Comments (1)
你好,三个句子type_ids: 0 0 0 0 1 1 0 0 0 0 不是BERT自带的,是我根据segment id的定义,以及预训练BERT只有两种segment id的现实设计的。
虽然用于三元组与BERT预训练的上下句有不一致之处,但输入很相似,BERT预训练仍然有助于三个句子间关系的推断,且预训练BERT已被成功应用于文本摘要等多句子任务。例如:
Liu, Y. and Lapata, M., 2019, November. Text Summarization with Pretrained Encoders. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 3721-3731).
由于是微调预训练BERT,实体词只参与微调,没有被mask。
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Related Issues (20)
- 是否支持中文 HOT 2
- GPU的使用 HOT 2
- Prediction Score for each time is different
- 论文中模型图
- hit@10在LP任务的testing阶段一直为0
- Using this with roberta-base
- gpu的使用
- UnicodeDecodeError: 'charmap' codec can't decode byte 0x90 in position 1519: character maps to <undefined>
- Time complexity of test triple ranking HOT 2
- Pre trained model
- 请问该文发表到正式期刊或会议了吗? HOT 2
- why umls dataset does not use long text? HOT 1
- 关于FB15K-237数据集文本句子的问题 HOT 2
- Run the model over a different kg
- 作者你好 请问下您的这篇论文现在发布在哪篇期刊上了? HOT 2
- 作者您好,我在您的实验的基础上将BERT模型替换为了DistilBERT模型,运行fb15k-237数据集,但是ACC为0,您知道是什么原因嘛
- 结果复现
- 三元组分类任务的数据集构建
- Why do the authors use long text in the link prediction task, whereas only node text is used in the others?
- 请问为什么我在本地运行时会报unicode error?
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