RoBERTa Large模型在Winogrande上的微调

该模型在Winogrande数据集（XL尺寸）上进行了微调，任务格式为序列分类，即原始的具有相应选项的句子对被分开、混排并独立进行分类。

模型描述

使用和限制

如何使用

训练数据

WinoGrande-XL 被重新格式化如下：

例如，

{
  "answer": "2",
  "option1": "plant",
  "option2": "urn",
  "sentence": "The plant took up too much room in the urn, because the _ was small."
}

变成

{
  "sentence1": "The plant took up too much room in the urn, because the ",
  "sentence2": "plant was small.",
  "label": false
}

和

{
  "sentence1": "The plant took up too much room in the urn, because the ",
  "sentence2": "urn was small.",
  "label": true
}

然后将这些句子对作为独立的示例处理。

BibTeX条目和引用信息

@article{sakaguchi2019winogrande,
    title={WinoGrande: An Adversarial Winograd Schema Challenge at Scale},
    author={Sakaguchi, Keisuke and Bras, Ronan Le and Bhagavatula, Chandra and Choi, Yejin},
    journal={arXiv preprint arXiv:1907.10641},
    year={2019}
}

@article{DBLP:journals/corr/abs-1907-11692,
  author    = {Yinhan Liu and
               Myle Ott and
               Naman Goyal and
               Jingfei Du and
               Mandar Joshi and
               Danqi Chen and
               Omer Levy and
               Mike Lewis and
               Luke Zettlemoyer and
               Veselin Stoyanov},
  title     = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
  journal   = {CoRR},
  volume    = {abs/1907.11692},
  year      = {2019},
  url       = {http://arxiv.org/abs/1907.11692},
  archivePrefix = {arXiv},
  eprint    = {1907.11692},
  timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}