Source code for textattack.transformations.sentence_transformations.back_translation

BackTranslation class


import random

from transformers import MarianMTModel, MarianTokenizer

from textattack.shared import AttackedText

from .sentence_transformation import SentenceTransformation

[docs]class BackTranslation(SentenceTransformation): """A type of sentence level transformation that takes in a text input, translates it into target language and translates it back to source language. letters_to_insert (string): letters allowed for insertion into words (used by some char-based transformations) src_lang (string): source language target_lang (string): target language, for the list of supported language check bottom of this page src_model: translation model from huggingface that translates from source language to target language target_model: translation model from huggingface that translates from target language to source language chained_back_translation: run back translation in a chain for more perturbation (for example, en-es-en-fr-en) Example:: >>> from textattack.transformations.sentence_transformations import BackTranslation >>> from textattack.constraints.pre_transformation import RepeatModification, StopwordModification >>> from textattack.augmentation import Augmenter >>> transformation = BackTranslation >>> constraints = [RepeatModification(), StopwordModification()] >>> augmenter = Augmenter(transformation = transformation, constraints = constraints) >>> s = 'What on earth are you doing here.' >>> augmenter.augment(s) """ def __init__( self, src_lang="en", target_lang="es", src_model="Helsinki-NLP/opus-mt-ROMANCE-en", target_model="Helsinki-NLP/opus-mt-en-ROMANCE", chained_back_translation=0, ): self.src_lang = src_lang self.target_lang = target_lang self.target_model = MarianMTModel.from_pretrained(target_model) self.target_tokenizer = MarianTokenizer.from_pretrained(target_model) self.src_model = MarianMTModel.from_pretrained(src_model) self.src_tokenizer = MarianTokenizer.from_pretrained(src_model) self.chained_back_translation = chained_back_translation
[docs] def translate(self, input, model, tokenizer, lang="es"): # change the text to model's format src_texts = [] if lang == "en": src_texts.append(input[0]) else: if ">>" and "<<" not in lang: lang = ">>" + lang + "<< " src_texts.append(lang + input[0]) # tokenize the input encoded_input = tokenizer.prepare_seq2seq_batch(src_texts, return_tensors="pt") # translate the input translated = model.generate(**encoded_input) translated_input = tokenizer.batch_decode(translated, skip_special_tokens=True) return translated_input
def _get_transformations(self, current_text, indices_to_modify): transformed_texts = [] current_text = current_text.text # to perform chained back translation, a random list of target languages are selected from the provided model if self.chained_back_translation: list_of_target_lang = random.sample( self.target_tokenizer.supported_language_codes, self.chained_back_translation, ) for target_lang in list_of_target_lang: target_language_text = self.translate( [current_text], self.target_model, self.target_tokenizer, target_lang, ) src_language_text = self.translate( target_language_text, self.src_model, self.src_tokenizer, self.src_lang, ) current_text = src_language_text[0] return [AttackedText(current_text)] # translates source to target language and back to source language (single back translation) target_language_text = self.translate( [current_text], self.target_model, self.target_tokenizer, self.target_lang ) src_language_text = self.translate( target_language_text, self.src_model, self.src_tokenizer, self.src_lang ) transformed_texts.append(AttackedText(src_language_text[0])) return transformed_texts
""" List of supported languages ['fr', 'es', 'it', 'pt', 'pt_br', 'ro', 'ca', 'gl', 'pt_BR<<', 'la<<', 'wa<<', 'fur<<', 'oc<<', 'fr_CA<<', 'sc<<', 'es_ES', 'es_MX', 'es_AR', 'es_PR', 'es_UY', 'es_CL', 'es_CO', 'es_CR', 'es_GT', 'es_HN', 'es_NI', 'es_PA', 'es_PE', 'es_VE', 'es_DO', 'es_EC', 'es_SV', 'an', 'pt_PT', 'frp', 'lad', 'vec', 'fr_FR', 'co', 'it_IT', 'lld', 'lij', 'lmo', 'nap', 'rm', 'scn', 'mwl'] """