Update README.md

README.md

@@ -40,15 +40,12 @@ It's intended to be used directly via the transformers library in Python for app
 import torch
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-# Define the model repository ID
 repo_id = "adhitia17/idmt"
 
-# Load the tokenizer and model
 print(f"Loading tokenizer and model from {repo_id}...")
 tokenizer = AutoTokenizer.from_pretrained(repo_id)
 model = AutoModelForSeq2SeqLM.from_pretrained(repo_id)
 
-# Move model to GPU if available
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
 print(f"Model loaded to device: {device}")
@@ -60,10 +57,9 @@ def generate_response(input_text, task_prefix):
     
     input_ids = tokenizer(full_input, return_tensors="pt").input_ids.to(device)
     
-    # Adjust generation parameters as needed
     outputs = model.generate(
         input_ids,
-        max_length=128, # Max length for the generated output
+        max_length=256, 
         num_beams=5,
         early_stopping=True
     )
@@ -72,19 +68,6 @@ def generate_response(input_text, task_prefix):
     print(f"Output: {decoded_output}")
     return decoded_output
 
-# --- Example Usage ---
-
-# 1. Dialogue Response Generation
-user_dialogue = "halo, apa kabar?"
-generate_response(user_dialogue, "dialog")
-
-# 2. Emotion Classification
-user_emotion_text = "saya sangat kecewa dengan hasilnya."
-generate_response(user_emotion_text, "emosi")
-
-# 3. Context Understanding (if applicable)
-# user_context = "artikel ini membahas dampak perubahan iklim terhadap pertanian."
-# generate_response(user_context, "konteks")
 
 print("\nInference examples complete.")
 ```