Employee Sentiment Classifier — DistilRoBERTa (Fine-tuned)
This model is a fine-tuned version of j-hartmann/emotion-english-distilroberta-base on a custom HR feedback dataset containing employee survey responses.
It is designed to classify text responses into the following sentiment categories:
- Disengaged
- Content
- Engaged
- At Risk of Leaving
Model Details
- Base Model:
distilroberta-base(via j-hartmann's emotion model) - Fine-tuned on: Employee survey feedback
- Framework: Hugging Face Transformers
- Training: Multi-class classification with W&B sweeps for hyperparameter tuning
Labels
| Label | Description |
|---|---|
| 0 | Disengaged |
| 1 | Content |
| 2 | Engaged |
| 3 | At Risk of Leaving |
Evaluation Metrics
Evaluated on a held-out test set of employee reviews:
- Accuracy: 91.75%
- Macro F1 Score: 91.69%
- Eval Loss: 0.380
These metrics indicate strong generalization on multi-class sentiment prediction in real HR text data.
💡 Intended Use
This model is intended for analyzing internal employee sentiment from free-text responses, especially for HR and PeopleOps use cases (e.g. engagement surveys, exit feedback, etc.)
👩💻 Author
Fine-tuned and maintained by @yaagni
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