Update README.md

README.md

@@ -19,7 +19,7 @@ The `srd2plantUml_Salesforce_codet5-base` model was fine-tuned using a dataset o
 - **Fine-tuned from model:** `Salesforce/codet5-base`
 
 ### Model Sources
-- **Paper:** If there's no direct paper, consider linking to the CodeT5 paper: [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://arxiv.org/abs/2109.00859)
+- **Paper:** the CodeT5 paper: [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://arxiv.org/abs/2109.00859)
 
 ## Uses
 
@@ -40,21 +40,4 @@ The model may have limitations in handling complex or highly technical SRDs outs
 ### Recommendations
 Users should be cautious of potential limitations in complex diagrams and may need to manually adjust or review the generated PlantUML code for accuracy, especially in domains not covered by the training data.
 
-## How to Get Started with the Model
 
-To use the model:
-
-```python
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-
-# Load the model and tokenizer
-model = AutoModel.from_pretrained("amgharhind/srd2plantUml_codet5base_V2")
-tokenizer = AutoTokenizer.from_pretrained("amgharhind/srd2plantUml_codet5base_V2")
-
-# Example usage
-input_text = "Sample SRD input for a use case diagram"
-inputs = tokenizer(input_text, return_tensors="pt")
-outputs = model.generate(**inputs)
-uml_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-print("Generated PlantUML code:", uml_code)