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Rabbit-bot/FRIDAY-roberta-burnout-lora

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face
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FRIDAY - Burnout Detection Model

A RoBERTa-base model fine-tuned with LoRA (Low-Rank Adaptation) for predicting burnout risk scores from text. Given a serialised telemetry string or free-form workplace text, the model outputs a continuous burnout score in [0, 1] (higher = greater burnout risk).

Model Details

  • —Developed by: Rabbit-bot
  • —Model type: Text Classification
  • —Language(s): English
  • —License: MIT
  • —Finetuned from: roberta-base

Uses

Intended Uses

  • —Burnout signal detection in employee feedback and workplace messages
  • —Passive stress monitoring from mobile telemetry
  • —Component score in larger wellbeing pipelines (e.g. blended with heuristic agents)
  • —Research on workplace stress language patterns

Out-of-Scope Uses

  • —Clinical diagnosis of burnout or any mental health condition
  • —Real-time employee surveillance without explicit informed consent
  • —Non-English text (model was trained on English only)
  • —Medical decision-making of any kind

Training Details

ParameterValue
Base modelroberta-base
LoRA rank8
LoRA alpha16
Dropout0.1
Target modulesquery, value
Learning rate2e-4
Batch size16 (train) / 32 (eval)
Epochs10 (early stopping, patience=3)
Max sequence length128 tokens
OptimizerAdamW + warmup (6%)

Training Data

Fine-tuned on FRIDAY Synthetic Burnout Telemetry — a labelled dataset of serialised Android sensor telemetry strings paired with continuous burnout scores in [0, 1], generated to reflect realistic mobile usage patterns across low, medium, high, and critical burnout conditions.

Evaluation

Evaluated on a held-out test split (10% of training data, stratified). The model is a regression head — MAE and MSE are the primary metrics.

MetricScore
Best Validation MAE0.0534
Final Epoch MAE0.0684
Final Epoch MSE0.0069
MAE of 0.0534 on a [0, 1] scale means the model's burnout score predictions are off by ~5.3 percentage points on average — suitable for risk-tier classification (low / medium / high / critical).

Limitations

  • —Trained on synthetic telemetry — real-world performance may vary until validated against labelled naturalistic data (WESAD, StudentLife, SWELL-KW)
  • —English-only; does not generalise to other languages
  • —Should not replace professional mental health assessment
  • —Battery and charging heuristics used in training may not transfer across device manufacturers
  • —Outputs a risk score, not a diagnosis — always interpret in context

Citation

If you use this model in research, please cite:

bibtex
@misc{friday-burnout-lora-2025,
  author    = {Rabbit-bot},
  title     = {FRIDAY: RoBERTa-LoRA Burnout Detection Model},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/Rabbit-bot/FRIDAY-roberta-burnout-lora}
}