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.