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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:
with st.spinner('Model is running...'):
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")
fig = go.Figure([go.Bar(x=output_labels, y=output_scores)])
st.plotly_chart(fig, use_container_width=False, sharing="streamlit")
st.success('Done!')
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