Update app.py
Browse files
app.py
CHANGED
@@ -10,6 +10,10 @@ import pandas as pd
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model_name = "MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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def classify_text(text):
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"""
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@@ -19,9 +23,8 @@ def classify_text(text):
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Returns:
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str: La clasificaci贸n del texto, que puede ser "Positivo", "Negativo" o "Neutro".
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"""
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result =
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#result = classifier(text)[0]['label']
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if result == "POS":
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return "Positivo"
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elif result == "NEG":
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@@ -37,6 +40,8 @@ def clasificador(input1, input2):
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output0 = classifier(sequence_to_classify, candidate_labels, multi_label=False)
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output1=pd.DataFrame(output0)
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output1=output1.iloc[:,1:3]
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output2=classify_text(input1)
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return output1, output2
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model_name = "MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Inicializa Modelo de Clasificaci贸n de Sentimientos
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model_name = 'pysentimiento/robertuito-sentiment-analysis'
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classifier_sent = pipeline("text-classification", model=model_name)
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def classify_text(text):
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"""
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Returns:
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str: La clasificaci贸n del texto, que puede ser "Positivo", "Negativo" o "Neutro".
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"""
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result = classifier_sent(text)[0]['label']
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if result == "POS":
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return "Positivo"
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elif result == "NEG":
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output0 = classifier(sequence_to_classify, candidate_labels, multi_label=False)
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output1=pd.DataFrame(output0)
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output1=output1.iloc[:,1:3]
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#analyzer = create_analyzer(task="sentiment", lang="es")
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#output2 = analyzer.predict(input1)
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output2=classify_text(input1)
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return output1, output2
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