Add description

app.py

@@ -83,10 +83,11 @@ st.title("RAFT Leaderboard")
 st.markdown("""
 RAFT: Real-world Annotated Few-shot Tasks
 
-Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? RAFT is a few-shot classification benchmark that tests language models
-across multiple domains (lit review, tweets, customer interaction, etc.)
-on economically valuable classification tasks (someone inherently cares about the task)
-in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set)
+Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? RAFT is a few-shot classification benchmark that tests language models:
+
+- across multiple domains (lit review, tweets, customer interaction, etc.)
+- on economically valuable classification tasks (someone inherently cares about the task)
+- in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set)
 
 To submit to RAFT, follow the instruction posted on [this page](https://github.com/oughtinc/raft_submission).
 """)