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, and all other attributes of the Label class:

# 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"