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from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline, pipeline |
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model_checkpoint = "Helsinki-NLP/opus-mt-en-fr" |
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translator = pipeline("translation", model=model_checkpoint) |
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from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline |
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pipeline = TranslationPipeline( |
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model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_fr_en"), |
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tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = |
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"SEBIS/legal_t5_small_trans_fr_en", do_lower_case=False, skip_special_tokens=True), device=0) |
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fr_text = "salut, comment vas-tu ?" |
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translator2 = pipeline([fr_text], max_length=512) |
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print(translator2) |
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responseBase = "this is the second test" |
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def englishtofrench(): |
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print(translator(responseBase)) |
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return translator(responseBase) |
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englishtofrench() |
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