Update lm_finetuning.py

lm_finetuning.py

@@ -96,7 +96,7 @@ def main():
     # setup model
     tokenizer = AutoTokenizer.from_pretrained(opt.model, local_files_only=not network)
     model = AutoModelForSequenceClassification.from_pretrained(
-        opt.model, num_labels=dataset[opt.split_train].features['label'].num_classes, local_files_only=not network)
+        opt.model, num_labels=6, local_files_only=not network)
     tokenized_datasets = dataset.map(
         lambda x: tokenizer(x["text"], padding="max_length", truncation=True, max_length=opt.seq_length),
         batched=True)
@@ -116,7 +116,7 @@ def main():
             train_dataset=tokenized_datasets[opt.split_train],
             eval_dataset=tokenized_datasets[opt.split_validation],
             compute_metrics=compute_metric_search,
-            model_init=lambda x: AutoModelForSequenceClassification.from_pretrained(opt.model, num_labels=dataset[opt.train_split].features['label'].num_classes, local_files_only=not network, return_dict=True)
+            model_init=lambda x: AutoModelForSequenceClassification.from_pretrained(opt.model, num_labels=6, local_files_only=not network, return_dict=True)
         )
         # parameter search
         if PARALLEL:
@@ -150,7 +150,7 @@ def main():
 
     # evaluation
     model = AutoModelForSequenceClassification.from_pretrained(
-        opt.model, num_labels=dataset[opt.train_split].features['label'].num_classes, local_files_only=not network)
+        opt.model, num_labels=6, local_files_only=not network)
     trainer = Trainer(
         model=model,
         args=TrainingArguments(
@@ -162,7 +162,7 @@ def main():
         eval_dataset=tokenized_datasets[opt.split_test],
         compute_metrics=compute_metric_all,
         model_init=lambda x: AutoModelForSequenceClassification.from_pretrained(
-        opt.model, num_labels=dataset[opt.train_split].features['label'].num_classes, local_files_only=not network, return_dict=True)
+        opt.model, num_labels=6, local_files_only=not network, return_dict=True)
     )
     summary_file = pj(opt.output_dir, opt.summary_file)
     if not opt.skip_eval: