---
# dialogue-bart-large-chinese
This is a seq2seq model fine-tuned on several Chinese dialogue datasets, from bart-large-chinese.


# Datasets
We utilize 4 Chinese dialogue datasets from [LUGE](https://www.luge.ai/#/)

|                              |            |                       |
| ----                         | ----       | ----                  |
|                              | Count      | Domain                |
| Chinese Persona Chat (CPC)   | 23,000     | Open                  | 
| LCCC                         | 11,987,759 | Open                  |
| Emotional STC (ESTC)         | 899,207    | Open                  |
| KdConv                       | 3,000      | Movie, Music, Travel  |
|                              |            |                       |


# Example
```python
from transformers import BertTokenizer, BartForConditionalGeneration

# Note that tokenizer is an object of BertTokenizer, instead of BartTokenizer
tokenizer = BertTokenizer.from_pretrained("HIT-TMG/dialogue-bart-large-chinese")
model = BartForConditionalGeneration.from_pretrained("HIT-TMG/dialogue-bart-large-chinese")

# an example from CPC dev data
dialogue_history = "可以 认识 一下 吗 ？ [SEP] 当然 可以 啦 ， 你好 。 [SEP] 嘿嘿 你好 ， 请问 你 最近 在 忙 什么 呢 ？ [SEP] 我 最近 养 了 一只 狗狗 ， 我 在 训练 它 呢 。"
input_ids = tokenizer(dialogue_history, return_tensors='pt').input_ids
output_ids = model.generate(input_ids)[0]
print(tokenizer.decode(output_ids))
 ```