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--- |
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language: |
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- en |
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- is |
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- multilingual |
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tags: |
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- translation |
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inference: |
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parameters: |
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src_lang: en_XX |
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tgt_lang: is_IS |
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decoder_start_token_id: 2 |
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max_length: 512 |
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widget: |
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- text: I once owned a horse. It was black and white. |
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--- |
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# mBART based translation model |
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This model was trained to translate multiple sentences at once, compared to one sentence at a time. |
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It will occasionally combine sentences or add an extra sentence. |
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This is the same model as are provided on CLARIN: https://repository.clarin.is/repository/xmlui/handle/20.500.12537/278 |
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You can use the following example to get started (note that it is necessary to alter the `decoder_start_token_id` of the model): |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline |
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import torch |
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device = torch.cuda.current_device() if torch.cuda.is_available() else -1 |
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tokenizer = AutoTokenizer.from_pretrained("mideind/nmt-doc-en-is-2022-10",src_lang="en_XX",tgt_lang="is_IS") |
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model = AutoModelForSeq2SeqLM.from_pretrained("mideind/nmt-doc-en-is-2022-10") |
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model.config.decoder_start_token_id = 2 |
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translate = pipeline("translation_XX_to_YY",model=model,tokenizer=tokenizer,device=device,src_lang="en_XX",tgt_lang="is_IS") |
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target_seq = translate("I am using a translation model to translate text from English to Icelandic.",src_lang="en_XX",tgt_lang="is_IS",max_length=128) |
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print(target_seq[0]['translation_text'].strip('YY ')) |
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