Update README.md

README.md

@@ -76,7 +76,7 @@ The model's performance and biases are influenced by its base model (`google/gem
 ## How to Get Started with the Model
 
 This model can be loaded using the Hugging Face `transformers` library.
-'''python
+```python
 # Example (conceptual, actual usage depends on task setup)
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
@@ -89,7 +89,7 @@ model = AutoModelForCausalLM.from_pretrained(model_name)
 # inputs = tokenizer(prompt, return_tensors="pt")
 # outputs = model.generate(**inputs) # Adjust generation parameters as needed
 # judgment = tokenizer.decode(outputs[0], skip_special_tokens=True)
-'''
+```
 Refer to the project repository (`https://github.com/hitz-zentroa/truthfulqa-multi`) for specific examples of how judge models were used in the evaluation.
 
 ## Training Details
@@ -166,7 +166,7 @@ The model is based on the `Gemma2` architecture (`Gemma2ForCausalLM`). It is a C
 ## Citation
 
 **Paper:**
-'''bibtex
+```bibtex
 @inproceedings{calvo-etal-2025-truthknowsnolanguage,
     title = "Truth Knows No Language: Evaluating Truthfulness Beyond English",
     author = "Calvo Figueras, Blanca and Sagarzazu, Eneko and Etxaniz, Julen and Barnes, Jeremy and Gamallo, Pablo and De Dios Flores, Iria and Agerri, Rodrigo",
@@ -176,7 +176,7 @@ The model is based on the `Gemma2` architecture (`Gemma2ForCausalLM`). It is a C
     primaryClass={cs.CL},
     url={https://arxiv.org/abs/2502.09387}
 }
-'''
+```
 
 ## More Information