Update README.md

README.md

@@ -90,7 +90,6 @@ This is how to use the model with Faster-Whisper.
   
     export LD_LIBRARY_PATH=`python3 -c 'import os; import nvidia.cublas.lib; import nvidia.cudnn.lib; print(os.path.dirname(nvidia.cublas.lib.__file__) + ":" + os.path.dirname(nvidia.cudnn.lib.__file__))'`
     ```
-    Big shout out to [Yasmin Moslem](https://huggingface.co/ymoslem), for solving this issue.
 
 ## Training procedure
 
@@ -112,7 +111,10 @@ This is how to use the model with Faster-Whisper.
 
 The performance of the baseline and fine-tuned model were evaluated using the BLEU and CHRF++ metrics on the validation dataset. 
 This fine-tuned model shows some improvement over the baseline model.
-
+| Model                 | BLEU  | ChrF++ |
+|-----------------------|------:|-------:|
+| Baseline             | 50.91 | 68.1  |
+| Fine-Tuned          |58.3 |  73.62 |
 ### Evaluation details
 - BLEU: Measures the overlap between predicted and reference text based on n-grams.
 - CHRF: Uses character n-grams for evaluation, making it particularly suitable for morphologically rich languages.