bert-suicide-detection

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

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:

@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.