Update README.md

README.md

@@ -30,9 +30,64 @@ This model provides a new refresher in the field of emotion-aware dialogue syste
 - **Paper:** xxxxxxxxxxxxxxxxxxx
 
 ## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+This model is designed for multitask text-to-text generation in Indonesian, specifically trained for:
+1. Dialogue Response Generation: Given a user utterance prefixed with dialog:, the model generates a relevant conversational response.
+2. Emotion Classification: Given a text prefixed with emosi:, the model predicts the underlying emotion expressed in the text.
+3. Context Understanding/Summarization (if applicable based on your training data): Given a text prefixed with konteks:, the model can perform tasks related to understanding or summarizing the provided context.
+It's intended to be used directly via the transformers library in Python for applications requiring these specific capabilities in Indonesian.
 ### Direct Use
-[More Information Needed]
+```
+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}")
+
+def generate_response(input_text, task_prefix):
+    """Generates a response from the model for a given task."""
+    full_input = f"{task_prefix}: {input_text}"
+    print(f"\nInput ({task_prefix}): {full_input}")
+    
+    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
+        num_beams=5,
+        early_stopping=True
+    )
+    
+    decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    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.")
+```
 
 
 ## How to Get Started with the Model