Predict Sentiment Using Allennlp TextClassifier

This function uses the TextClassifier Predictor from the Allennlp library to predict the sentiment of the given text. First, it loads the pre-trained model and vocabulary, then converts the input text into a format acceptable by the model, and finally uses the predictor to get the sentiment label of the text.

Technology Stack : Allennlp, TextClassifier Predictor, Vocabulary

Code Type : Text classification

Code Difficulty : Intermediate

                
                    
def predict_sentiment(text):
    from allennlp.predictors.text_classifier import TextClassifier Predictor
    from allennlp.data import Instance, Vocabulary
    from allennlp.models import Model

    # Load the pre-trained model and vocabulary
    model = Model.load('path_to_model')
    vocab = Vocabulary.from_file('path_to_vocab.json')

    # Create an instance from the input text
    instance = Instance(
        text_field=Instance.from_dict({
            "tokens": [vocab.get_token_from_index(i) for i in text.split()]
        }),
        label_field=None  # The model is a sentiment analysis model
    )

    # Use the predictor to predict the sentiment of the text
    predictor = Predictor.from_path('path_to_model')
    prediction = predictor.predict(instance)

    return prediction.label