Update lm_finetuning.py

lm_finetuning.py

@@ -1,3 +1,24 @@
+'''
+wandb offline
+export WANDB_DISABLED='true'
+export RAY_RESULTS='ray_results'
+
+python lm_finetuning.py -m "roberta-large" -o "ckpt/2021/roberta-large" --push-to-hub --hf-organization "cardiffnlp" -a "roberta-large-tweet-topic-multi-all" --split-train "train_all" --split-valid "validation_2021" --split-test "test_2021"
+python lm_finetuning.py -m "roberta-large" -o "ckpt/2020/roberta-large" --push-to-hub --hf-organization "cardiffnlp" -a "roberta-large-tweet-topic-multi-2020" --split-train "train_2020" --split-valid "validation_2020" --split-test "test_2021"
+
+python lm_finetuning.py -m "roberta-base" -o "ckpt/2021/roberta_base" --push-to-hub --hf-organization "cardiffnlp" -a "roberta-base-tweet-topic-multi-all" --split-train "train_all" --split-valid "validation_2021" --split-test "test_2021"
+python lm_finetuning.py -m "roberta-base" -o "ckpt/2020/roberta_base" --push-to-hub --hf-organization "cardiffnlp" -a "roberta-base-tweet-topic-multi-2020" --split-train "train_2020" --split-valid "validation_2020" --split-test "test_2021"
+
+python lm_finetuning.py -m "cardiffnlp/twitter-roberta-base-2019-90m" -o "ckpt/2021/twitter-roberta-base-2019-90m" --push-to-hub --hf-organization "cardiffnlp" -a "twitter-roberta-base-2019-90m-tweet-topic-multi-all" --split-train "train_all" --split-valid "validation_2021" --split-test "test_2021"
+python lm_finetuning.py -m "cardiffnlp/twitter-roberta-base-2019-90m" -o "ckpt/2020/twitter-roberta-base-2019-90m" --push-to-hub --hf-organization "cardiffnlp" -a "twitter-roberta-base-2019-90m-tweet-topic-multi-2020" --split-train "train_2020" --split-valid "validation_2020" --split-test "test_2021"
+
+python lm_finetuning.py -m "cardiffnlp/twitter-roberta-base-dec2020" -o "ckpt/2021/twitter-roberta-base-dec2020" --push-to-hub --hf-organization "cardiffnlp" -a "twitter-roberta-base-dec2020-tweet-topic-multi-all" --split-train "train_all" --split-valid "validation_2021" --split-test "test_2021"
+python lm_finetuning.py -m "cardiffnlp/twitter-roberta-base-dec2020" -o "ckpt/2020/twitter-roberta-base-dec2020" --push-to-hub --hf-organization "cardiffnlp" -a "twitter-roberta-base-dec2020-tweet-topic-multi-2020" --split-train "train_2020" --split-valid "validation_2020" --split-test "test_2021"
+
+python lm_finetuning.py -m "cardiffnlp/twitter-roberta-base-dec2021" -o "ckpt/2021/twitter-roberta-base-dec2021" --push-to-hub --hf-organization "cardiffnlp" -a "twitter-roberta-base-dec2021-tweet-topic-multi-all" --split-train "train_all" --split-valid "validation_2021" --split-test "test_2021"
+python lm_finetuning.py -m "cardiffnlp/twitter-roberta-base-dec2021" -o "ckpt/2020/twitter-roberta-base-dec2021" --push-to-hub --hf-organization "cardiffnlp" -a "twitter-roberta-base-dec2021-tweet-topic-multi-2020" --split-train "train_2020" --split-valid "validation_2020" --split-test "test_2021"
+'''
+
 import argparse
 import json
 import logging
@@ -52,7 +73,9 @@ 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_single', type=str)
-    parser.add_argument('--dataset-name', help='huggingface dataset name', default='citation_intent', 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)
@@ -68,7 +91,7 @@ def main():
     opt = parser.parse_args()
     assert opt.summary_file.endswith('.json'), f'`--summary-file` should be a json file {opt.summary_file}'
     # setup data
-    dataset = load_dataset(opt.dataset, opt.dataset_name)
+    dataset = load_dataset(opt.dataset)
     network = internet_connection()
     # setup model
     tokenizer = AutoTokenizer.from_pretrained(opt.model, local_files_only=not network)
@@ -90,11 +113,11 @@ 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)
+            model_init=lambda x: AutoModelForSequenceClassification.from_pretrained(opt.model, num_labels=dataset['train'].features['label'].num_classes, local_files_only=not network, return_dict=True)
+                )
         )
         # parameter search
         if PARALLEL:
@@ -128,9 +151,7 @@ def main():
 
     # evaluation
     model = AutoModelForSequenceClassification.from_pretrained(
-        best_model_path,
-        num_labels=dataset['train'].features['label'].num_classes,
-        local_files_only=not network)
+        opt.model, num_labels=dataset['train'].features['label'].num_classes, local_files_only=not network)
     trainer = Trainer(
         model=model,
         args=TrainingArguments(
@@ -138,11 +159,11 @@ 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, num_labels=dataset['train'].features['label'].num_classes, local_files_only=not network, return_dict=True)
     )
     summary_file = pj(opt.output_dir, opt.summary_file)
     if not opt.skip_eval:
@@ -168,3 +189,4 @@ def main():
 
 if __name__ == '__main__':
     main()
+