Update utils.py

utils.py

@@ -193,21 +193,6 @@ def predict(text,
         pass
 
 
-#Funktion, die den gegebenen Text aus dem Datenset gruppiert
-def group_texts(examples, block_size):
-    # Concatenate all texts.
-    concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
-    total_length = len(concatenated_examples[list(examples.keys())[0]])
-    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
-    # customize this part to your needs.
-    total_length = (total_length // block_size) * block_size
-    # Split by chunks of max_len.
-    result = {
-        k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
-        for k, t in concatenated_examples.items()
-    }
-    result["labels"] = result["input_ids"].copy()
-    return result
 
 #Funktion, die der trainer braucht, um das Training zu evaluieren - mit einer Metrik
 def compute_metrics(eval_pred):