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HuggingFace Space - ESS Variable Classification Demo
Interactive Gradio interface for the XLM-RoBERTa ESS classifier.
"""
import gradio as gr
from transformers import pipeline
# Load the model
MODEL_NAME = "benjaminBeuster/xlm-roberta-base-ess-classification"
classifier = pipeline("text-classification", model=MODEL_NAME)
# Category descriptions
CATEGORY_INFO = {
"DEMOGRAPHY (POPULATION, VITAL STATISTICS, AND CENSUSES)": "Demographics, population statistics, age, gender",
"ECONOMICS": "Economic issues, finance, income",
"EDUCATION": "Education, schooling, qualifications",
"HEALTH": "Healthcare, medical services, health satisfaction",
"POLITICS": "Political systems, trust in government, parliament",
"SOCIETY AND CULTURE": "Social issues, cultural topics, religion",
"LABOUR AND EMPLOYMENT": "Work, occupation, employment status",
"PSYCHOLOGY": "Mental health, psychological wellbeing",
"OTHER": "General or uncategorized topics"
}
def classify_text(text):
"""Classify survey question/variable."""
if not text.strip():
return "Please enter some text to classify."
result = classifier(text)[0]
label = result['label']
score = result['score']
# Format output
output = f"**Category:** {label}\n\n"
output += f"**Confidence:** {score:.2%}\n\n"
if label in CATEGORY_INFO:
output += f"**Description:** {CATEGORY_INFO[label]}"
return output
# Example questions
examples = [
["What is your age?"],
["How satisfied are you with the healthcare system?"],
["Trust in country's parliament"],
["What is your highest level of education?"],
["How often do you pray?"],
["What is your occupation?"],
["Do you feel safe walking alone at night?"],
]
# Create Gradio interface
demo = gr.Interface(
fn=classify_text,
inputs=gr.Textbox(
lines=3,
placeholder="Enter a survey question or variable description...",
label="Input Text"
),
outputs=gr.Markdown(label="Classification Result"),
title="ESS Variable Classification",
description="""
Classify European Social Survey (ESS) variables into 19 subject categories.
This model is fine-tuned from XLM-RoBERTa-Base and achieves 83.8% accuracy on the test set.
""",
examples=examples,
article="""
### About
This classifier helps organize survey variables by automatically categorizing them into subject areas.
Built on [XLM-RoBERTa-Base](https://huggingface.co/FacebookAI/xlm-roberta-base),
trained on European Social Survey metadata.
**Model:** [benjaminBeuster/xlm-roberta-base-ess-classification](https://huggingface.co/benjaminBeuster/xlm-roberta-base-ess-classification)
**Performance:** 83.8% accuracy | F1: 0.796 (weighted)
""",
theme=gr.themes.Soft(),
allow_flagging="never"
)
if __name__ == "__main__":
demo.launch()
|