| | --- |
| | license: afl-3.0 |
| | --- |
| | # Suicidal |
| | This text categorization model can predict if a word sequence is suicidal (1) or not (0). |
| |
|
| | ## Data |
| | The model was trained on the [Suicide and Depression Dataset](https://www.kaggle.com/) obtained from Kaggle. The dataset was taken from Reddit and contains 232,074 data divided into two categories: suicide and non-suicide. |
| |
|
| | ## Parameters |
| | The model fine-tuning was conducted on 1 epoch, with a batch size of 6, and a learning rate of 0.00001. Due to limited computing resources and time, we were unable to scale up the number of epochs and batch size. |
| |
|
| | ## Performance |
| | Following fine-tuning on the mentioned dataset, the model generated the subsequent results: |
| | - Accuracy: 0.9792 |
| | - Recall: 0.9788 |
| | - Precision: 0.9677 |
| | - F1 Score: 0.9732 |
| |
|
| | ## How to Use |
| | Import the model from the transformers library: |
| | ``` |
| | from transformers import AutoTokenizer, AutoModel |
| | tokenizer = AutoTokenizer.from_pretrained("Aryan4/suicidal") |
| | model = AutoModel.from_pretrained("Aryan4/suicidal") |
| | ``` |
| |
|
| | ## Resources |
| | For more resources, including the source code, please refer to the GitHub repository [Aryanstha/suicidal-text-detection](https://github.com/Aryanstha/). |
| |
|
