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"""
Augmenter Recipes:
===================
Transformations and constraints can be used for simple NLP data augmentations. Here is a list of recipes for NLP data augmentations
"""
import random
from textattack.constraints.pre_transformation import (
RepeatModification,
StopwordModification,
)
from textattack.constraints.semantics.sentence_encoders import UniversalSentenceEncoder
from . import Augmenter
DEFAULT_CONSTRAINTS = [RepeatModification(), StopwordModification()]
class EasyDataAugmenter(Augmenter):
"""An implementation of Easy Data Augmentation, which combines:
- WordNet synonym replacement
- Randomly replace words with their synonyms.
- Word deletion
- Randomly remove words from the sentence.
- Word order swaps
- Randomly swap the position of words in the sentence.
- Random synonym insertion
- Insert a random synonym of a random word at a random location.
in one augmentation method.
"EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks" (Wei and Zou, 2019)
https://arxiv.org/abs/1901.11196
"""
def __init__(self, pct_words_to_swap=0.1, transformations_per_example=4):
assert 0.0 <= pct_words_to_swap <= 1.0, "pct_words_to_swap must be in [0., 1.]"
assert (
transformations_per_example > 0
), "transformations_per_example must be a positive integer"
self.pct_words_to_swap = pct_words_to_swap
self.transformations_per_example = transformations_per_example
n_aug_each = max(transformations_per_example // 4, 1)
self.synonym_replacement = WordNetAugmenter(
pct_words_to_swap=pct_words_to_swap,
transformations_per_example=n_aug_each,
)
self.random_deletion = DeletionAugmenter(
pct_words_to_swap=pct_words_to_swap,
transformations_per_example=n_aug_each,
)
self.random_swap = SwapAugmenter(
pct_words_to_swap=pct_words_to_swap,
transformations_per_example=n_aug_each,
)
self.random_insertion = SynonymInsertionAugmenter(
pct_words_to_swap=pct_words_to_swap, transformations_per_example=n_aug_each
)
def augment(self, text):
augmented_text = []
augmented_text += self.synonym_replacement.augment(text)
augmented_text += self.random_deletion.augment(text)
augmented_text += self.random_swap.augment(text)
augmented_text += self.random_insertion.augment(text)
augmented_text = list(set(augmented_text))
random.shuffle(augmented_text)
return augmented_text[: self.transformations_per_example]
def __repr__(self):
return "EasyDataAugmenter"
class SwapAugmenter(Augmenter):
def __init__(self, **kwargs):
from textattack.transformations import WordInnerSwapRandom
transformation = WordInnerSwapRandom()
super().__init__(transformation, constraints=DEFAULT_CONSTRAINTS, **kwargs)
class SynonymInsertionAugmenter(Augmenter):
def __init__(self, **kwargs):
from textattack.transformations import WordInsertionRandomSynonym
transformation = WordInsertionRandomSynonym()
super().__init__(transformation, constraints=DEFAULT_CONSTRAINTS, **kwargs)
class WordNetAugmenter(Augmenter):
"""Augments text by replacing with synonyms from the WordNet thesaurus."""
def __init__(self, **kwargs):
from textattack.transformations import WordSwapWordNet
transformation = WordSwapWordNet()
super().__init__(transformation, constraints=DEFAULT_CONSTRAINTS, **kwargs)
class DeletionAugmenter(Augmenter):
def __init__(self, **kwargs):
from textattack.transformations import WordDeletion
transformation = WordDeletion()
super().__init__(transformation, constraints=DEFAULT_CONSTRAINTS, **kwargs)
class EmbeddingAugmenter(Augmenter):
"""Augments text by transforming words with their embeddings."""
def __init__(self, **kwargs):
from textattack.transformations import WordSwapEmbedding
transformation = WordSwapEmbedding(max_candidates=50)
from textattack.constraints.semantics import WordEmbeddingDistance
constraints = DEFAULT_CONSTRAINTS + [WordEmbeddingDistance(min_cos_sim=0.8)]
super().__init__(transformation, constraints=constraints, **kwargs)
class CharSwapAugmenter(Augmenter):
"""Augments words by swapping characters out for other characters."""
