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 (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 (18-class) |
English |
Ontonotes |
89.06 (F1) |
||
NER (18-class) |
English |
Ontonotes |
89.27 (F1) |
(fast model) |
|
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 |
|||
‘de-ner’ |
NER (4-class) |
German |
Conll-03 |
87.94 (F1) |
|
NER (4-class) |
German / Multilingual |
Conll-03 |
92.31 (F1) |
||
‘de-ner-germeval’ |
NER (4-class) |
German |
Germeval |
84.90 (F1) |
|
NER (legal text) |
German |
LER dataset |
96.35 (F1) |
||
‘fr-ner’ |
NER (4-class) |
French |
95.57 (F1) |
||
NER (4-class) |
Spanish |
CoNLL-03 |
90.54 (F1) |
||
‘nl-ner’ |
NER (4-class) |
Dutch |
92.58 (F1) |
||
NER (4-class) |
Dutch |
Conll-03 |
95.25 (F1) |
||
‘nl-ner-rnn’ |
NER (4-class) |
Dutch |
90.79 (F1) |
||
NER (4-class) |
Ukrainian |
86.05 (F1) |
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.