Update README.md

README.md

@@ -27,26 +27,33 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 - **License:** [More Information Needed]
 - **Finetuned from model [google-t5-small]:** [More Information Needed]
 
-# Check if CUDA (GPU) is available
+
+- ** Load model directly
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("onlysainaa/cyrillic_to_script-t5-model")
+model = AutoModelForSeq2SeqLM.from_pretrained("onlysainaa/cyrillic_to_script-t5-model")
+
+- ** Check if CUDA (GPU) is available
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-# Move the model to the same device (GPU or CPU)
+- ** Move the model to the same device (GPU or CPU)
 model.to(device)
 
-# Prepare text input
-input_text = "сайн уу"  # Mongolian greeting
+- ** Prepare text input
+input_text = "сайн уу"  - ** Mongolian greeting
 
-# Tokenize the input text
+- ** Tokenize the input text
 inputs = tokenizer(input_text, return_tensors="pt")
 
-# Move the input tensors to the same device as the model
+- ** Move the input tensors to the same device as the model
 inputs = {k: v.to(device) for k, v in inputs.items() if k in ['input_ids', 'attention_mask']}
 
-# Generate translation
+- ** Generate translation
 outputs = model.generate(**inputs)
 
-# Decode the output to human-readable text
+- ** Decode the output to human-readable text
 translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-# Print the translated text
+- ** Print the translated text
 print(f"Translated Text: {translated_text}")