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--- |
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language: |
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- ru |
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- zh |
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- en |
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tags: |
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- translation |
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license: apache-2.0 |
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datasets: |
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- ccmatrix |
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base_model: |
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- utrobinmv/t5_translate_en_ru_zh_large_1024 |
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metrics: |
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- sacrebleu |
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widget: |
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- example_title: translate zh-ru |
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text: > |
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translate to ru: 开发的目的是为用户提供个人同步翻译。 |
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- example_title: translate ru-en |
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text: > |
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translate to en: Цель разработки — предоставить пользователям личного синхронного переводчика. |
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- example_title: translate en-ru |
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text: > |
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translate to ru: The purpose of the development is to provide users with a personal synchronized interpreter. |
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- example_title: translate en-zh |
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text: > |
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translate to zh: The purpose of the development is to provide users with a personal synchronized interpreter. |
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- example_title: translate zh-en |
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text: > |
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translate to en: 开发的目的是为用户提供个人同步解释器。 |
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- example_title: translate ru-zh |
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text: > |
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translate to zh: Цель разработки — предоставить пользователям личного синхронного переводчика. |
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--- |
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# T5 English, Russian and Chinese multilingual machine translation |
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This model represents a conventional T5 transformer in multitasking mode for translation into the required language, precisely configured for machine translation for pairs: ru-zh, zh-ru, en-zh, zh-en, en-ru, ru-en. |
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The model can perform direct translation between any pair of Russian, Chinese or English languages. For translation into the target language, the target language identifier is specified as a prefix 'translate to <lang>:'. In this case, the source language may not be specified, in addition, the source text may be multilingual. |
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Fine tune from the base model: utrobinmv/t5_translate_en_ru_zh_large_1024 |
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This version of the model was based on noisier data with a noise reduction function. |
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The model can additionally insert punctuation marks into sentences if they are missing from the source text. This is convenient to use for translating texts after ASR models. |
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The model has learned how to translate small markdown files while maintaining the markup and html tags. |
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Example translate Russian to Chinese |
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```python |
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from transformers import T5ForConditionalGeneration, T5Tokenizer |
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device = 'cuda' #or 'cpu' for translate on cpu |
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model_name = 'utrobinmv/t5_translate_en_ru_zh_large_1024_v2' |
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model = T5ForConditionalGeneration.from_pretrained(model_name) |
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model.eval() |
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model.to(device) |
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tokenizer = T5Tokenizer.from_pretrained(model_name) |
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prefix = 'translate to zh: ' |
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src_text = prefix + "Съешь ещё этих мягких французских булок." |
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# translate Russian to Chinese |
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input_ids = tokenizer(src_text, return_tensors="pt") |
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generated_tokens = model.generate(**input_ids.to(device)) |
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) |
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print(result) |
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# 再吃这些法国的甜蜜的面包。 |
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``` |
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and Example translate Chinese to Russian |
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```python |
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from transformers import T5ForConditionalGeneration, T5Tokenizer |
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device = 'cuda' #or 'cpu' for translate on cpu |
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model_name = 'utrobinmv/t5_translate_en_ru_zh_large_1024_v2' |
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model = T5ForConditionalGeneration.from_pretrained(model_name) |
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model.eval() |
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model.to(device) |
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tokenizer = T5Tokenizer.from_pretrained(model_name) |
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prefix = 'translate to ru: ' |
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src_text = prefix + "再吃这些法国的甜蜜的面包。" |
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# translate Russian to Chinese |
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input_ids = tokenizer(src_text, return_tensors="pt") |
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generated_tokens = model.generate(**input_ids.to(device)) |
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) |
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print(result) |
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# Съешьте этот сладкий хлеб из Франции. |
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``` |
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## |
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## Languages covered |
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Russian (ru_RU), Chinese (zh_CN), English (en_US) |
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