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thu-coai/
roberta-base-cdconv

Name: roberta-base-cdconv
Availability: InStock

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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:

python

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.

bib

@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}
}