FastText, employing Continuous Bag of Words and Skip-gram models for vectorization, is particularly effective for chatbots requiring advanced language semantics and sub-word understanding, although a more extensive training dataset is often needed for peak performance.
Use Case in Chatbots:
FastText is ideal for more complex chatbot applications that require a deeper understanding of language semantics. It's especially useful for chatbots that need to understand slang, abbreviations, or sub-word details.
For optimal performance, FastText generally requires more data compared to TF-IDF, at least 30-50 samples per intent. The model was initially trained on Wikipedia data, which provides it a rich language understanding capability.
Although FastText might take longer to train than TF-IDF, the investment often pays off in terms of improved performance and understanding of natural language.
Updated about 1 month ago