Update README.md

README.md

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 # 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.
 
-For more information and to view the source codes, please refer to the GitHub repository [gohjiayi/suicidal-text-detection](https://github.com/gohjiayi/suicidal-text-detection/).
+## 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")
+```
 
----
-license: mit
----
+## 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/).