The model can generate definition in Chinese. Usage:
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline

checkpoint = 'PoeticPaper/mbart-large-50_definition_zh_CN'
tokenizer = AutoTokenizer.from_pretrained(checkpoint, src_lang="zh_CN", tgt_lang="zh_CN")
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
generator = pipeline(
    task='text2text-generation',
    model=model,
    tokenizer=tokenizer,
    forced_bos_token_id=tokenizer.lang_code_to_id["zh_CN"],
)

input = "define: 槌 context: 能够 带来 幸福 的 小 槌 吊饰 共有 金白 两色 。 definition:"
output = generator(input) # output will be: "圆柱形 的 物体 。""


{}
transformers pytorch mbart text2text-generation autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us 

The model can generate definition in Chinese. Usage:
'''python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline

checkpoint = 'PoeticPaper/mbart-large-50_definition_zh_CN'
tokenizer = AutoTokenizer.from_pretrained(checkpoint, src_lang="zh_CN", tgt_lang="zh_CN")
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
generator = pipeline(
    task='text2text-generation',
    model=model,
    tokenizer=tokenizer,
    forced_bos_token_id=tokenizer.lang_code_to_id["zh_CN"],
)

input = "define: 槌 context: 能够 带来 幸福 的 小 槌 吊饰 共有 金白 两色 。 definition:"
output = generator(input) # output will be: "圆柱形 的 物体 。""


# output will be: "圆柱形 的 物体 。""
TAGS
#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us 
 # output will be: "圆柱形 的 物体 。""