Few shots learning

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:
Training time varies widely depending on the specific approach, but it's generally more computationally intensive.

Preferred Vectorizers:
Works best with advanced, contextual embeddings like FlauBERT.

Preprocessing Requirements:
Usually requires little to no preprocessing, as these models are good at handling raw text data.