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| import streamlit as st | |
| title = "Hate Speech Detection in Automatic Content Moderation" | |
| description = "The history and development of hate speech detection as a modeling task" | |
| date = "2022-01-26" | |
| thumbnail = "images/huggingface_logo.png" | |
| def run_article(): | |
| st.markdown(""" | |
| # What is Automatic Content Moderation? | |
| This is where the history of automatic content moderation (ACM) will go. | |
| # The Landscape of ACM | |
| This is where the current platforms and approaches with go. | |
| # Current Challenges | |
| This is where the discussion of current challenges, examples from media, and value tensions will go. | |
| So what does all this mean for conceptualizing this real world problem as a machine learning task? First we'll look at the data, then at the models. | |
| """) | |