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import streamlit as st |
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from transformers import pipeline |
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models = { |
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"fBert Convabuse": "alexabrahall/fbert_convabuse_ss", |
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"fBert HTDM": "alexabrahall/fbert_htdm_ss", |
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"hateBert Convabuse": "alexabrahall/hatebert_convabuse_ss", |
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"hateBert HTDM": "alexabrahall/hatebert_htdm_ss", |
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"berTweet Convabuse": "alexabrahall/bertweet_convabuse_ss", |
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"berTweet HTDM": "alexabrahall/bertweet_htdm_ss", |
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"roberta Convabuse": "alexabrahall/roberta_convabuse_ss", |
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"roberta HTDM": "alexabrahall/roberta_htdm_ss", |
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} |
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model_descriptions = { |
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"fBert Convabuse": "This is the model fBert, trained on the conversational abuse public dataset. It is a binary classification model that predicts whether a given text is abusive or not. The model is based on the fBert architecture and was trained using the Sentence Transformers library.", |
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"fBert HTDM": "This is the model fBert, trained on the hate speech public dataset. It is a binary classification model that predicts whether a given text is hate speech or not. The model is based on the fBert architecture and was trained using the Sentence Transformers library.", |
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"hateBert Convabuse": "This is the model hateBert, trained on the conversational abuse public dataset. It is a binary classification model that predicts whether a given text is abusive or not. The model is based on the hateBert architecture and was trained using the Sentence Transformers library.", |
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"hateBert HTDM": "This is the model hateBert, trained on the hate speech public dataset. It is a binary classification model that predicts whether a given text is hate speech or not. The model is based on the hateBert architecture and was trained using the Sentence Transformers library.", |
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"berTweet Convabuse": "This is the model berTweet, trained on the conversational abuse public dataset. It is a binary classification model that predicts whether a given text is abusive or not. The model is based on the berTweet architecture and was trained using the Sentence Transformers library.", |
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"berTweet HTDM": "This is the model berTweet, trained on the hate speech public dataset. It is a binary classification model that predicts whether a given text is hate speech or not. The model is based on the berTweet architecture and was trained using the Sentence Transformers library.", |
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"roberta Convabuse": "This is the model roberta, trained on the conversational abuse public dataset. It is a binary classification model that predicts whether a given text is abusive or not. The model is based on the roberta architecture and was trained using the Sentence Transformers library.", |
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"roberta HTDM": "This is the model roberta, trained on the hate speech public dataset. It is a binary classification model that predicts whether a given text is hate speech or not. The model is based on the roberta architecture and was trained using the Sentence Transformers library.", |
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} |
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def classify_text(text, model): |
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classifier = pipeline("text-classification", model=model) |
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results = classifier(text) |
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return results |
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st.title("Homotransphobia Detection App") |
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st.markdown("[By Alex Abrahall](https://huggingface.co/alexabrahall)", unsafe_allow_html=True) |
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st.write("Enter the text you want to classify:") |
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user_input = st.text_input("") |
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selected_model = st.selectbox("Select Model", options=list(models.keys())) |
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selected_model_description = model_descriptions[selected_model] |
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st.write("Model Description:") |
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st.write(selected_model_description) |
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label_map ={ |
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"LABEL_0": "Not Homotransphobic", |
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"LABEL_1": "Homotransphobic" |
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} |
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if st.button('Classify'): |
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prediction_raw_text = st.empty() |
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prediction_text = st.empty() |
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loading_text = st.text("Predicting... (if the model has not been used before, this may take a while)") |
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prediction = classify_text(user_input, models[selected_model]) |
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loading_text.empty() |
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prediction_raw_text.write("Prediction:") |
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print(prediction) |
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prediction_text.markdown(f"The text is <span style='color: {'red' if prediction[0]['label'] == 'LABEL_1' else 'green'}'>{label_map[prediction[0]['label']]}</span> with a confidence of {prediction[0]['score']*100}%", unsafe_allow_html=True) |
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