Fine-Tuning for Classification: Unlocking Multilabel and Multilingual Use Cases

Cohere has updated its fine-tuning capabilities for classification models, aiming to enhance performance and expand options for multilabel and multilingual classification. This advancement allows enterprises to employ fewer data points, with a new minimum of 32 text-label pairs required for effective model training, and results in an average accuracy improvement of 30% for small datasets. The improved system also delivers significantly higher throughput, processing over 2000 examples per second, and supports multilabel classification, enabling models to predict multiple categories simultaneously. Additionally, it offers a choice between English-only and multilingual base models, enhancing flexibility and accuracy across various language tasks. These developments are designed to facilitate new use cases in document categorization, customer support, healthcare, and more, offering a more robust and efficient classification toolset for enterprises.