def __init__(self, **kwargs):
from textattack.transformations import (
CompositeTransformation,
WordSwapNeighboringCharacterSwap,
WordSwapRandomCharacterDeletion,
WordSwapRandomCharacterInsertion,
WordSwapRandomCharacterSubstitution,
)
transformation = CompositeTransformation(
[
# (1) Swap: Swap two adjacent letters in the word.
WordSwapNeighboringCharacterSwap(),
# (2) Substitution: Substitute a letter in the word with a random letter.
WordSwapRandomCharacterSubstitution(),
# (3) Deletion: Delete a random letter from the word.
WordSwapRandomCharacterDeletion(),
# (4) Insertion: Insert a random letter in the word.
WordSwapRandomCharacterInsertion(),
]
)
super().__init__(transformation, constraints=DEFAULT_CONSTRAINTS, **kwargs)
class CheckListAugmenter(Augmenter):
"""Augments words by using the transformation methods provided by CheckList
INV testing, which combines:
- Name Replacement
- Location Replacement
- Number Alteration
- Contraction/Extension
"Beyond Accuracy: Behavioral Testing of NLP models with CheckList" (Ribeiro et al., 2020)
https://arxiv.org/abs/2005.04118
"""
def __init__(self, **kwargs):
from textattack.transformations import (
CompositeTransformation,
WordSwapChangeLocation,
WordSwapChangeName,
WordSwapChangeNumber,
WordSwapContract,
WordSwapExtend,
)
transformation = CompositeTransformation(
[
WordSwapChangeNumber(),
WordSwapChangeLocation(),
WordSwapChangeName(),
WordSwapExtend(),
WordSwapContract(),
]
)
constraints = [DEFAULT_CONSTRAINTS[0]]
super().__init__(transformation, constraints=constraints, **kwargs)
class CLAREAugmenter(Augmenter):
"""Li, Zhang, Peng, Chen, Brockett, Sun, Dolan.
"Contextualized Perturbation for Textual Adversarial Attack" (Li et al., 2020)
https://arxiv.org/abs/2009.07502
CLARE builds on a pre-trained masked language model and modifies the inputs in a contextaware manner.
We propose three contextualized perturbations, Replace, Insert and Merge, allowing for generating outputs
of varied lengths.
"""
def __init__(
self, model="distilroberta-base", tokenizer="distilroberta-base", **kwargs
):
import transformers
from textattack.transformations import (
CompositeTransformation,
WordInsertionMaskedLM,
WordMergeMaskedLM,
WordSwapMaskedLM,
)
shared_masked_lm = transformers.AutoModelForCausalLM.from_pretrained(model)
shared_tokenizer = transformers.AutoTokenizer.from_pretrained(tokenizer)
transformation = CompositeTransformation(
[
WordSwapMaskedLM(
method="bae",
masked_language_model=shared_masked_lm,
tokenizer=shared_tokenizer,
max_candidates=50,
min_confidence=5e-4,
),
WordInsertionMaskedLM(
masked_language_model=shared_masked_lm,
tokenizer=shared_tokenizer,
max_candidates=50,
min_confidence=0.0,
),
WordMergeMaskedLM(
masked_language_model=shared_masked_lm,
tokenizer=shared_tokenizer,
max_candidates=50,
min_confidence=5e-3,
),
]
)
use_constraint = UniversalSentenceEncoder(
threshold=0.7,
metric="cosine",
compare_against_original=True,
window_size=15,
skip_text_shorter_than_window=True,
)
constraints = DEFAULT_CONSTRAINTS + [use_constraint]
super().__init__(transformation, constraints=constraints, **kwargs)
class BackTranslationAugmenter(Augmenter):
"""Sentence level augmentation that uses MarianMTModel to back-translate.
https://huggingface.co/transformers/model_doc/marian.html
"""
def __init__(self, **kwargs):
from textattack.transformations.sentence_transformations import BackTranslation
transformation = BackTranslation(chained_back_translation=5)
super().__init__(transformation, **kwargs)
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