webesama
/

MADRS-BERT

@@ -24,21 +24,18 @@ tags:
 This model was developed to support standardized, scalable mental health assessments in both clinical and low-resource settings.
----
-## 🧠 Model Details
 - **Base model**: `bert-base-german-cased`
-- **Task**: Ordinal regression/classification (scores 0–6)
-- **Language**: German 🇩🇪
 - **Input**: Text (dialogue segment grouped by MADRS topic)
 - **Output**: Predicted score for each MADRS item (rounded integer 0–6)
 - **Training data**: Mix of real and synthetic clinician–patient interviews (MADRS-structured)
----
-## 💡 Intended Use
 This model is intended for research and development use. It is not a certified medical device. The goal is to:
 - Explore AI-assisted symptom severity assessment
@@ -88,7 +85,7 @@ model = AutoModelForSequenceClassification.from_pretrained(model_name)
 model.eval().to("cuda" if torch.cuda.is_available() else "cpu")
 ```
-### 📝 Predict on a full structured interview / Run inference:
 Assume you have a conversation log like this:
 ```python
@@ -113,20 +110,17 @@ print(scores)
 ---
-## 🙏 Acknowledgements
 Model trained and released by [Samantha Weber](https://github.com/webersamantha). Research conducted as part of efforts to improve AI-driven mental health tools. Thanks to all clinicians and collaborators who contributed to the annotated MADRS dataset.
----
-## 🧪 Evaluation
 The model was evaluated on a held-out clinical validation set and achieved strong performance under both strict and flexible scoring criteria (±1 deviation tolerance). See publication for full metrics.
----
-## 🧪 Citation
 If you use this model, please cite:
 > Weber, S. et al. (2025). "Using a Fine-tuned Large Language Model for Symptom-based Depression Evaluation" *Preprint*. https://doi.org/10.21203/rs.3.rs-6555767/v1

 This model was developed to support standardized, scalable mental health assessments in both clinical and low-resource settings.
+## Model Details
 - **Base model**: `bert-base-german-cased`
+- **Task**: Ordinal regression (scores 0–6)
+- **Language**: German
 - **Input**: Text (dialogue segment grouped by MADRS topic)
 - **Output**: Predicted score for each MADRS item (rounded integer 0–6)
 - **Training data**: Mix of real and synthetic clinician–patient interviews (MADRS-structured)
+## Intended Use
 This model is intended for research and development use. It is not a certified medical device. The goal is to:
 - Explore AI-assisted symptom severity assessment
 model.eval().to("cuda" if torch.cuda.is_available() else "cpu")
 ```
+### Predict on a full structured interview / Run inference:
 Assume you have a conversation log like this:
 ```python
 ---
+## Acknowledgements
 Model trained and released by [Samantha Weber](https://github.com/webersamantha). Research conducted as part of efforts to improve AI-driven mental health tools. Thanks to all clinicians and collaborators who contributed to the annotated MADRS dataset.
+## Evaluation
 The model was evaluated on a held-out clinical validation set and achieved strong performance under both strict and flexible scoring criteria (±1 deviation tolerance). See publication for full metrics.
+## Citation
 If you use this model, please cite:
 > Weber, S. et al. (2025). "Using a Fine-tuned Large Language Model for Symptom-based Depression Evaluation" *Preprint*. https://doi.org/10.21203/rs.3.rs-6555767/v1