readme: # Suicidal-ELECTRA
  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: 0.9792
  - Recall: 0.9788
  - Precision: 0.9677
  - F1 Score: 0.9732
  
  ## How to Use
  Load the model via the transformers library:
  ```
  from transformers import AutoTokenizer, AutoModel
  tokenizer = AutoTokenizer.from_pretrained("gooohjy/suicidal-electra")
  model = AutoModel.from_pretrained("gooohjy/suicidal-electra")
  ```
  
  ## 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/).