Update README.md

README.md

@@ -35,6 +35,8 @@ T5ForConditionalGeneration(
   (lm_head): Linear(in_features=768, out_features=32100, bias=False)
 )
 
+```
+
 ```bash
 pip install -U transformers torch datasets
 #Then, load the model and run inference:
@@ -43,7 +45,7 @@ from transformers import T5ForConditionalGeneration, RobertaTokenizer
 
 # Download from the 🤗 Hub
 ```python
-model_name = "your-username/codet5-conala-comments"  # Update with your HF model ID
+model_name = "AventIQ-AI/t5_code_summarizer"  # Update with your HF model ID
 tokenizer = RobertaTokenizer.from_pretrained(model_name)
 model = T5ForConditionalGeneration.from_pretrained(model_name)
 
@@ -91,11 +93,11 @@ snippet: int(''.join(map(str, x))), rewritten_intent: "Convert a list of integer
 snippet: datetime.strptime('2010-11-13 10:33:54.227806', '%Y-%m-%d %H:%M:%S.%f'), rewritten_intent: "Convert a DateTime string back to a DateTime object of format '%Y-%m-%d %H:%M:%S.%f'"
 ```
 # Training Hyperparameters
-Non-Default Hyperparameters:
-**per_device_train_batch_size:** 4
-**per_device_eval_batch_size:** 4
-**gradient_accumulation_steps:** 2 (effective batch size = 8)
-**num_train_epochs:** 10
-**learning_rate:** 1e-4
-**fp16:** True
+### Non-Default Hyperparameters:
+- **per_device_train_batch_size:** 4
+- **per_device_eval_batch_size:** 4
+- **gradient_accumulation_steps:** 2 (effective batch size = 8)
+- **num_train_epochs:** 10
+- **learning_rate:** 1e-4
+- **fp16:** True