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app.py
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# Necessary imports
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import gradio as gr
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from transformers import pipeline
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# Load the zero-shot classification model
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classifier = pipeline(
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"zero-shot-classification", model="MoritzLaurer/ModernBERT-large-zeroshot-v2.0"
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)
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# Function to perform zero-shot classification
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def ZeroShotTextClassification(text_input, candidate_labels):
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"""
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Performs zero-shot classification on the given text input using the provided candidate labels.
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Args:
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- text_input (str): The input text to classify.
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- candidate_labels (str): A comma-separated string of candidate labels.
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Returns:
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dict: A dictionary containing the predicted labels as keys and their corresponding scores as values.
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"""
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# Split the candidate labels
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labels = [label.strip(" ") for label in candidate_labels.split(",")]
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# Perform zero-shot classification
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prediction = classifier(text_input, labels)
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return {
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prediction["labels"][i]: prediction["scores"][i]
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for i in range(len(prediction["labels"]))
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}
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# Examples to display in the interface
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examples = [
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["I love to play the guitar", "music, artist, food, travel"],
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["I am a software engineer at Google", "technology, engineering, art, science"],
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["I am a professional basketball player", "sports, athlete, chef, politics"],
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]
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# Title and description and article for the interface
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title = "Zero Shot Text Classification"
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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."
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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>"
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# Launch the interface
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demo = gr.Interface(
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fn=ZeroShotTextClassification,
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inputs=[gr.Textbox(label="Input"), gr.Textbox(label="Candidate Labels")],
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outputs=gr.Label(label="Classification"),
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title=title,
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description=description,
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article=article,
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examples=examples,
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cache_examples=True,
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cache_mode="lazy",
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theme="Soft",
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flagging_mode="never",
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)
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demo.launch(debug=False)
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