Update README.md

README.md

@@ -37,7 +37,23 @@ The details about the training data used to pre-train and fine-tune these models
 
 ## Evaluation Metrics
 
-The evaluation metrics for each specific model, including accuracy, F1-score, BLEU score, or other relevant metrics, are provided in the associated research paper.
+The evaluation metrics for each specific model, including accuracy, F1-score, BLEU score, or other relevant metrics, are provided below and also in the associated research paper.
+
+| Task                           | Model                   | Evaluation Metric | Performance |
+|--------------------------------|-------------------------|-------------------|-------------|
+| Language model-based Embedding | FastText                | MRR               | 0.46        |
+| Language Model                 | BERT                    | Perplexity        | 48.05       |
+| Text Classification            | TF-IDF + SVM            | Accuracy          | 0.79        |
+|                                | TF-IDF + SVM            | F1-score          | 0.78        |
+|                                | FastText + SVM          | Accuracy          | 0.86        |
+|                                | FastText + SVM          | F1-score          | 0.86        |
+|                                | BERT                    | Accuracy          | 0.89        |
+|                                | BERT                    | F1-score          | 0.89        |
+| Token Classification           | BERT POS-tagger         | Accuracy          | 0.86        |
+|                                | BERT POS-tagger         | Macro F1-score    | 0.67        |
+| Machine Translation            | Text Translation azb2fa | SacreBLEU         | 10.34       |
+|                                | Text Translation fa2azb | SacreBLEU         | 8.07        |
+
 
 ## Acknowledgments