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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- text-classification |
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- suicidal-detection |
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pipeline_tag: text-classification |
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datasets: |
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- jsfactory/mental_health_reddit_posts |
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metrics: |
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- accuracy |
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base_model: |
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- distilbert/distilbert-base-uncased |
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library_name: transformers |
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--- |
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# Suicidal Detection System |
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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. |
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Example output |
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| Text Input | Label | Score | |
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| :------------------------------- | :--------| :---- | |
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| "I want to jump off this bridge" | Suicidal | 0.89 | |
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## Example |
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```python |
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from transformers import pipeline, DistilBertTokenizer, DistilBertForSequenceClassification |
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tokenizer = DistilBertTokenizer.from_pretrained("Kebinnuil/suicidal_detection_model") |
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model = pipeline("text-classification", model="Kebinnuil/suicidal_detection_model") |
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result = model("I want to jump off the bridge") |
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print(result) |
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``` |
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## Training Metrics |
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The dataset was split into 80/10/10 for train/validation/test set. Table below shows the result of the model's training metrics. |
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| Epoch | Training Loss | Validation Loss | Accuracy | AUC | |
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| :---- | :------------ | :-------------- | :------- | :------- | |
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| 1 | 0.442800 | 0.348061 | 0.838000 | 0.925000 | |
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| 2 | 0.304100 | 0.331631 | 0.850000 | 0.935000 | |
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| 3 | 0.261600 | 0.329701 | 0.851000 | 0.936000 | |
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## Classification Report |
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| Class | Precision | Recall | F1-score | Support | |
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| :---- | :-------- | :----- | :------- | :------ | |
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| 0 | 0.87 | 0.84 | 0.85 | 1211 | |
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| 1 | 0.84 | 0.87 | 0.86 | 1189 | |
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**Accuracy**: 0.86 |
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**Macro avg**: Precision 0.86, Recall 0.86, F1-score 0.86 |
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**Weighted avg**: Precision 0.86, Recall 0.86, F1-score 0.86 |
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**Total samples**: 2400 |
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## Mapping Config |
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Please follow the config.json |
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