Update README.md

README.md

@@ -61,32 +61,26 @@ Anda dapat menggunakan model ini dengan pustaka `transformers` dari Hugging Face
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-# Ganti 'nama-anda/nama-model-anda' dengan path ke model yang disimpan atau nama di Hugging Face Hub
-model_name_or_path = "New/models/finetuned_perturb_double_weighted_run_roberta_large/epoch-6" # Contoh epoch terakhir
-# Atau jika diunggah ke Hub: "username/model_name"
+model_name = "fabhiansan/indoBERT-Base-FactChecking-Summarization"
 
 tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
 model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path)
 
-# Pindahkan model ke GPU jika tersedia
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
 
 premise = "Timnas Indonesia berhasil memenangkan pertandingan sepak bola."
 hypothesis = "Indonesia kalah dalam laga tersebut."
 
-# Tokenisasi input
 inputs = tokenizer(premise, hypothesis, return_tensors="pt", truncation=True, padding=True, max_length=512)
 inputs = {k: v.to(device) for k, v in inputs.items()}
 
-# Lakukan prediksi
-model.eval() # Set model ke mode evaluasi
+model.eval()
 with torch.no_grad():
     outputs = model(**inputs)
     logits = outputs.logits
     predictions = torch.argmax(logits, dim=-1)
 
-# Interpretasi hasil (asumsi label 0 = non-entailment, label 1 = entailment)
 if predictions.item() == 1:
     print("Hipotesis dapat disimpulkan dari premis (Entailment).")
 else: