metadata
language:
- zh
tags:
- pytorch
- zh
- Conversational
hfl/chinese-roberta-wwm-ext first pre-trained on CMNLI and OCNLI and then fine-tuned on the CDConv dataset. It supports 2-class classification for 2-turn dialogue contradiction detection. Usage example:
import torch
from transformers.models.bert import BertTokenizer, BertForSequenceClassification
tokenizer = BertTokenizer.from_pretrained('thu-coai/roberta-base-cdconv')
model = BertForSequenceClassification.from_pretrained('thu-coai/roberta-base-cdconv')
model.eval()
turn1 = [
"嗯嗯，你喜欢钓鱼吗？", # user
"喜欢啊，钓鱼很好玩的", # bot
]
turn2 = [
"你喜欢钓鱼吗？", # user
"不喜欢，我喜欢看别人钓鱼", # bot, we want to identify whether this utterance makes a contradiction
] # turn1 and turn2 are not required to be two consecutive turns
text1 = "[SEP]".join(turn1 + turn2[:1])
text2 = turn2[1]
model_input = tokenizer(text1, text2, return_tensors='pt', return_token_type_ids=True, return_attention_mask=True)
model_output = model(**model_input, return_dict=False)
prediction = torch.argmax(model_output[0].cpu(), dim=-1)[0].item()
print(prediction) # output 1. 0 for non-contradiction, 1 for contradiction
This fine-tuned model obtains 75.7 accuracy and 72.3 macro-F1 on the test set.
Please kindly cite the original paper if you use this model.
@inproceedings{zheng-etal-2022-cdconv,
title={Towards Emotional Support Dialog Systems},
author={Zheng, Chujie and
Zhou, Jinfeng and
Zheng, Yinhe and
Peng, Libiao and
Guo, Zhen and
Wu, Wenquan and
Niu, Zhengyu and
Wu, Hua and
Huang, Minlie},
booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
year={2022}
}