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README.md

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 ---
 library_name: transformers
-tags: []
+tags:
+- text-classification
+- burnout-detection
+- lora
+- peft
+- mental-health
+- workplace-stress
+metrics:
+- mae
+- mse
+license: apache-2.0
+language:
+- en
 ---
+# FRIDAY - Burnout Detection Model
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+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
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Developed by:** Rabbit-bot
+- **Model type:** Text Classification
+- **Language(s):** English
+- **License:** MIT
+- **Finetuned from:** roberta-base
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+## Intended Uses
 
-### Recommendations
+- 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
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+## Out-of-Scope Uses
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+- 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
 
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
+| 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
 
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+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
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+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}
+}
+```