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  license: mit
 
 
 
 
 
 
 
 
 
 
 
 
 
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  license: mit
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+ language:
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+ - en
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+ - zh
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+ library_name: transformers
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+ tags:
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+ - translation
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+ - fine tune
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+ - fine_tune
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+ widget:
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+ - text: >-
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+ I {i}should{/i} say that I feel a little relieved to find out that
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+ {i}this{/i} is why you’ve been hanging out with Kaori lately, though. She’s
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+ really pretty and I got jealous and...I’m sorry.
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  ---
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+
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+ # Normal1919/mbart-large-50-one-to-many-lil-fine-tune
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+
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+ * base model: mbart-large-50
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+ * pretrained_ckpt: facebook/mbart-large-50-one-to-many-mmt
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+ * This model was trained for [rpy dl translate](https://github.com/O5-7/rpy_dl_translate)
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+
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+ ## Model description
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+ * source group: English
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+ * target group: Chinese
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+ * model: transformer
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+ * source language(s): eng
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+ * target language(s): cjy_Hans cjy_Hant cmn cmn_Hans cmn_Hant gan lzh lzh_Hans nan wuu yue yue_Hans yue_Hant
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+ * fine_tune: On the basis of mbart-large-50-one-to-many-mmt checkpoints, train English original text with renpy text features (including but not limited to {i} [text] {/i}) to Chinese with the same reserved flag, as well as training for English name retention for LIL
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+
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+ ## How to use
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+ ```python
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+ >>> from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
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+ >>> mode_name = 'Normal1919/mbart-large-50-one-to-many-lil-fine-tune'
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+ >>> model = MBartForConditionalGeneration.from_pretrained(mode_name)
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+ >>> tokenizer = MBart50TokenizerFast.from_pretrained(mode_name, src_lang="en_XX", tgt_lang="zh_CN")
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+ >>> translation = pipeline("Marian-NMT-en-zh-lil-fine-tune", model=model, tokenizer=tokenizer)
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+ >>> translation('I {i} should {/i} say that I feel a little relieved to find out that {i}this {/i} is why you’ve been hanging out with Kaori lately, though. She’s really pretty and I got jealous and...I’m sorry', max_length=400)
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+ [{'我{i}应该{/i}说发现{i}这{/i}是你最近和Kaori出去的原因,我有点松了一口气。她很漂亮,我嫉妒,而且......我很抱歉。'}]
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+ ```
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+
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+ ## Contact
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+
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+ 517205163@qq.com or
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+ a4564563@gmail.com