| # Suicidal-BERT | |
| This text classification model predicts whether a sequence of words are suicidal (1) or non-suicidal (0). | |
| ## Data | |
| The model was trained on the [Suicide and Depression Dataset](https://www.kaggle.com/nikhileswarkomati/suicide-watch) obtained from Kaggle. The dataset was scraped from Reddit and consists of 232,074 rows equally distributed between 2 classes - suicide and non-suicide. | |
| ## Parameters | |
| The model fine-tuning was conducted on 1 epoch, with batch size of 6, and 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 | |
| The model has achieved the following results after fine-tuning on the aforementioned dataset: | |
| - Accuracy: 0.9757 | |
| - Recall: 0.9669 | |
| - Precision: 0.9701 | |
| - F1 Score: 0.9685 | |
| ## How to Use | |
| Load the model via the transformers library: | |
| ``` | |
| from transformers import AutoTokenizer, AutoModel | |
| tokenizer = AutoTokenizer.from_pretrained("gooohjy/suicidal-bert") | |
| model = AutoModel.from_pretrained("gooohjy/suicidal-bert") | |
| ``` | |
| ## Resources | |
| For more resources, including the source code, please refer to the GitHub repository [gohjiayi/suicidal-text-detection](https://github.com/gohjiayi/suicidal-text-detection/). |