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---
language:
- zh
thumbnail: "url to a thumbnail used in social sharing"
tags:
- bart-base-chinese
datasets:
- lccc
- kd_conv
---
# dialogue-bart-base-chinese
This is a seq2seq model fine-tuned on several Chinese dialogue datasets, from bart-base-chinese.
# Spaces
Now you can experience our model on HuggingFace Spaces [HIT-TMG/dialogue-bart-large-chinese](https://huggingface.co/spaces/HIT-TMG/dialogue-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-base-chinese")
model = BartForConditionalGeneration.from_pretrained("HIT-TMG/dialogue-bart-base-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, skip_special_tokens=True))
```
# Contact
If you encounter any issue, feel free to contact us via the email: <u>yanshekwoo@foxmail.com</u> |