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Library/ NLP & Transformers/ Module 5 · Project 3: Customer Chat Summariser (fine-tuning T5 for text generation)/ 5.7 Evaluate

subtopic

ROUGE, human rating and fact check

10 min applied15 min research1 curated resources

On the test set compute ROUGE-1/2/L for baselines and the fine-tuned model. Then rate 30 summaries (including your own chats) with the guide: faithfulness, coverage, clarity. Check every name and number in those summaries against the chat — count summaries with made-up facts. Try 2 decoding settings and keep the best.

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ROUGE, human rating and fact check · 1x1labs.ai