Quick Start

Let’s discover Flair in less than 5 minutes.

Requirements and Installation

In your favorite virtual environment, simply do:

pip install flair

Flair requires Python 3.9+.

Example 1: Tag Entities in Text

Let’s run named entity recognition (NER) over the following example sentence: “I love Berlin and New York.”

Our goal is to identify names in this sentence, and their types.

To do this, all you need is to make a Sentence for this text, load a pre-trained model and use it to predict tags for the sentence:

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

# make a sentence
sentence = Sentence('I love Berlin and New York.')

# load the NER tagger
tagger = Classifier.load('ner')

# run NER over sentence
tagger.predict(sentence)

# print the sentence with all annotations
print(sentence)

This should print:

Sentence[7]: "I love Berlin and New York." → ["Berlin"/LOC, "New York"/LOC]

The output shows that both “Berlin” and “New York” were tagged as location entities (LOC) in this sentence.

Example 2: Detect Sentiment

Let’s run sentiment analysis over the same sentence to determine whether it is POSITIVE or NEGATIVE.

You can do this with essentially the same code as above. Just instead of loading the ‘ner’ model, you now load the ‘sentiment’ model:

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

# make a sentence
sentence = Sentence('I love Berlin and New York.')

# load the sentiment tagger
tagger = Classifier.load('sentiment')

# run sentiment analysis over sentence
tagger.predict(sentence)

# print the sentence with all annotations
print(sentence)

This should print:

Sentence[7]: "I love Berlin and New York." → POSITIVE (0.9982)

The output shows that the sentence “I love Berlin and New York.” was tagged as having POSITIVE sentiment.

Summary

Congrats, you now know how to use Flair to find entities and detect sentiment!

Next steps

If you want to know more about Flair, next check out Tutorial 1 that gives an intro into the basics of Flair!