Update Fluency_Score.py

Fluency_Score.py

@@ -3,12 +3,13 @@ import datasets
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-class FluencyScore(evaluate.Metric):
+class Fluency_Score(evaluate.Metric):
+    
     def _info(self):
         return evaluate.MetricInfo(
-            description="Computes the fluency score of a given text using a pre-trained model.",
+            description="",
             citation="",
-            inputs_description="A list of text strings to evaluate for fluency.",
+            inputs_description="",
             features=datasets.Features(
                 {
                     "texts": datasets.Value("string", id="sequence"),
@@ -16,31 +17,31 @@ class FluencyScore(evaluate.Metric):
             ),
             reference_urls=[],
         )
-
-    def __init__(self, device=None):
-        super().__init__()
+    
+    def _download_and_prepare(self, dl_manager, device=None):
         if device is None:
             device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.device = device
-
-        # Load the tokenizer and model
+        
+        # Load the tokenizer and model from the specified repository
         self.tokenizer = AutoTokenizer.from_pretrained("Baleegh/Fluency_Score")
         self.model = AutoModelForSequenceClassification.from_pretrained("Baleegh/Fluency_Score")
-        self.model.to(self.device)
+        
+        self.model.to(device)
+        self.device = device
 
-    def _compute(self, texts):
-        # Tokenize the input texts
+    def _compute(self, texts, temperature=2):
+        device = self.device
+        
         inputs = self.tokenizer(
-            texts,
-            return_tensors="pt",
-            truncation=True,
-            padding='max_length',
+            texts, 
+            return_tensors="pt", 
+            truncation=True, 
+            padding='max_length', 
             max_length=128
-        ).to(self.device)
-
-        # Get model predictions
-        with torch.no_grad():
+        ).to(device)
+        
+        with torch.inference_mode():
             output = self.model(**inputs)
-            predictions = output.logits.clip(0, 1).squeeze().tolist()  # Convert to list
+            prediction = output.logits.clip(0, 1)
 
-        return {"fluency_scores": predictions}
+        return {"classical_score": prediction}|