Update README.md

README.md

@@ -11,18 +11,55 @@ metrics:
 - f1
 ---
 
-# Model Card for Model ID
+This model is a fine-tuned version of the BERT language model, specifically adapted for multi-label classification tasks in the 
+financial regulatory domain. It is built upon the pre-trained ProsusAI/finbert model, which has been further fine-tuned using a diverse 
+dataset of financial regulatory texts. This allows the model to accurately classify text into multiple relevant categories simultaneously.
 
-<!-- Multilabel Classification -->
+# Model Architecture
 
+- **Base Model**: BERT
+- **Pre-trained Model**: ProsusAI/finbert
+- **Task**: Multi-label classification
 
-### Model Description
+## Intended Use
 
-<!-- Provide a longer summary of what this model is. -->
+This model is intended for multi-label classification tasks related to the following categories:
 
-**Multilabel Classification:** Text Data
+- Regulatory 
+- Compliance
+- Risks
 
-**Finetuned from model:** ProsusAI/finbert
+
+
+## Performance
+
+Performance metrics on the validation set:
+
+- F1 Score: 0.8637
+- ROC AUC: 0.9044
+- Accuracy: 0.6155
+
+## Limitations and Ethical Considerations
+
+- This model's performance may vary depending on the specific nature of the text data and label distribution.
+- Class imbalance in the dataset.
+
+## Dataset Information
+
+- **Training Dataset**: Number of samples: 6562
+- **Validation Dataset**: Number of samples: 929
+- **Test Dataset**: Number of samples: 1884
+
+## Training Details
+
+- **Training Strategy**: Fine-tuning BERT with a randomly initialized classification head.
+- **Optimizer**: Adam
+- **Learning Rate**: 1e-4
+- **Batch Size**: 16
+- **Number of Epochs**: 2
+- **Evaluation Strategy**: Epoch
+- **Weight Decay**: 0.01
+- **Metric for Best Model**: F1 Score