Update README.md

README.md

@@ -43,7 +43,7 @@ To load and use the fine-tuned model:
 ```python
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-model_name = "your-username/khmer-mt5-summarization"
+model_name = "songhieng/khmer-mt5-summarization"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 ```
@@ -70,7 +70,7 @@ For a simpler approach:
 ```python
 from transformers import pipeline
 
-summarizer = pipeline("summarization", model="your-username/khmer-mt5-summarization")
+summarizer = pipeline("summarization", model="songhieng/khmer-mt5-summarization")
 khmer_text = "កម្ពុជាមានប្រជាជនប្រមាណ ១៦ លាននាក់ ហើយវាគឺជាប្រទេសនៅតំបន់អាស៊ីអាគ្នេយ៍។"
 summary = summarizer(khmer_text, max_length=150, min_length=30, do_sample=False)
 print("🔹 Khmer Summary:", summary[0]['summary_text'])
@@ -118,13 +118,13 @@ trainer.evaluate()
 ## 💾 Saving & Uploading the Model
 After fine-tuning, the model was uploaded to Hugging Face Hub:
 ```python
-model.push_to_hub("your-username/khmer-mt5-summarization")
-tokenizer.push_to_hub("your-username/khmer-mt5-summarization")
+model.push_to_hub("songhieng/khmer-mt5-summarization")
+tokenizer.push_to_hub("songhieng/khmer-mt5-summarization")
 ```
 To download it later:
 ```python
-model = AutoModelForSeq2SeqLM.from_pretrained("your-username/khmer-mt5-summarization")
-tokenizer = AutoTokenizer.from_pretrained("your-username/khmer-mt5-summarization")
+model = AutoModelForSeq2SeqLM.from_pretrained("songhieng/khmer-mt5-summarization")
+tokenizer = AutoTokenizer.from_pretrained("songhieng/khmer-mt5-summarization")
 ```
 
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