setfit

Why we included this project

Most teams that need a text classifier don't have thousands of labeled examples ready, and SetFit is built for that exact situation. It fine-tunes a sentence transformer to produce embeddings straight from the text, so there are no prompt templates or verbalizers to write by hand. On the Customer Reviews sentiment dataset, 8 labeled examples per class gets it close to a RoBERTa Large model trained on the full 3,000 examples, while training stays fast enough for ordinary hardware. Because any multilingual sentence-transformer checkpoint on the Hub can serve as the starting point, the same recipe carries over to non-English text with little extra data. The library installs with pip, offers a trainer and classification head for building a model, and the docs and notebooks are enough to go from zero to a working classifier in one sitting.