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from transformers import ( |
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MarianMTModel, |
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MarianTokenizer, |
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) |
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class BackTranslator: |
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""" |
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Perform Back-translation with pivot language. English -> German -> Spanish -> English |
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Args: |
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source_lang: Source language (default: 'en') |
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pivot_lang: Pivot language (default: 'de') |
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target_lang: Target language (default: 'es') |
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Examples: |
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back_translator = BackTranslator() |
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back_translator.back_translate("Hello, how are you?") |
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""" |
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def __init__(self, source_lang='en', pivot_lang='de', target_lang='es'): |
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pivot_forward_model_name = f'Helsinki-NLP/opus-mt-{source_lang}-{pivot_lang}' |
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self.tokenizer_pivot_forward = MarianTokenizer.from_pretrained(pivot_forward_model_name) |
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self.model_pivot_forward = MarianMTModel.from_pretrained(pivot_forward_model_name) |
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pivot_backward_model_name = f'Helsinki-NLP/opus-mt-{pivot_lang}-{target_lang}' |
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self.tokenizer_pivot_backward = MarianTokenizer.from_pretrained(pivot_backward_model_name) |
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self.model_pivot_backward = MarianMTModel.from_pretrained(pivot_backward_model_name) |
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backward_model_name = f'Helsinki-NLP/opus-mt-{target_lang}-{source_lang}' |
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self.tokenizer_backward = MarianTokenizer.from_pretrained(backward_model_name) |
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self.model_backward = MarianMTModel.from_pretrained(backward_model_name) |
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def back_translate(self, text): |
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""" |
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Perform back-translation through German and Spanish to generate text variations. |
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Args: |
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text (str): The input text to be back-translated |
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Returns: |
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str: The back-translated text |
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""" |
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encoded_pivot = self.tokenizer_pivot_forward([text], padding=True, truncation=True, return_tensors='pt') |
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generated_pivot = self.model_pivot_forward.generate(**encoded_pivot) |
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pivot_text = self.tokenizer_pivot_forward.batch_decode(generated_pivot, skip_special_tokens=True)[0] |
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encoded_back_pivot = self.tokenizer_pivot_backward([pivot_text], padding=True, truncation=True, return_tensors='pt') |
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retranslated_pivot = self.model_pivot_backward.generate(**encoded_back_pivot) |
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tgt_text_back = self.tokenizer_pivot_backward.batch_decode(retranslated_pivot, skip_special_tokens=True)[0] |
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encoded_back = self.tokenizer_backward([tgt_text_back], padding=True, truncation=True, return_tensors='pt') |
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retranslated = self.model_backward.generate(**encoded_back) |
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src_text = self.tokenizer_backward.batch_decode(retranslated, skip_special_tokens=True)[0] |
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return src_text |