custom_tensorflow_keras_nlp/util/preprocessing.py [44:54]:
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def dummy_encode_labels(df,label):
    encoder = preprocessing.LabelEncoder()
    encoded_y = encoder.fit_transform(df[label].values)
    num_classes = len(encoder.classes_)
    # convert integers to dummy variables (i.e. one hot encoded)
    dummy_y = np.eye(num_classes, dtype="float32")[encoded_y]
    return dummy_y, encoder.classes_


def tokenize_and_pad_docs(df, columns, max_length=40):
    docs = df[columns].values
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pytorch_alternatives/custom_pytorch_nlp/util/preprocessing.py [67:77]:
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def dummy_encode_labels(df,label):
    encoder = preprocessing.LabelEncoder()
    encoded_y = encoder.fit_transform(df[label].values)
    num_classes = len(encoder.classes_)
    # convert integers to dummy variables (i.e. one hot encoded)
    dummy_y = np.eye(num_classes, dtype="float32")[encoded_y]
    return dummy_y, encoder.classes_


def tokenize_and_pad_docs(df, columns, max_length=40):
    docs = df[columns].values
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