Update lm_finetuning.py

lm_finetuning.py

@@ -1,13 +1,14 @@
-```
+'''
 wandb offline
 export WANDB_DISABLED='true'
 export RAY_RESULTS='ray_results'
-python lm_finetuning.py -m "roberta-large" -c "ckpt/roberta_large" --push-to-hub --hf-organization "cardiffnlp" -a "twitter-roberta-base-2019-90m-tweet-topic-multi"
+python lm_finetuning.py -m "roberta-large" -c "ckpt/roberta_large" --push-to-hub --hf-organization "cardiffnlp" -a "roberta-large-tweet-topic-multi" --split-train "train_all" --split-valid "validation_2021" --split-test "test_2021"
 python lm_finetuning.py -m "roberta-base" -c "ckpt/roberta_base" --push-to-hub --hf-organization "cardiffnlp" -a "twitter-roberta-base-2019-90m-tweet-topic-multi"
 python lm_finetuning.py -m "cardiffnlp/twitter-roberta-base-2019-90m" -c "ckpt/twitter-roberta-base-2019-90m" --push-to-hub --hf-organization "cardiffnlp" -a "twitter-roberta-base-2019-90m-tweet-topic-multi"
 python lm_finetuning.py -m "cardiffnlp/twitter-roberta-base-dec2020" -c "ckpt/twitter-roberta-base-dec2020"
 python lm_finetuning.py -m "cardiffnlp/twitter-roberta-base-dec2021" -c "ckpt/twitter-roberta-base-dec2021"
-```
+'''
+
 import argparse
 import json
 import logging
@@ -65,6 +66,9 @@ def main():
     parser = argparse.ArgumentParser(description='Fine-tuning language model.')
     parser.add_argument('-m', '--model', help='transformer LM', default='roberta-base', type=str)
     parser.add_argument('-d', '--dataset', help='', default='cardiffnlp/tweet_topic_multi', type=str)
+    parser.add_argument('--split-train', help='', required=True, type=str)
+    parser.add_argument('--split-validation', help='', required=True, type=str)
+    parser.add_argument('--split-test', help='', required=True, type=str)
     parser.add_argument('-l', '--seq-length', help='', default=128, type=int)
     parser.add_argument('--random-seed', help='', default=42, type=int)
     parser.add_argument('--eval-step', help='', default=50, type=int)
@@ -86,7 +90,7 @@ def main():
     tokenizer = AutoTokenizer.from_pretrained(opt.model, local_files_only=not network)
     model = AutoModelForSequenceClassification.from_pretrained(
         opt.model,
-        num_labels=len(dataset['train'][0]['label']),
+        num_labels=len(dataset[opt.split_train][0]['label']),
         local_files_only=not network,
         problem_type="multi_label_classification"
     )
@@ -106,11 +110,11 @@ def main():
                 eval_steps=opt.eval_step,
                 seed=opt.random_seed
             ),
-            train_dataset=tokenized_datasets["train"],
-            eval_dataset=tokenized_datasets["validation"],
+            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, return_dict=True, num_labels=dataset['train'].features['label'].num_classes)
+                opt.model, return_dict=True, num_labels=dataset[opt.split_train].features['label'].num_classes)
         )
         # parameter search
         if PARALLEL:
@@ -145,7 +149,7 @@ def main():
     # evaluation
     model = AutoModelForSequenceClassification.from_pretrained(
         best_model_path,
-        num_labels=dataset['train'].features['label'].num_classes,
+        num_labels=dataset[opt.split_train].features['label'].num_classes,
         local_files_only=not network)
     trainer = Trainer(
         model=model,
@@ -154,11 +158,11 @@ def main():
             evaluation_strategy="no",
             seed=opt.random_seed
         ),
-        train_dataset=tokenized_datasets["train"],
-        eval_dataset=tokenized_datasets["test"],
+        train_dataset=tokenized_datasets[opt.split_train],
+        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=dataset['train'].features['label'].num_classes)
+            opt.model, return_dict=True, num_labels=dataset[opt.split_train].features['label'].num_classes)
     )
     summary_file = pj(opt.output_dir, opt.summary_file)
     if not opt.skip_eval: