readme: ---
  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/).
  