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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.

Training Data:
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.

Training Time:
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.