textflint.generation_layer.transformation.UT.swap_named_ent¶
SwapNamedEnt substitute class¶
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class
textflint.generation_layer.transformation.UT.swap_named_ent.SwapNamedEnt(entity_res=None, **kwargs)[source]¶ Bases:
textflint.generation_layer.transformation.transformation.TransformationSwap entities with other entities of the same category.
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__init__(entity_res=None, **kwargs)[source]¶ - Parameters
entity_res (dict) – dic of categories and their entities.
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static
decompose_entities_info(entities_info)[source]¶ Decompose given entities and normalize entity tag to [‘LOCATION’, ‘PERSON’, ‘ORGANIZATION’]
Example:
[('Lionel Messi', 0, 2, 'PERSON'), ('Argentina', 7, 8, 'LOCATION')]
- >> [[0, 2], [7, 8]], [‘Lionel Messi’, ‘Argentina’],
[‘PERSON’, ‘LOCATION’]
- Parameters
entities_info (dict) – parsed by default ner component.
- Return list indices
indices
- Return list entities
entity values
- Return list categories
categories
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class
textflint.generation_layer.transformation.UT.swap_named_ent.Transformation(**kwargs)[source]¶ Bases:
abc.ABCAn abstract class for transforming a sequence of text to produce a list of potential adversarial example.
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processor= <textflint.common.preprocess.en_processor.EnProcessor object>¶
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transform(sample, n=1, field='x', **kwargs)[source]¶ Transform data sample to a list of Sample.
- Parameters
sample (Sample) – Data sample for augmentation.
n (int) – Max number of unique augmented output, default is 5.
field (str|list) – Indicate which fields to apply transformations.
**kwargs (dict) –
other auxiliary params.
- Returns
list of Sample
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textflint.generation_layer.transformation.UT.swap_named_ent.download_if_needed(folder_name)[source]¶ Folder name will be saved as .cache/textflint/[folder_name]. If it doesn’t exist on disk, the zip file will be downloaded and extracted.
- Parameters
folder_name (str) – path to folder or file in cache
- Returns
path to the downloaded folder or file on disk
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textflint.generation_layer.transformation.UT.swap_named_ent.trade_off_sub_words(sub_words, sub_indices, trans_num=None, n=1)[source]¶ Select proper candidate words to maximum number of transform result. Select words of top n substitutes words number.
- Parameters
sub_words (list) – list of substitutes word of each legal word
sub_indices (list) – list of indices of each legal word
trans_num (int) – max number of words to apply substitution
n (int) –
- Returns
sub_words after alignment + indices of sub_words