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---
language: en
license: apache-2.0
tags:
- text-classification
- suicidal-detection
pipeline_tag: text-classification
datasets:
- jsfactory/mental_health_reddit_posts
metrics:
- accuracy
base_model:
- distilbert/distilbert-base-uncased
library_name: transformers
---
# 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
| Text Input | Label | Score |
| :------------------------------- | :--------| :---- |
| "I want to jump off this bridge" | Suicidal | 0.89 |
## Example
```python
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.
| Epoch | Training Loss | Validation Loss | Accuracy | AUC |
| :---- | :------------ | :-------------- | :------- | :------- |
| 1 | 0.442800 | 0.348061 | 0.838000 | 0.925000 |
| 2 | 0.304100 | 0.331631 | 0.850000 | 0.935000 |
| 3 | 0.261600 | 0.329701 | 0.851000 | 0.936000 |
## Classification Report
| Class | Precision | Recall | F1-score | Support |
| :---- | :-------- | :----- | :------- | :------ |
| 0 | 0.87 | 0.84 | 0.85 | 1211 |
| 1 | 0.84 | 0.87 | 0.86 | 1189 |
**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
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