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sections/intro.md

@@ -18,7 +18,7 @@ We follow the two-staged training approach, our pre-training task being text-onl
 
 Our checkpoints achieve a **validation accuracy of 0.69 on our MLM** task, while our fine-tuned model is able to achieve a **validation accuracy of 0.49 on our multilingual VQAv2 validation set**. With better captions, hyperparameter-tuning, and further training, we expect to see higher performance.
 
-**Novel Contributions**:
+### Novel Contributions
 Our novel contributions include:
 - A [multilingual variant of the Conceptual-12M dataset](https://huggingface.co/datasets/flax-community/conceptual-12m-mbart-50-multilingual) containing 2.5M image-text pairs each in four languages - English, French, German and Spanish, translated using mBART-50 model.
 - [Multilingual variants of the VQAv2 train and validation sets](https://huggingface.co/datasets/flax-community/multilingual-vqa) containing four times the original data in English, French, German and Spanish, translated using Marian models.