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data-silence
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d6ecd3b
Update app.py
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app.py
CHANGED
@@ -1,30 +1,30 @@
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from transformers import pipeline
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# Загрузка модели через pipeline
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classifier = pipeline("text-classification", model="data-silence/
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# Словарь для преобразования меток
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category_mapper = {
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'LABEL_0': 'climate',
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'LABEL_1': 'conflicts',
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'LABEL_2': 'culture',
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'LABEL_3': 'economy',
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'LABEL_4': 'gloss',
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'LABEL_5': 'health',
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'LABEL_6': 'politics',
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'LABEL_7': 'science',
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'LABEL_8': 'society',
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'LABEL_9': 'sports',
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'LABEL_10': 'travel'
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}
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def classify(text):
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result = classifier(text)
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category = category_mapper[result[0]['label']]
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score = result[0]['score']
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return {"category": category, "confidence": score}
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# Для Gradio интерфейса
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def run_inference(text):
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result = classify(text)
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return f"Predicted category: {result['category']} (confidence: {result['confidence']:.2f})"
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from transformers import pipeline
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# Загрузка модели через pipeline
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classifier = pipeline("text-classification", model="data-silence/news_classifier_ft")
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# Словарь для преобразования меток
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category_mapper = {
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'LABEL_0': 'climate',
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'LABEL_1': 'conflicts',
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'LABEL_2': 'culture',
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'LABEL_3': 'economy',
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'LABEL_4': 'gloss',
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'LABEL_5': 'health',
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'LABEL_6': 'politics',
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'LABEL_7': 'science',
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'LABEL_8': 'society',
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'LABEL_9': 'sports',
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'LABEL_10': 'travel'
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}
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def classify(text):
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result = classifier(text)
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category = category_mapper[result[0]['label']]
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score = result[0]['score']
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return {"category": category, "confidence": score}
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# Для Gradio интерфейса
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def run_inference(text):
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result = classify(text)
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return f"Predicted category: {result['category']} (confidence: {result['confidence']:.2f})"
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