Update README.md

README.md

@@ -63,7 +63,7 @@ It achieves the following results:
 - WER on ShEMO dev set:            32.85
 - WER on Common Voice 13 test set: 19.21
 
-## Evaluation
+## Evaluation results 🌡️
 |                             Checkpoint Name                              | WER on ShEMO dev set | WER on Common Voice 13 test set | Max :) |
 | :---------------------------------------------------------------------------------------------------------------: | :------:  | :-------: | :---:     |
 | [m3hrdadfi/wav2vec2-large-xlsr-persian-v3](https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v3)       | 46.55     | **17.43** | 46.55     |
@@ -75,7 +75,8 @@ As you can see, my model performs better in maximum case :D
 
 ## Training procedure
 
-#### Training hyperparameters
+##### Training hyperparameters
+**Training hyperparameters**
 
 The following hyperparameters were used during training:
 - learning_rate: 1e-05
@@ -90,6 +91,8 @@ The following hyperparameters were used during training:
 - training_steps: 2000
 - mixed_precision_training: Native AMP
 
+You may need *gradient_accumulation* because you need more batch size.
+
 #### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Wer    |
@@ -168,9 +171,14 @@ As you can see this model performs better with WER metric on validation(evaluati
 
 The script used for training can be found [here](https://colab.research.google.com/github/m3hrdadfi/notebooks/blob/main/Fine_Tune_XLSR_Wav2Vec2_on_Persian_ShEMO_ASR_with_%F0%9F%A4%97_Transformers_ipynb.ipynb).
 
+Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for more information.
+
 #### Framework versions
 
 - Transformers 4.35.2
 - Pytorch 2.1.0+cu118
 - Datasets 2.15.0
-- Tokenizers 0.15.0
+- Tokenizers 0.15.0
+
+Contact us 🤝
+If you have any technical question regarding the model, pretraining, code or publication, please create an issue in the repository. This is the *best* way to reach us.