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# Necessary imports
import gradio as gr
from transformers import pipeline
# Load the zero-shot classification model
classifier = pipeline(
"zero-shot-classification", model="MoritzLaurer/ModernBERT-large-zeroshot-v2.0"
)
# Function to perform zero-shot classification
def ZeroShotTextClassification(text_input, candidate_labels):
"""
Performs zero-shot classification on the given text input using the provided candidate labels.
Args:
- text_input (str): The input text to classify.
- candidate_labels (str): A comma-separated string of candidate labels.
Returns:
dict: A dictionary containing the predicted labels as keys and their corresponding scores as values.
"""
# Split the candidate labels
labels = [label.strip(" ") for label in candidate_labels.split(",")]
# Perform zero-shot classification
prediction = classifier(text_input, labels)
return {
prediction["labels"][i]: prediction["scores"][i]
for i in range(len(prediction["labels"]))
}
# Examples to display in the interface
examples = [
["I love to play the guitar", "music, artist, food, travel"],
["I am a software engineer at Google", "technology, engineering, art, science"],
["I am a professional basketball player", "sports, athlete, chef, politics"],
]
# Title and description and article for the interface
title = "Zero Shot Text Classification"
description = "Classify text using zero-shot classification with ModernBERT-large zeroshot model! Provide a text input and a list of candidate labels separated by commas. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2412.13663' target='_blank'>Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference</a> | <a href='https://huggingface.co/MoritzLaurer/ModernBERT-large-zeroshot-v2.0' target='_blank'>Model Page</a></p>"
# Launch the interface
demo = gr.Interface(
fn=ZeroShotTextClassification,
inputs=[gr.Textbox(label="Input"), gr.Textbox(label="Candidate Labels")],
outputs=gr.Label(label="Classification"),
title=title,
description=description,
article=article,
examples=examples,
cache_examples=True,
cache_mode="lazy",
theme="Soft",
flagging_mode="never",
)
demo.launch(debug=False)
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