---
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](https://github.com/O5-7/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
```python
>>> 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。'}]
```

## Contact

517205163@qq.com or
a4564563@gmail.com