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Kingston Yip
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9bceaae
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Parent(s):
c7e1afa
added desc
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app.py
CHANGED
@@ -66,12 +66,12 @@ def perform_default_analysis(model_name):
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# main -->
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st.title("Toxic Tweets Analyzer")
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image = "kanye_loves_tweet.jpg"
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st.image(image, use_column_width=True)
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labels = ['comment', 'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
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with st.form("my_form"):
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#select model
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model_name = st.selectbox("Enter a text and select a pre-trained model to get the sentiment analysis", ["kingsotn/finetuned_cyberbullying", "distilbert-base-uncased-finetuned-sst-2-english", "finiteautomata/bertweet-base-sentiment-analysis", "distilbert-base-uncased"])
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# main -->
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st.title("Toxic Tweets Analyzer")
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st.write("💡 Toxic Tweets Analyzer is an app that helps you determine the likelihood of a tweet or any text being toxic, abusive or cyberbullying. The app offers different pre-trained models to choose from, each with their own strengths and limitations. kingsotn/finetuned_cyberbullying is a finetuned distilbert. It uses artificial intelligence to analyze the text you input and then calculates a probability score for each label: toxic, severe_toxic, obscene, threat, insult, and identity_hate. The scores range from 0 to 1, with 1 being the highest probability of that label being present in the tweet. The output is a table that shows the probability scores for each label, giving you an idea of the toxicity of the tweet. This can be helpful in identifying and preventing cyberbullying and other forms of online abuse.")
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image = "kanye_loves_tweet.jpg"
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st.image(image, use_column_width=True)
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labels = ['comment', 'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
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with st.form("my_form"):
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#select model
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model_name = st.selectbox("Enter a text and select a pre-trained model to get the sentiment analysis", ["kingsotn/finetuned_cyberbullying", "distilbert-base-uncased-finetuned-sst-2-english", "finiteautomata/bertweet-base-sentiment-analysis", "distilbert-base-uncased"])
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