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This is a seq2seq model pre-trained on several Chinese dialogue datasets, from bart-large-chinese. It's better to fine-tune it on downstream tasks for better performance.


Now you can experience our model on HuggingFace Spaces HIT-TMG/dialogue-bart-large-chinese .


We utilize 4 Chinese dialogue datasets from LUGE .

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> [SEP] 知识:<knowledge> [SEP]

Output: [CLS] <response> [SEP]


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, skip_special_tokens=True))


If you encounter any issue, feel free to contact us via the email: yanshekwoo@foxmail.com

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Datasets used to train HIT-TMG/dialogue-bart-large-chinese

Spaces using HIT-TMG/dialogue-bart-large-chinese 2