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--- |
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language: |
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- zh |
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tags: |
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- pytorch |
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- zh |
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- Conversational |
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--- |
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[hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) first pre-trained on CMNLI and OCNLI and then fine-tuned on the [CDConv dataset](https://github.com/thu-coai/cdconv). It supports 2-class classification for 2-turn dialogue contradiction detection. Usage example: |
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```python |
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import torch |
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from transformers.models.bert import BertTokenizer, BertForSequenceClassification |
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tokenizer = BertTokenizer.from_pretrained('thu-coai/roberta-base-cdconv') |
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model = BertForSequenceClassification.from_pretrained('thu-coai/roberta-base-cdconv') |
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model.eval() |
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turn1 = [ |
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"嗯嗯，你喜欢钓鱼吗？", # user |
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"喜欢啊，钓鱼很好玩的", # bot |
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] |
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turn2 = [ |
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"你喜欢钓鱼吗？", # user |
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"不喜欢，我喜欢看别人钓鱼", # bot, we want to identify whether this utterance makes a contradiction |
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] # turn1 and turn2 are not required to be two consecutive turns |
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text1 = "[SEP]".join(turn1 + turn2[:1]) |
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text2 = turn2[1] |
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model_input = tokenizer(text1, text2, return_tensors='pt', return_token_type_ids=True, return_attention_mask=True) |
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model_output = model(**model_input, return_dict=False) |
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prediction = torch.argmax(model_output[0].cpu(), dim=-1)[0].item() |
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print(prediction) # output 1. 0 for non-contradiction, 1 for contradiction |
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``` |
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This fine-tuned model obtains 75.7 accuracy and 72.3 macro-F1 on the test set. |
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Please kindly cite the [original paper](https://arxiv.org/abs/2210.08511) if you use this model. |
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```bib |
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@inproceedings{zheng-etal-2022-cdconv, |
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title={Towards Emotional Support Dialog Systems}, |
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author={Zheng, Chujie and |
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Zhou, Jinfeng and |
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Zheng, Yinhe and |
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Peng, Libiao and |
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Guo, Zhen and |
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Wu, Wenquan and |
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Niu, Zhengyu and |
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Wu, Hua and |
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Huang, Minlie}, |
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booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing}, |
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year={2022} |
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} |
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``` |
