Update metrics.py

metrics.py

@@ -5,18 +5,22 @@ from bert_score import score as bert_score
 
 def compute_scores(predictions, references):
     scorer = rouge_scorer.RougeScorer(["rouge1", "rouge2", "rougeL"], use_stemmer=True)
-    scores = {"ROUGE-1": [], "ROUGE-2": [], "ROUGE-L": []}
-
+    scores = {"ROUGE-1": [], "ROUGE-2": [], "ROUGE-L": [], "BERT-F1": []}
+    
+    # Compute ROUGE scores
     for pred, ref in zip(predictions, references):
         rouge_scores = scorer.score(pred, ref)
         scores["ROUGE-1"].append(rouge_scores["rouge1"].fmeasure)
         scores["ROUGE-2"].append(rouge_scores["rouge2"].fmeasure)
         scores["ROUGE-L"].append(rouge_scores["rougeL"].fmeasure)
-
+    
+    # Compute BERTScore F1
+    P, R, F1 = bert_score(predictions, references, lang="en", rescale_with_baseline=True)
+    scores["BERT-F1"].extend(F1.tolist())
+    
     return {key: np.mean(value) for key, value in scores.items()}
 
 def save_scores(scores, model_name, experiment_type, dataset_name):
     with open("rouge_results.csv", mode="a", newline="") as file:
         writer = csv.writer(file)
-        writer.writerow([model_name, experiment_type, dataset_name, scores["ROUGE-1"], scores["ROUGE-2"], scores["ROUGE-L"]])
-   
+        writer.writerow([model_name, experiment_type, dataset_name, scores["ROUGE-1"], scores["ROUGE-2"], scores["ROUGE-L"], scores["BERT-F1"]])