Update README.md

README.md

@@ -168,9 +168,9 @@ Learning rate is 1e-5 for all three models.
 
 As you can see this model performs better with WER metric on validation(evaluation) set.
 
-The script used for training can be found [here](https://colab.research.google.com/github/lnxdx/Wav2Vec2-Large-XLSR-Persian-ASR/blob/main/Fine_Tune_Wav2Vec2_Large_XLSR_on_ShEMO_for_Persian_ASR_with_%F0%9F%A4%97_Transformers.ipynb).
+📒 The script used for training can be found [here](https://colab.research.google.com/github/lnxdx/Wav2Vec2-Large-XLSR-Persian-ASR/blob/main/Fine_Tune_Wav2Vec2_Large_XLSR_on_ShEMO_for_Persian_ASR_with_%F0%9F%A4%97_Transformers.ipynb).
 
-The script used for evaluating on ShEMO and Common Voice can be found [here](https://colab.research.google.com/github/lnxdx/Wav2Vec2-Large-XLSR-Persian-ASR/blob/main/evaluate_asr_on_shemo_and_common_voice.ipynb).
+📒 The script used for evaluating on ShEMO and Common Voice can be found [here](https://colab.research.google.com/github/lnxdx/Wav2Vec2-Large-XLSR-Persian-ASR/blob/main/evaluate_asr_on_shemo_and_common_voice.ipynb).
 
 Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for more information.