Update README.md

README.md

@@ -31,7 +31,7 @@ This model is a bidirectional LSTM neural network designed for musical era class
 
 ## Model description
 
-The EraClassifierBiLSTM-134M is a bidirectional LSTM neural network specifically designed for classifying musical compositions into historical eras based on MIDI data analysis. This large model variant (~134M parameters) offers superior performance compared to the compact 4.76M version, making it suitable for applications requiring higher accuracy.
+The EraClassifierBiLSTM-134M is a bidirectional LSTM neural network specifically designed for classifying musical compositions into historical eras based on MIDI data analysis. This large model variant (~134M parameters) offers superior performance compared to the [compact 4.76M version](https://huggingface.co/TiMauzi/EraClassifierBiLSTM-4.76M), making it suitable for applications requiring higher accuracy.
 
 ### Architecture
 - **Model Type**: Custom Bidirectional LSTM (BiLSTM)
@@ -185,7 +185,7 @@ Below is the full training metrics plot, showing loss, accuracy, and F1-score tr
 
 [<img src="training_metrics.png" alt="Training Metrics" width="500"/>](training_metrics.png)
 
-The training shows decent convergence with the model reaching its best performance around step 40,000 (epoch 2.06). The larger model capacity allows for faster learning and better final performance compared to the 4.76M variant. The training loss decreases more rapidly while validation metrics show stable improvement, indicating effective use of the increased model capacity without overfitting. The model achieves its peak F1 score of 0.5121 at step 40,000, which was selected as the best checkpoint.
+The training shows decent convergence with the model reaching its best performance around step 40,000 (epoch 2.06). The larger model capacity allows for faster learning and better final performance compared to the [4.76M variant](https://huggingface.co/TiMauzi/EraClassifierBiLSTM-4.76M). The training loss decreases more rapidly while validation metrics show stable improvement, indicating effective use of the increased model capacity without overfitting. The model achieves its peak F1 score of 0.5121 at step 40,000, which was selected as the best checkpoint.
 
 ### Framework versions