NLP and LLMs - Evaluating NLP Models
About
An introduction to common benchmarks, metrics, and other key factors for selecting and evaluating the performance of LLMs.
Learning Objectives
You will be able to:
- Describe use cases and common methods for benchmarking.
- Evaluate performance of NLP models.
- Appraise the cost impacts of various models.
- Assess risk and impact of bias in each model.
Content
| Topic | About |
|---|---|
| Full Lesson Deck | NLP Model Evaluation |
| ROUGE-BLEU | Introduction to Hugging Face Evaluate and Rouge/SacreBLEU metrics |
Prerequisites:
- Python
- Core data science libraries
numpyandpandas - Understand, run, and evaluate classification metrics in
scikit-learn - Comfort with Hugging Face Pipeline API for NLP tasks.
References
📖 Reference Materials
Important note
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