diff --git a/cprt/model/cprt_model.py b/cprt/model/cprt_model.py
index f02ef8e..bc30b89 100644
--- a/cprt/model/cprt_model.py
+++ b/cprt/model/cprt_model.py
@@ -3,7 +3,7 @@
 import torch
 from lightning import LightningModule
 from torch import FloatTensor, LongTensor, Tensor, nn
-from torchmetrics.text import BERTScore, BLEUScore, Perplexity
+from torchmetrics.text import Perplexity, ROUGEScore
 from transformers import GPT2LMHeadModel, GPT2Tokenizer
 from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
 
@@ -39,8 +39,7 @@ def __init__(
 
         self.train_perplexity = Perplexity(ignore_index=-100)
         self.val_perplexity = Perplexity(ignore_index=-100)
-        self.val_bleu_score = BLEUScore()
-        self.val_bert_scores = BERTScore()
+        self.val_rouge_scores = ROUGEScore()
 
     def _add_cross_attention_to_llm(self) -> None:
         """Add Cross-Attention layers to all decoder blocks."""
@@ -131,18 +130,17 @@ def validation_step(self, batch: CprtData, batch_idx: int) -> None:
         self.val_perplexity.update(out["logits"], batch.labels)
         input_text = self.text_tokenizer.batch_decode(batch.info, skip_special_tokens=True)
         generated_text = self.text_tokenizer.batch_decode(torch.argmax(out["logits"], dim=-1), skip_special_tokens=True)
-        self.val_bleu_score.update(generated_text, input_text)
-        # self.val_bert_scores.update(generated_text, input_text)
+        self.val_rouge_scores.update(generated_text, input_text)
+        if batch_idx == 0:
+            print(generated_text)
         torch.cuda.empty_cache()
 
     def on_validation_epoch_end(self) -> None:
         self.log("metrics/val_perplexity", self.val_perplexity.compute())
         self.val_perplexity.reset()
-        self.log("metrics/val_bleu", self.val_bleu_score.compute())
-        self.val_bleu_score.reset()
-        # bert_score: Dict[str, Tensor] = self.val_bert_scores.compute()  # type: ignore[assignment]
-        # self.log_dict({f"metrics/val_bert_{k}": v.mean() for k, v in bert_score.items()})
-        # self.val_bert_scores.reset()
+        rouge_scores: Dict[str, Tensor] = self.val_rouge_scores.compute()
+        self.log_dict({f"metrics/val_{k}": v.mean() for k, v in rouge_scores.items()})
+        self.val_bert_scores.reset()
         for idx, layer in enumerate(self.cprt_llm.transformer.h):
             self.log(f"gates/layer_{idx}_attn_gate", layer.attn_gate.item())
             self.log(f"gates/layer_{idx}_ff_gate", layer.ff_gate.item())