This function imports the flair.models module from the Flair NLP Python
library, providing access to several powerful models tailored for NLP tasks.
Here are three primary methods available in this module:
TextClassifier: This method represents a neural network model designed for text classification tasks. Given a piece of text, it predicts a class label or category. For instance, it can be used to classify movie reviews as positive or negative.
SequenceTagger: Tailored for tasks like Named Entity Recognition (NER) and Part-of-Speech (POS) tagging, this method annotates sequences of words. For NER, it could tag entities in a sentence such as locations, persons, or organizations. For POS tagging, it can label each word in a sentence with its grammatical role like noun, verb, adjective, etc.
LanguageModel: This method represents a model trained to predict the next word in a sequence, making it powerful for tasks like text generation and completion. It learns the statistical properties and structure of the language, and can be the base for transfer learning in other NLP tasks.