suicidal_detection_model

Suicidal Detection System

This is a fine-tuned model based on a transformer architecture distilBERT for detecting suicidal intent or ideation in text. This model purpose is for text-classification in suicidal detection system.

Example output

Example

from transformers import pipeline, DistilBertTokenizer, DistilBertForSequenceClassification

tokenizer = DistilBertTokenizer.from_pretrained("Kebinnuil/suicidal_detection_model")
model = pipeline("text-classification", model="Kebinnuil/suicidal_detection_model")

result = model("I want to jump off the bridge")
print(result)

Training Metrics

The dataset was split into 80/10/10 for train/validation/test set. Table below shows the result of the model's training metrics.

Classification Report

Accuracy: 0.86
Macro avg: Precision 0.86, Recall 0.86, F1-score 0.86
Weighted avg: Precision 0.86, Recall 0.86, F1-score 0.86
Total samples: 2400

Mapping Config

Please follow the config.json