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app.py

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+# Turkish Zero-Shot Text Classification with XLM-RoBERTa
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import sentencepiece
+import torch
+import plotly.graph_objects as go
+import streamlit as st
+
+text_1 = """Bilim insanları Botsvana’da Covid-19’un şu ana kadar en çok mutasyona uğramış varyantını tespit etti. \
+Resmi olarak B.1.1.529 koduyla bilinen bu varyantı ise “Nu varyantı” adı verildi. Uzmanlar bu varyant içerisinde \
+tam 32 farklı mutasyon tespit edildiğini açıklarken, bu virüsün corona virüsü aşılarına karşı daha dirençli olabileceğini duyurdu."""
+
+text_2 = """Şampiyonlar Ligi’nde 5. hafta oynanan karşılaşmaların ardından sona erdi. Real Madrid, Inter ve Sporting \
+oynadıkları mücadeleler sonrasında Son 16 turuna yükselmeyi başardı. Gecenin dev mücadelesinde ise Manchester City, \
+PSG’yi yenerek liderliği garantiledi."""
+
+@st.cache(allow_output_mutation=True)
+def list2text(label_list):
+    labels = ""
+    for label in label_list:
+        labels = labels + label + ","
+    labels = labels[:-1]
+    return labels
+
+label_list_1 = ["dünya", "ekonomi", "kültür", "sağlık", "siyaset", "spor", "teknoloji"]
+label_list_2 = ["positive", "negative", "neutral"]
+
+st.title("Turkish Zero-Shot Text Classification \
+    with Multilingual XLM-RoBERTa and mDeBERTa Models")
+
+model_list = ['vicgalle/xlm-roberta-large-xnli-anli',
+             'joeddav/xlm-roberta-large-xnli',
+             'MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7']
+
+st.sidebar.header("Select Model")
+model_checkpoint = st.sidebar.radio("", model_list)
+
+st.sidebar.write("For details of models:")
+st.sidebar.write("https://huggingface.co/vicgalle")
+st.sidebar.write("https://huggingface.co/joeddav")
+st.sidebar.write("https://huggingface.co/MoritzLaurer")
+
+st.sidebar.write("For XNLI Dataset:")
+st.sidebar.write("https://huggingface.co/datasets/xnli")
+
+st.subheader("Select Text and Label List")
+st.text_area("Text #1", text_1, height=128)
+st.text_area("Text #2", text_2, height=128)
+st.write(f"Label List #1: {list2text(label_list_1)}")
+st.write(f"Label List #2: {list2text(label_list_2)}")
+
+text = st.radio("Select Text", ("Text #1", "Text #2", "New Text"))
+labels = st.radio("Select Label List", ("Label List #1", "Label List #2", "New Label List"))
+
+if text == "Text #1": selected_text = text_1
+elif text == "Text #2": selected_text = text_2
+elif text == "New Text":
+    selected_text = st.text_area("New Text", value="", height=128)
+
+if labels == "Label List #1": selected_labels = label_list_1
+elif labels == "Label List #2": selected_labels = label_list_2
+elif labels == "New Label List":
+    selected_labels = st.text_area("New Label List (Pls Input as comma-separated)", value="", height=16).split(",")
+
+@st.cache(allow_output_mutation=True)
+def setModel(model_checkpoint):
+    model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
+    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+    return pipeline("zero-shot-classification", model=model, tokenizer=tokenizer)
+        
+Run_Button = st.button("Run", key=None)
+if Run_Button == True:
+    
+    zstc_pipeline = setModel(model_checkpoint)
+    output = zstc_pipeline(sequences=selected_text, candidate_labels=selected_labels)
+    output_labels = output["labels"]
+    output_scores = output["scores"]
+
+    st.header("Result")
+    import plotly.graph_objects as go
+    fig = go.Figure([go.Bar(x=output_labels, y=output_scores)])
+    st.plotly_chart(fig, use_container_width=False, sharing="streamlit")
+
+  

requirements.txt

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+streamlit
+torch
+transformers
+sentencepiece
+plotly