Upload README.md

README.md

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+---
+language: en
+tags:
+- suicide-detection
+- mental-health
+- bert
+- text-classification
+- pytorch
+- safetensors
+license: mit
+datasets:
+- suicide-watch
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: BERT Suicide Risk Detection
+  results:
+  - task:
+      type: text-classification
+      name: Suicide Risk Detection
+    dataset:
+      type: suicide-watch
+      name: Suicide Detection Dataset
+    metrics:
+    - type: accuracy
+      value: 0.9772
+      name: Accuracy
+    - type: f1
+      value: 0.9772
+      name: F1 Score
+    - type: precision
+      value: 0.9773
+      name: Precision
+    - type: recall
+      value: 0.9772
+      name: Recall
+---
+
+# BERT Suicide Risk Detection Model
+
+## Model Description
+
+This is a fine-tuned BERT model for suicide risk detection in text. The model can classify text as either "suicide" (indicating potential suicide risk) or "non-suicide" (indicating no immediate risk).
+
+## Model Performance
+
+- **Accuracy**: 97.72%
+- **F1 Score**: 97.72%
+- **Precision**: 97.73%
+- **Recall**: 97.72%
+
+## Intended Use
+
+This model is designed to assist mental health professionals and support systems in identifying potentially at-risk individuals. It should **NOT** be used as a standalone diagnostic tool.
+
+## Usage
+
+```python
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_name = "Akashpaul123/bert-suicide-detection"
+tokenizer = BertTokenizer.from_pretrained(model_name)
+model = BertForSequenceClassification.from_pretrained(model_name)
+
+# Example usage
+text = "I'm feeling really down and don't know if I can keep going."
+inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True, padding=True)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    
+suicide_prob = predictions[0][1].item()
+non_suicide_prob = predictions[0][0].item()
+
+print(f"Suicide probability: {suicide_prob:.4f}")
+print(f"Non-suicide probability: {non_suicide_prob:.4f}")
+```
+
+## Training Data
+
+The model was trained on the Suicide Detection dataset containing 232,074 samples with balanced classes (50% suicide, 50% non-suicide).
+
+## Training Details
+
+- **Model**: bert-base-uncased
+- **Epochs**: 5
+- **Batch Size**: 32
+- **Learning Rate**: 2e-5
+- **Max Length**: 512
+- **Optimizer**: AdamW
+- **Hardware**: A100 GPU
+
+## Ethical Considerations
+
+⚠️ **Important Notice**: This model is a tool to assist in suicide risk assessment and should not replace professional mental health evaluation. Always consult with qualified mental health professionals for proper assessment and intervention.
+
+### Limitations
+
+- The model may produce false positives or false negatives
+- It should be used as part of a comprehensive mental health assessment system
+- Regular monitoring and validation are recommended
+- The model's performance may vary across different populations and contexts
+
+## License
+
+This model is released under the MIT License.
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@model{akashpaul2024bert-suicide-detection,
+  title={BERT Suicide Risk Detection Model},
+  author={Akash Paul},
+  year={2024},
+  url={https://huggingface.co/Akashpaul123/bert-suicide-detection}
+}
+```
+
+## Contact
+
+For questions or issues, please contact through the Hugging Face model page.