fairnlp/holistic-bias

--- configs: - config_name: noun_phrases data_files: - split: test path: nouns.csv - config_name: sentences data_files: - split: test path: sentences.csv license: cc-by-sa-4.0 language: en --- # Usage When downloading, specify which files you want to download and set the split to `train` (required by `datasets`). ```python from datasets import load_dataset nouns = load_dataset("fairnlp/holistic-bias", data_files=["nouns.csv"], split="train") sentences = load_dataset("fairnlp/holistic-bias", data_files=["sentences.csv"], split="train") ``` # Dataset Card for Holistic Bias This dataset contains the source data of the Holistic Bias dataset as described [by Smith et. al. (2022)](https://arxiv.org/abs/2205.09209). The dataset contains noun phrases and sentences used to measure the likelihood bias of various models. The original dataset is released on [GitHub](https://github.com/facebookresearch/ResponsibleNLP/tree/main/holistic_bias). Disclaimer: this re-release of the dataset is not associated with the original authors. The dataset is released under the [CC-BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/) license. ## Dataset Details The data is generated from the [official generation script](https://github.com/facebookresearch/ResponsibleNLP/blob/main/holistic_bias/generate_sentences.py). The data is the v1.0 data from the original paper. For details on the methodology, please refer to the original paper. This dataset is contributed to Hugging Face as part of the [FairNLP `fairscore` library](https://github.com/FairNLP/fairscore/). ### Dataset Sources - **Paper:** https://arxiv.org/pdf/2205.09209.pdf **BibTeX:** ```bibtex @inproceedings{smith2022m, title={“I’m sorry to hear that”: Finding New Biases in Language Models with a Holistic Descriptor Dataset}, author={Smith, Eric Michael and Hall, Melissa and Kambadur, Melanie and Presani, Eleonora and Williams, Adina}, booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing}, pages={9180--9211}, year={2022} } ```--- 配置项： - 配置名称：名词短语（noun_phrases） 数据文件： - 数据拆分：测试集（test） 文件路径：nouns.csv - 配置名称：句子（sentences） 数据文件： - 数据拆分：测试集（test） 文件路径：sentences.csv 许可证：CC-BY-SA-4.0 语言：英语 --- # 使用方法 下载数据集时，请指定所需下载的文件，并将数据拆分设置为`train`（这是`datasets`库的强制要求）。 python from datasets import load_dataset nouns = load_dataset("fairnlp/holistic-bias", data_files=["nouns.csv"], split="train") sentences = load_dataset("fairnlp/holistic-bias", data_files=["sentences.csv"], split="train") # 整体偏差（Holistic Bias）数据集卡片 本数据集包含Smith等人（2022年）提出的整体偏差（Holistic Bias）数据集的源数据，相关细节可参阅[Smith et. al. (2022)](https://arxiv.org/abs/2205.09209)。该数据集收录了用于衡量各类模型似然偏差的名词短语与语句，原始数据集已发布于[GitHub](https://github.com/facebookresearch/ResponsibleNLP/tree/main/holistic_bias)。 免责声明：本数据集的本次重发布与原始作者无关联，数据集采用[CC-BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/)许可协议进行发布。 ## 数据集详情 本数据集的数据由[官方生成脚本](https://github.com/facebookresearch/ResponsibleNLP/blob/main/holistic_bias/generate_sentences.py)生成，采用原始论文中的v1.0版本数据。 如需了解方法学细节，请参阅原始论文。本数据集由FairNLP团队作为[FairNLP `fairscore`库](https://github.com/FairNLP/fairscore/)的一部分提交至Hugging Face平台。 ### 数据集来源 - **论文链接**：https://arxiv.org/pdf/2205.09209.pdf **BibTeX引用格式：** bibtex @inproceedings{smith2022m, title={"I’m sorry to hear that": Finding New Biases in Language Models with a Holistic Descriptor Dataset}, author={Smith, Eric Michael and Hall, Melissa and Kambadur, Melanie and Presani, Eleonora and Williams, Adina}, booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing}, pages={9180--9211}, year={2022} }

原始信息汇总

数据集卡片摘要

数据集概述

• 名称: Holistic Bias
• 描述: 该数据集包含名词短语和句子，用于测量各种模型的偏见倾向。数据集来源于Smith等人的研究（2022年）。
• 许可: CC-BY-SA-4.0
• 语言: 英语

数据集详情

• 数据来源: 数据生成自官方生成脚本，版本为v1.0。
• 数据文件:
  • 名词短语: nouns.csv
  • 句子: sentences.csv

参考文献

• 论文: “I’m sorry to hear that”: Finding New Biases in Language Models with a Holistic Descriptor Dataset
• 作者: Smith, Eric Michael 等
• 会议: 2022 Conference on Empirical Methods in Natural Language Processing

搜集汇总

背景与挑战

背景概述

该数据集是一个用于检测语言模型偏见的文本数据集，包含大量名词短语和句子，适用于自然语言处理领域的偏见研究。数据集基于CC-BY-SA-4.0许可证发布，来源于相关学术论文的研究成果。