message

app.py

@@ -3,6 +3,7 @@ from datasets import load_dataset
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 import torch.nn.functional as F
 import torch
+import numpy as np
 
 dataset_dict = load_dataset('HUPD/hupd',
     name='sample',
@@ -37,11 +38,22 @@ for i in range(0, 20):
 
 def get_score(abstract):
 
-    model_name = "arianasutanto/finetuned-distilbert"
-    classifier = pipeline("text-classification", model=model_name)
-    result = classifier(abstract)
+    model = AutoModelForSequenceClassification.from_pretrained("arianasutanto/finetuned-distilbert")
+    tokenizer = AutoTokenizer.from_pretrained("arianasutanto/finetuned-distilbert", pad_to_max_length=True)
+
+    inputs = tokenizer(abstract, padding='max_length', truncation=True, return_tensors='pt')
+
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        print(logits)
+
+    predictions = F.softmax(logits, 0)
+    idk = np.argmax(predictions, 0)
+    print(predictions)
+    print(idk)
+
    
-    return result
+    return logits