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import streamlit as st | |
title = "Key Takeaways" | |
description = "The key takeaways from this exploration" | |
date = "2022-01-26" | |
thumbnail = "images/huggingface_logo.png" | |
def run_article(): | |
st.markdown(""" | |
# Conclusion | |
Here are some of the main ideas we have conveyed in this exploration: | |
- Defining hate speech is hard and changes depending on your context and goals. | |
- Capturing a snapshot of what you've defined to be hate speech in a dataset is hard. | |
- Models learn lots of different things based on the data it sees, and that can include things you didn't intend for them to learn. | |
Action items? | |
""") | |