Few-shot tuning works wonders in niche domains and reduces data collection costs。
Users expect conversational AI to “understand” them better. To achieve this, could implemented:
Sentiment Analysis: A fine-tuned Roberta model analyzes user sentiment in real-time (e.g., happy, confused, angry).
Contextual Memory: Maintains a summary of conversation history, ensuring the bot provides contextually relevant responses.
from transformers import pipeline
sentiment_analyzer = pipeline("sentiment-analysis", model="roberta-base")
result = sentiment_analyzer("I'm feeling great today!")
print(result)
Cost Control in Model Deployment
Quantization: Use Hugging Face’s BitsAndBytes library to quantize models to INT8, significantly reducing computational costs.
Service Splitting: Route user requests to different models (light/standard/enhanced versions) based on complexity.