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import os |
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os.system("pip install torch") |
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os.system("pip install transformers") |
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os.system("pip install sentencepiece") |
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os.system("pip install plotly") |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline |
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import sentencepiece |
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import torch |
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import plotly.graph_objects as go |
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import streamlit as st |
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text_1 = """Avec la Ligue 1 qui reprend ses droits à partir de vendredi 5 août, et un premier match pour ce qui les concerne samedi soir, à Clermont-Ferrand, l’heure est désormais arrivée pour les Parisiens d’apporter les preuves que ce changement d’ère est bien une réalité.""" |
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text_2 = """Créées en 1991 sur un modèle inspiré de la Fête de la musique, les Nuits des étoiles ont pour thème en 2022 l’exploration spatiale, en partenariat avec l’Agence spatiale européenne.""" |
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@st.cache(allow_output_mutation=True) |
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def list2text(label_list): |
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labels = "" |
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for label in label_list: |
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labels = labels + label + "," |
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labels = labels[:-1] |
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return labels |
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label_list_1 = ["monde", "économie", "sciences", "culture", "santé", "politique", "sport", "technologie"] |
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label_list_2 = ["positif", "négatif", "neutre"] |
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st.title("French Zero-Shot Text Classification \ |
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with CamemBERT and XLM-R") |
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st.markdown( |
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""" |
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This application makes use of [CamemBERT](https://camembert-model.fr/) and [XLM-R](https://arxiv.org/abs/1911.02116) models that were fine-tuned on the XNLI corpus. While CamemBERT was fine-tuned only on the French part of the corpus by [Baptiste Doyen](https://huggingface.co/BaptisteDoyen), XLM-R was done so on all parts of it by [Joe Davison](https://huggingface.co/joeddav), including French. Therefore, in this app, both of these two models are intended to be used and made comparison of each other for zero-shot classification in French. |
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""" |
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) |
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model_list = ['BaptisteDoyen/camembert-base-xnli', |
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'joeddav/xlm-roberta-large-xnli'] |
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st.sidebar.header("Select Model") |
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model_checkpoint = st.sidebar.radio("", model_list) |
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st.sidebar.write("For the full descriptions of the models:") |
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st.sidebar.write("[camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli)") |
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st.sidebar.write("[xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli)") |
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st.sidebar.write("For the XNLI Dataset:") |
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st.sidebar.write("[XNLI](https://huggingface.co/datasets/xnli)") |
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st.subheader("Select Text and Label List") |
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st.text_area("Text #1", text_1, height=128) |
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st.text_area("Text #2", text_2, height=128) |
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st.write(f"Label List #1: {list2text(label_list_1)}") |
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st.write(f"Label List #2: {list2text(label_list_2)}") |
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text = st.radio("Select Text", ("Text #1", "Text #2", "New Text")) |
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labels = st.radio("Select Label List", ("Label List #1", "Label List #2", "New Label List")) |
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if text == "Text #1": selected_text = text_1 |
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elif text == "Text #2": selected_text = text_2 |
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elif text == "New Text": |
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selected_text = st.text_area("New Text", value="", height=128) |
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if labels == "Label List #1": selected_labels = label_list_1 |
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elif labels == "Label List #2": selected_labels = label_list_2 |
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elif labels == "New Label List": |
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selected_labels = st.text_area("New Label List (Pls Input as comma-separated)", value="", height=16).split(",") |
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@st.cache(allow_output_mutation=True) |
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def setModel(model_checkpoint): |
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model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint) |
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tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) |
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return pipeline("zero-shot-classification", model=model, tokenizer=tokenizer) |
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Run_Button = st.button("Run", key=None) |
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if Run_Button == True: |
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zstc_pipeline = setModel(model_checkpoint) |
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output = zstc_pipeline(sequences=selected_text, candidate_labels=selected_labels) |
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output_labels = output["labels"] |
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output_scores = output["scores"] |
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st.header("Result") |
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import plotly.graph_objects as go |
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fig = go.Figure([go.Bar(x=output_labels, y=output_scores)]) |
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st.plotly_chart(fig, use_container_width=False, sharing="streamlit") |