Update lm_finetuning.py

lm_finetuning.py

@@ -40,6 +40,28 @@ logging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s', level=logg
 
 PARALLEL = bool(int(os.getenv("PARALLEL", 1)))
 RAY_RESULTS = os.getenv("RAY_RESULTS", "ray_results")
+LABEL2ID = {
+    "arts_&_culture": 0,
+    "business_&_entrepreneurs": 1,
+    "celebrity_&_pop_culture": 2,
+    "diaries_&_daily_life": 3,
+    "family": 4,
+    "fashion_&_style": 5,
+    "film_tv_&_video": 6,
+    "fitness_&_health": 7,
+    "food_&_dining": 8,
+    "gaming": 9,
+    "learning_&_educational": 10,
+    "music": 11,
+    "news_&_social_concern": 12,
+    "other_hobbies": 13,
+    "relationships": 14,
+    "science_&_technology": 15,
+    "sports": 16,
+    "travel_&_adventure": 17,
+    "youth_&_student_life": 18
+}
+ID2LABEL = {v: k for k, v in LABEL2ID.items()}
 
 
 def internet_connection(host='http://google.com'):
@@ -105,6 +127,8 @@ def main():
     tokenizer = AutoTokenizer.from_pretrained(opt.model, local_files_only=not network)
     model = AutoModelForSequenceClassification.from_pretrained(
         opt.model,
+        id2label=ID2LABEL,
+        label2id=LABEL2ID,
         num_labels=len(dataset[opt.split_train]['label'][0]),
         local_files_only=not network,
         problem_type="multi_label_classification"
@@ -129,7 +153,8 @@ 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(dataset[opt.split_train]['label'][0]))
+                opt.model, return_dict=True, num_labels=len(dataset[opt.split_train]['label'][0])),
+                id2label=ID2LABEL, label2id=LABEL2ID,
         )
         # parameter search
         if PARALLEL:
@@ -166,7 +191,10 @@ def main():
         best_model_path,
         num_labels=len(dataset[opt.split_train]['label'][0]),
         local_files_only=not network,
-        problem_type="multi_label_classification")
+        problem_type="multi_label_classification",
+        id2label=ID2LABEL,
+        label2id=LABEL2ID
+    )
     trainer = Trainer(
         model=model,
         args=TrainingArguments(
@@ -182,7 +210,10 @@ def main():
             return_dict=True,
             num_labels=len(dataset[opt.split_train]['label'][0]),
             local_files_only=not network,
-            problem_type="multi_label_classification")
+            problem_type="multi_label_classification",
+            id2label=ID2LABEL,
+            label2id=LABEL2ID
+            )
     )
     summary_file = pj(opt.output_dir, opt.summary_file)
     if not opt.skip_eval: