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.

Akashpaul123
/

bert-suicide-detection