Update README.md

README.md

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 license: afl-3.0
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+# 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/nikhileswarkomati/suicide-watch) 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 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
+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("Aryan$/suicidal")
+```
+
+## Resources
+For more resources, including the source code, please refer to the GitHub repository [Aryanstha/suicidal-text-detection](https://github.com/Aryanstha/).