Update lm_finetuning.py

lm_finetuning.py

@@ -118,7 +118,7 @@ def main():
             eval_dataset=tokenized_datasets[opt.split_validation],
             compute_metrics=compute_metric_search,
             model_init=lambda x: AutoModelForSequenceClassification.from_pretrained(
-                opt.model, return_dict=True, num_labels=len(data[opt.split_train]['label'][0]))
+                opt.model, return_dict=True, num_labels=len(dataset[opt.split_train]['label'][0]))
         )
         # parameter search
         if PARALLEL:
@@ -153,7 +153,7 @@ def main():
     # evaluation
     model = AutoModelForSequenceClassification.from_pretrained(
         best_model_path,
-        num_labels=len(data[opt.split_train]['label'][0]),
+        num_labels=len(dataset[opt.split_train]['label'][0]),
         local_files_only=not network)
     trainer = Trainer(
         model=model,
@@ -166,7 +166,7 @@ def main():
         eval_dataset=tokenized_datasets[opt.split_test],
         compute_metrics=compute_metric_all,
         model_init=lambda x: AutoModelForSequenceClassification.from_pretrained(
-            opt.model, return_dict=True, num_labels=len(data[opt.split_train]['label'][0]))
+            opt.model, return_dict=True, num_labels=len(dataset[opt.split_train]['label'][0]))
     )
     summary_file = pj(opt.output_dir, opt.summary_file)
     if not opt.skip_eval: