Update README.md

README.md

@@ -50,40 +50,13 @@ The fine-tuned model outperforms the base model significantly in terms of Charac
 ## Performance Comparison Charts
 
 ### WER & CER Comparison
-```python
-import matplotlib.pyplot as plt
 
-categories = ["WER", "CER"]
-base_values = [1.3435, 1.1915]
-fine_tuned_values = [0.0675, 0.0193]
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/630535e0c7fed54edfaa1a75/uNTC--tLhDhJN3bXy-7P2.png)
 
-x = range(len(categories))
-plt.bar(x, base_values, width=0.4, label="Base Model", color='r', align='center')
-plt.bar(x, fine_tuned_values, width=0.4, label="Fine-Tuned Model", color='g', align='edge')
+### BLEU Score Comparison
 
-plt.xticks(x, categories)
-plt.ylabel("Error Rate")
-plt.title("WER & CER Comparison")
-plt.legend()
-plt.show()
-```
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/630535e0c7fed54edfaa1a75/BcURaYVjTabmqMI37Sw4I.png)
 
-### BLEU Score Comparison
-```python
-categories = ["BLEU", "Precision @1", "Precision @2", "Precision @3", "Precision @4"]
-base_bleu = [0.2007, 26.85, 21.65, 18.13, 15.39]
-fine_tuned_bleu = [0.8596, 93.95, 88.55, 83.82, 79.52]
-
-x = range(len(categories))
-plt.bar(x, base_bleu, width=0.4, label="Base Model", color='r', align='center')
-plt.bar(x, fine_tuned_bleu, width=0.4, label="Fine-Tuned Model", color='g', align='edge')
-
-plt.xticks(x, categories)
-plt.ylabel("Score (%)")
-plt.title("BLEU Score & Precision Comparison")
-plt.legend()
-plt.show()
-```
 
 ## How to Use
 You can load this model using the `transformers` library: