Transformers is Hugging Face’s open-source library for applying pretrained models to language tasks such as translation, text generation, classification, and sentiment analysis. section 1.2 section 1.3
Pretrained models learn language patterns from extensive datasets, letting practitioners adapt existing knowledge to a specific task instead of training from scratch. This reduces time and computational cost. section 1.2
The library supports practical transformer workflows:
- Fine-tune a pretrained model with a labeled dataset, such as sentence pairs marked neutral, negative, or positive for sentiment analysis. section 1.2 section 1.3
- Use zero-shot learning for tasks absent from specific training, or few-shot learning when only a small task-specific dataset is available. section 1.3
- Run computationally intensive training and inference with GPUs, including cloud GPU access through Google Colab. section 1.2
Sentiment analysis
Given sentence-and-label pairs for neutral, negative, and positive sentiment, adapt a pretrained transformer rather than building and training a model from scratch. section 1.2 section 1.3
