feat: Put intro text as Markdown

app.py

@@ -49,6 +49,8 @@ _BANNER = f'<div style="display: flex; justify-content: space-around;"><img src=
 
 _INTRODUCTION_TEXT = """The 🤗 Nucleotide Transformer Leaderboard aims to track, rank and evaluate DNA foundational models on a set of curated downstream tasks introduced in the huggingface dataset [nucleotide_transformer_downstream_tasks](https://huggingface.co/datasets/InstaDeepAI/nucleotide_transformer_downstream_tasks) , with a standardized evaluation protocole presented in the "Methods" tab."""  # noqa
 
+_METHODS_TEXT = """We have compared the fine-tuned performance of Nucleotide Transformer models on the 18 downstream tasks with four different pre-trained models: [DNABERT-1](https://academic.oup.com/bioinformatics/article/37/15/2112/6128680), [DNABERT-2](https://arxiv.org/abs/2306.15006), [HyenaDNA](https://arxiv.org/abs/2306.15794) (1kb and 32kb context length) and the [Enformer](https://www.nature.com/articles/s41592-021-01252-x) (which was trained as a supervised model on several genomics tasks). We ported the architecture and trained weights of each model to our code framework and performed parameter-efficient fine-tuning for every model as described above, using the same cross-validation scheme for a fair comparison. All results can be visulaized in an interactive leader-board 2. Only for HyenaDNA we performed full fine-tuning due to the incompatibility of our parameter-efficient fine-tuning approach with the model architecture."""  # noqa
+
 
 def retrieve_array_from_text(text):
     return np.fromstring(text.replace("[", "").replace("]", ""), dtype=float, sep=",")
@@ -125,7 +127,8 @@ def get_bar_plot(
 with gr.Blocks() as demo:
     with gr.Row():
         gr.Image(banner_url, height=160, scale=1)
-        gr.Textbox(_INTRODUCTION_TEXT, scale=5)
+        gr.Markdown(_INTRODUCTION_TEXT, elem_classes="markdown-text", scale=5)
+        # gr.Textbox(_INTRODUCTION_TEXT, scale=5)
 
     with gr.Row():
         metric_choice = gr.Dropdown(
@@ -173,7 +176,7 @@ with gr.Blocks() as demo:
             )
 
         with gr.TabItem("ℹ️ Methods", elem_id="od-benchmark-tab-table", id=1):
-            gr.Markdown("""We have compared the fine-tuned performance of Nucleotide Transformer models on the 18 downstream tasks with four different pre-trained models: [DNABERT-1](https://academic.oup.com/bioinformatics/article/37/15/2112/6128680), [DNABERT-2](https://arxiv.org/abs/2306.15006), [HyenaDNA](https://arxiv.org/abs/2306.15794) (1kb and 32kb context length) and the [Enformer](https://www.nature.com/articles/s41592-021-01252-x) (which was trained as a supervised model on several genomics tasks). We ported the architecture and trained weights of each model to our code framework and performed parameter-efficient fine-tuning for every model as described above, using the same cross-validation scheme for a fair comparison. All results can be visulaized in an interactive leader-board 2. Only for HyenaDNA we performed full fine-tuning due to the incompatibility of our parameter-efficient fine-tuning approach with the model architecture.""", elem_classes="markdown-text")
+            gr.Markdown(_METHODS_TEXT, elem_classes="markdown-text")
 
     gr.Markdown(f"Last updated on **{_LAST_UPDATED}**", elem_classes="markdown-text")