metadata
license: mit
language:
- en
- zh
library_name: transformers
tags:
- translation
- fine tune
- fine_tune
- mbart-50
inference:
parameters:
src_lang: en_XX
tgt_lang: zh_CN
widget:
- text: >-
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.
pipeline_tag: translation
Normal1919/mbart-large-50-one-to-many-lil-fine-tune
- base model: mbart-large-50
- pretrained_ckpt: facebook/mbart-large-50-one-to-many-mmt
- This model was trained for rpy dl translate
Model description
- source group: English
- target group: Chinese
- model: transformer
- source language(s): eng
- target language(s): cjy_Hans cjy_Hant cmn cmn_Hans cmn_Hant gan lzh lzh_Hans nan wuu yue yue_Hans yue_Hant
- 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
How to use
>>> from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
>>> mode_name = 'Normal1919/mbart-large-50-one-to-many-lil-fine-tune'
>>> model = MBartForConditionalGeneration.from_pretrained(mode_name)
>>> tokenizer = MBart50TokenizerFast.from_pretrained(mode_name, src_lang="en_XX", tgt_lang="zh_CN")
>>> translation = pipeline("mbart-large-50-one-to-many-lil-fine-tune", model=model, tokenizer=tokenizer)
>>> 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)
[{'我{i}应该{/i}说,我有点松了一口气,发现{i}这个{/i}是你最近和Kaori一起出去玩的原因。她真的很漂亮,我嫉妒了,而且......对不起。'}]