Update README.md

README.md

@@ -43,8 +43,8 @@ To use this model for text summarization, you can follow the code example below:
 from transformers import T5Tokenizer, T5ForConditionalGeneration
 
 # Load the fine-tuned model and tokenizer
-model = T5ForConditionalGeneration.from_pretrained("username/fine_tuned_t5")
-tokenizer = T5Tokenizer.from_pretrained("username/fine_tuned_t5")
+model = T5ForConditionalGeneration.from_pretrained("kawinduwijewardhane/BriefT5")
+tokenizer = T5Tokenizer.from_pretrained("kawinduwijewardhane/BriefT5")
 
 # Input text for summarization
 input_text = "Your long input text here."
@@ -56,3 +56,15 @@ summary_ids = model.generate(inputs["input_ids"], max_length=150, num_beams=4, e
 # Decode the summary
 summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
 print(summary)
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+### Explanation of the YAML metadata:
+- **`language`**: Specifies the language the model supports, in this case, English (`en`).
+- **`license`**: Describes the licensing information for your model, here it is set to MIT (you can change it depending on your license).
+- **`tags`**: These tags help categorize your model on Hugging Face and make it easier for others to discover. I've added tags like `summarization`, `t5`, `text-to-text`, and `fine-tuned`.
+
+This will help you resolve the warning and provide the necessary metadata for your model card!
+