add torch.no_grad()

meaningbert.py

@@ -19,6 +19,7 @@ from typing import List, Dict
 
 import datasets
 import evaluate
+import torch
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
 
@@ -128,34 +129,35 @@ class MeaningBERT(evaluate.Metric):
         )
         scorer.eval()
 
-        # We load MeaningBERT tokenizer
-        tokenizer = AutoTokenizer.from_pretrained("davebulaval/MeaningBERT")
-
-        # We tokenize the text as a pair and return Pytorch Tensors
-        tokenize_text = tokenizer(
-            references,
-            predictions,
-            truncation=True,
-            padding=True,
-            return_tensors="pt",
-        ).to(device)
-
-        with filter_logging_context():
-            # We process the text
-            scores = scorer(**tokenize_text)
-
-        scores = scores.logits.tolist()
-
-        # Flatten the list of list of logits
-        scores = list(chain(*scores))
-
-        # Handle case of perfect match
-        if len(matching_index) > 0:
-            for matching_element_index in matching_index:
-                scores[matching_element_index] = 100
-
-        output_dict = {
-            "scores": scores,
-            "hashcode": hashcode,
-        }
+        with torch.no_grad():
+            # We load MeaningBERT tokenizer
+            tokenizer = AutoTokenizer.from_pretrained("davebulaval/MeaningBERT")
+
+            # We tokenize the text as a pair and return Pytorch Tensors
+            tokenize_text = tokenizer(
+                references,
+                predictions,
+                truncation=True,
+                padding=True,
+                return_tensors="pt",
+            ).to(device)
+
+            with filter_logging_context():
+                # We process the text
+                scores = scorer(**tokenize_text)
+
+            scores = scores.logits.tolist()
+
+            # Flatten the list of list of logits
+            scores = list(chain(*scores))
+
+            # Handle case of perfect match
+            if len(matching_index) > 0:
+                for matching_element_index in matching_index:
+                    scores[matching_element_index] = 100
+
+            output_dict = {
+                "scores": scores,
+                "hashcode": hashcode,
+            }
         return output_dict