Few-Shots Learning is perfect for specialized chatbots operating in niche domains where labeled data is scarce; despite its need for computational resources, it excels in delivering high performance with minimal data.
Use Case for Chatbots:
Few-Shots Learning is ideal for highly specialized chatbots where labeled data is scarce but high performance is required.
Training Data Needed:
Can perform well even with as few as 5-10 samples per intent, due to its ability to generalize from small data.
Training time varies widely depending on the specific approach, but it's generally more computationally intensive.
Works best with advanced, contextual embeddings like FlauBERT.
Usually requires little to no preprocessing, as these models are good at handling raw text data.
Updated about 1 month ago