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import gradio as gr |
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from transformers import pipeline |
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model = pipeline("text-classification", |
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model="OpenAlex/bert-base-multilingual-cased-finetuned-openalex-topic-classification-title-abstract") |
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def classify_text(text, top_k): |
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result = model(text, top_k=top_k, truncation=True, max_length=512) |
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return {p["label"]: p["score"] for p in result} |
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with gr.Blocks() as demo: |
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gr.Interface( |
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fn=classify_text, |
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inputs=[gr.Textbox(lines=5, label="Text", placeholder="<TITLE> {title}\n<ABSTRACT> {abstract}", |
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value="<TITLE> {title}\n<ABSTRACT> {abstract}"), |
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gr.Number(label="top_k", value=10, precision=0)], |
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outputs=gr.Label(label="openalex topic predicted"), |
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title="OpenAlex topic classification", |
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description="Enter a text and see the topic classification result!", |
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flagging_mode="never", |
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api_name="classify" |
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) |
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if __name__ == "__main__": |
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print(gr.__version__) |
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demo.launch() |
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