Fixed alignments

README.md

@@ -14,7 +14,7 @@ metrics:
 # distilbert-base-turkish-cased-emotion
 
 ## Model description:
-[Distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) finetuned on the emotion dataset using HuggingFace Trainer with below Hyperparameters
+[Distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) finetuned on the emotion dataset (Translated to Turkish via Google Translate API) using HuggingFace Trainer with below Hyperparameters
 ```
  learning rate 2e-5, 
  batch size 64,
@@ -25,12 +25,14 @@ metrics:
 
 | Model | Accuracy | F1 Score |  Test Sample per Second |
 | --- | --- | --- | --- |
-| [Distilbert-base-turkish-cased-emotion](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) | 83.25 | 83.17 | 232.197 |
+| [Distilbert-base-turkish-cased-emotion](https://huggingface.co/zafercavdar/distilbert-base-turkish-cased-emotion) | 83.25 | 83.17 | 232.197 |
 
 ## How to Use the model:
 ```python
 from transformers import pipeline
-classifier = pipeline("text-classification",model='zafercavdar/distilbert-base-turkish-cased-emotion', return_all_scores=True)
+classifier = pipeline("text-classification",
+                       model='zafercavdar/distilbert-base-turkish-cased-emotion',
+                       return_all_scores=True)
 prediction = classifier("Bu kütüphaneyi seviyorum, en iyi yanı kolay kullanımı.", )
 print(prediction)