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Text classification AI models are an essential tool for anyone looking to analyze and categorize large volumes of text data. These models can be used for a variety of applications, including sentiment analysis, spam filtering, topic modeling, and more.

To train a text classification AI model, you will need labeled data, meaning that the text has already been categorized. The model learns from this data and then applies what it has learned to classify new, unseen text. The accuracy of the model depends on a variety of factors, including the quality and quantity of the training data, the complexity of the text, and the choice of model.

To ensure that your text classification AI model remains accurate and effective, it's important to continually evaluate and improve the model over time. This may involve adding new labeled data, tweaking the model's hyperparameters, or trying out different algorithms.

distilbert-base-uncased-finetuned-sst-2-english cover image
$0.0005 / sec
  • text-classification

DistilBERT-base-uncased-finetuned-sst-2-english achieved an accuracy of 0.91 on the glue dataset, with a loss of 0.39 and an F1 score of 0.91. The model was trained on the sst2 dataset with a configuration of default and a split of train.