--- library_name: transformers tags: - text-classification - burnout-detection - lora - peft - mental-health - workplace-stress metrics: - mae - mse license: apache-2.0 language: - en --- # 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 | Parameter | Value | |---|---| | Base model | `roberta-base` | | LoRA rank | 8 | | LoRA alpha | 16 | | Dropout | 0.1 | | Target modules | `query`, `value` | | Learning rate | 2e-4 | | Batch size | 16 (train) / 32 (eval) | | Epochs | 10 (early stopping, patience=3) | | Max sequence length | 128 tokens | | Optimizer | AdamW + 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. | Metric | Score | |---|---| | **Best Validation MAE** | **0.0534** | | Final Epoch MAE | 0.0684 | | Final Epoch MSE | 0.0069 | > 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} } ```
