README.md

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# 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  |
|                              |            |                       |


# Data format
Input: `[CLS] 对话历史：<history> 知识：<knowledge> [SEP]`
Output: `[CLS] <response> [SEP]`


# 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
history = ["可以 认识 一下 吗 ？", "当然 可以 啦 ， 你好 。", "嘿嘿 你好 ， 请问 你 最近 在 忙 什么 呢 ？", "我 最近 养 了 一只 狗狗 ， 我 在 训练 它 呢 。"]
history_str = "对话历史：" + tokenizer.sep_token.join(history)
input_ids = tokenizer(history_str, return_tensors='pt').input_ids
output_ids = model.generate(input_ids)[0]
print(tokenizer.decode(output_ids))
 ```