Evaluation code fixed.

examples/run_lm_finetuning.py

@@ -86,6 +86,7 @@ class TextDataset(Dataset):
             # Note that we are loosing the last truncated example here for the sake of simplicity (no padding)
             # If your dataset is small, first you should loook for a bigger one :-) and second you
             # can change this behavior by adding (model specific) padding.
+            self.examples.append(tokenizer.build_inputs_with_special_tokens(tokenized_text[-block_size:])) # DIRTY!
 
             logger.info("Saving features into cached file %s", cached_features_file)
             with open(cached_features_file, 'wb') as handle:
@@ -309,10 +310,12 @@ def evaluate(args, model, tokenizer, prefix=""):
     model.eval()
 
     for batch in tqdm(eval_dataloader, desc="Evaluating"):
-        batch = batch.to(args.device)
+        inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch)
+        inputs = inputs.to(args.device)
+        labels = labels.to(args.device)
 
         with torch.no_grad():
-            outputs = model(batch, masked_lm_labels=batch) if args.mlm else model(batch, labels=batch)
+            outputs = model(inputs, masked_lm_labels=labels) if args.mlm else model(inputs, labels=labels)
             lm_loss = outputs[0]
             eval_loss += lm_loss.mean().item()
         nb_eval_steps += 1
@@ -540,4 +543,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    main()