Update README.md

README.md

@@ -34,15 +34,15 @@ 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:
-
+#Then, load the model and run inference:
 ```
 from transformers import T5ForConditionalGeneration, RobertaTokenizer
 
-# Download from the 🤗 Hub (replace with your model ID after uploading)
+# Download from the 🤗 Hub
+```python
 model_name = "your-username/codet5-conala-comments"  # Update with your HF model ID
 tokenizer = RobertaTokenizer.from_pretrained(model_name)
 model = T5ForConditionalGeneration.from_pretrained(model_name)
@@ -65,8 +65,8 @@ comment = tokenizer.decode(outputs[0], skip_special_tokens=True)
 print(f"Code: {code_snippet}")
 print(f"Comment: {comment}")
 # Expected output: Something close to "Concatenate elements of a list 'x' of multiple integers to a single integer"
-
 ```
+
 # Training Details
 Training Dataset
 **Name:** janrauhl/conala
@@ -99,13 +99,3 @@ Non-Default Hyperparameters:
 **learning_rate:** 1e-4
 **fp16:** True
 
-```
-@article{wang2021codet5,
-    title={CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation},
-    author={Wang, Yue and Wang, Weishi and Joty, Shafiq and Hoi, Steven C. H.},
-    journal={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
-    year={2021},
-    url={https://arxiv.org/abs/2109.00859}
-}
-
-```