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How predictions work

All taggers in Flair make predictions. This tutorial helps you understand what information you can get out of each prediction.

Running example​

Let's use our standard NER example to illustrate how annotations work: 

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 the sentence with the tags
print(sentence)

This should print:

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

Showing us that two entities are labeled in this sentence: "George Washington" as PER (person) and "Washington" as LOC (location.)

Getting the predictions​

A common question that gets asked is how to access these predictions directly. You can do this by using the get_labels() method to iterate over all predictions:

for label in sentence.get_labels():
    print(label)

This should print the two NER predictions:

Span[0:2]: "George Washington" → PER (0.9989)
Span[4:5]: "Washington" → LOC (0.9942)

As you can see, each entity is printed, together with the predicted class. The confidence of the prediction is indicated as a score in brackets.

Values for each prediction​

For each prediction, you can even directly access the label value, it's score and the entity text: 

# iterate over all labels in the sentence
for label in sentence.get_labels():
    # print label value and score
    print(f'label.value is: "{label.value}"')
    print(f'label.score is: "{label.score}"')
    # access the data point to which label attaches and print its text
    print(f'the text of label.data_point is: "{label.data_point.text}"\n')

This should print: 

label.value is: "PER"
label.score is: "0.998886227607727"
the text of label.data_point is: "George Washington"

label.value is: "LOC"
label.score is: "0.9942097663879395"
the text of label.data_point is: "Washington"