•1 min read•from Towards Data Science
How to Fine-Tune an SLM for Emotion Recognition
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Fine-tune a Mistral Small 3.1 to classify 15 emotions in social media text, even when your dataset is heavily imbalanced. This Python tutorial walks you through data preparation, class‑weighting, and epoch scheduling, all while keeping the model’s inference speed fast enough for real‑time applications. You’ll learn how to balance precision and recall, evaluate using macro‑averaged metrics, and deploy the fine‑tuned SLM with a lightweight API. For deeper prompt‑engineering insights, see “Automate Writing Your LLM Prompts.”
Python tutorial for fine-tuning a Mistral Small 3.1 on an imbalanced training set to classify 15 emotions in social media communication
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