Transformer-Based Text Classification

Text classification is a Natural Language Processing (NLP) task where a model assigns predefined categories to a given text. These models are widely used for sentiment analysis, spam detection, topic labeling, and more.

Hugging Face provides pre-trained transformer models for text classification, such as BERT, DistilBERT, RoBERTa, and XLNet, which are fine-tuned on various datasets. These models analyze input text and generate predictions with confidence scores.

For instance, DistilBERT (distilbert-base-uncased-finetuned-sst-2-english) is optimized for sentiment analysis, classifying text as positive or negative. These models are accessible via the Hugging Face Inference API and can be easily integrated into applications.

By leveraging pre-trained transformers, businesses and developers can implement text classification efficiently without requiring extensive training data or computing resources.