File size: 959 Bytes
04bbd33 aa53297 91dd84e 04bbd33 240b689 aa53297 0f375b6 04bbd33 19dc535 04bbd33 aa53297 04bbd33 19dc535 04bbd33 aa53297 04bbd33 aa53297 04bbd33 225ca46 88c381a aa53297 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 |
import os
import gradio as gr
import spaces
from transformers import pipeline
import huggingface_hub
# Login to Hugging Face Hub
token = os.getenv("HF_TOKEN")
huggingface_hub.login(token=token)
# Load the pre-trained model
classifier = pipeline("text-classification", model="ICILS/xlm-r-icils-ilo", device=0)
# Define the prediction function
@spaces.GPU
def classify_text(text):
result = classifier(text)[0]
label = result['label']
score = result['score']
return label, score
# Create the Gradio interface
demo = gr.Interface(
fn=classify_text,
inputs=gr.Textbox(lines=2, label="Job description text", placeholder="Enter a job description..."),
outputs=[gr.Textbox(label="ISCO-08 Label"), gr.Number(label="Score")],
title="XLM-R ISCO classification with ZeroGPU",
description="Classify occupations using a pre-trained XLM-R-ISCO model on Hugging Face Spaces with ZeroGPU"
)
if __name__ == "__main__":
demo.launch() |