init

experiments/model_finetuning_topic.py

@@ -76,9 +76,6 @@ def main(
     best_model_path = pj(output_dir, "best_model")
 
     tokenizer = AutoTokenizer.from_pretrained(model)
-    model = AutoModelForSequenceClassification.from_pretrained(
-        model, id2label=ID2LABEL, label2id=LABEL2ID, num_labels=len(LABEL2ID), problem_type="multi_label_classification"
-    )
     dataset = load_dataset(dataset, dataset_type)
     tokenized_datasets = dataset.map(
         lambda x: tokenizer(x["text"], padding="max_length", truncation=True, max_length=256), batched=True
@@ -150,15 +147,14 @@ def main(
         else:
             metric = {}
         for single_test in test_split:
-            model = AutoModelForSequenceClassification.from_pretrained(
-                best_model_path,
-                num_labels=len(LABEL2ID),
-                problem_type="multi_label_classification",
-                id2label=ID2LABEL,
-                label2id=LABEL2ID
-            )
             trainer = Trainer(
-                model=model,
+                model=AutoModelForSequenceClassification.from_pretrained(
+                    best_model_path,
+                    num_labels=len(LABEL2ID),
+                    problem_type="multi_label_classification",
+                    id2label=ID2LABEL,
+                    label2id=LABEL2ID
+                ),
                 args=TrainingArguments(
                     output_dir=output_dir,
                     evaluation_strategy="no",
@@ -176,7 +172,7 @@ def main(
     if not skip_upload:
         logging.info("uploading to huggingface")
         model_organization = "tweettemposhift"
-        model = AutoModelForSequenceClassification.from_pretrained(
+        model_instance = AutoModelForSequenceClassification.from_pretrained(
             best_model_path,
             num_labels=len(LABEL2ID),
             problem_type="multi_label_classification",
@@ -184,7 +180,7 @@ def main(
             label2id=LABEL2ID
         )
         tokenizer = AutoTokenizer.from_pretrained(best_model_path)
-        model.push_to_hub(f"{model_organization}/{model_alias}", use_auth_token=True)
+        model_instance.push_to_hub(f"{model_organization}/{model_alias}", use_auth_token=True)
         tokenizer.push_to_hub(f"{model_organization}/{model_alias}", use_auth_token=True)
         repo = Repository(model_alias, f"{model_organization}/{model_alias}")
         for i in glob(f"{best_model_path}/*"):