Random Entity Extraction with Flair#s NER Model

This function uses the NER model from the Flair library to randomly extract entities from the given text.

Technology Stack : Flair, SequenceTagger, Sentence

Code Type : Function

Code Difficulty : Intermediate

                
                    
def random_entity_extraction(text, model='bert-base-uncased'):
    """
    Extracts random entities from a given text using Flair's NER model.
    """
    from flair.models import SequenceTagger
    from flair.data import Sentence

    # Load the NER model
    nlp = SequenceTagger.load(model)

    # Create a sentence from the input text
    sentence = Sentence(text)

    # Perform NER
    nlp.predict(sentence)

    # Randomly select an entity and return it
    if len(sentence.get_tokens()) > 0:
        tokens = sentence.get_tokens()
        selected_token = tokens[random.randint(0, len(tokens) - 1)]
        return f"{selected_token.text} ({selected_token.tag})"
    else:
        return "No entities found in the text."