Сьогодні в Знам’янці проживають нащадки поета — родина Шкоди.")

# predict NER tags
tagger.predict(sentence)

# print sentence with predicted tags
print(sentence)

… in Arabic#

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

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

# make a sentence
sentence = Sentence('عمرو عادلي أستاذ للاقتصاد السياسي المساعد في الجامعة الأمريكية  بالقاهرة .')

# predict NER tags
tagger.predict(sentence)

# print sentence with predicted tags
print(sentence)