Introducing LICAT framework - New revolutionized way of NLP model fine-tuning

Jan. 22, 2024 - PRLog -- Exciting news! Next week, we're launching LICAT(Language Inference Categorization Training Framework) -  our new framework for few-shot learning of cross-encoder models. It's easy to use and incredibly effective for tasks like text classification, NER, and Q&A, achieving competitive results with just 8 examples per label!

Key features and benefits of LICAT are:

🔢 A small number of examples are required - LICAT can significantly improve the accuracy of the default zero-shot classifier having just 8 examples;
📝 Can solve many different information-extraction tasks - Natural language inference is a universal task that can be applied as a setting for many other information extraction tasks, like named entity recognition of question&answering;
🌈 Can work for other classes not presented in the training set - Having all needed classes in a training set is not mandatory. Because of pre-finetuning on large amounts of NLI and classification tasks, a model will save generalisability to other classes;
⚙️ Support of a variety of cross-encoder realisations - LICAT supports different types of cross-encoders including conventional, binary and encoder-decoder architectures;
⚖️ Stable to unbalanced datasets - LICAT uses normalization techniques that allow it to work well even in the cases of unbalanced data;
🏷️ Multi-label classification support - The approach can be applied for both multi-class and multi-label classification;

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