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neulab/PangeaBench-xgqa

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Hugging Face2024-10-31 更新2025-04-12 收录
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https://hf-mirror.com/datasets/neulab/PangeaBench-xgqa
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--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: full_answer dtype: string - name: image_id dtype: string - name: image dtype: image splits: - name: bn num_bytes: 498517814 num_examples: 9666 - name: de num_bytes: 498108367 num_examples: 9666 - name: en num_bytes: 498078827 num_examples: 9666 - name: id num_bytes: 498180441 num_examples: 9666 - name: ko num_bytes: 498157980 num_examples: 9666 - name: pt num_bytes: 498078408 num_examples: 9666 - name: ru num_bytes: 498298164 num_examples: 9666 - name: zh num_bytes: 498005624 num_examples: 9666 download_size: 2692912777 dataset_size: 3985425625 configs: - config_name: default data_files: - split: bn path: data/bn-* - split: de path: data/de-* - split: en path: data/en-* - split: id path: data/id-* - split: ko path: data/ko-* - split: pt path: data/pt-* - split: ru path: data/ru-* - split: zh path: data/zh-* license: cc-by-4.0 task_categories: - visual-question-answering language: - bn - de - en - id - ko - pt - ru - zh pretty_name: xgqa size_categories: - 10K<n<100K --- # xGQA ### This is a clone of the `few_shot-test` split of the xGQA dataset Please find the original repository here: https://github.com/adapter-hub/xGQA If you use this dataset, please cite the original authors: ```bibtex @inproceedings{pfeiffer-etal-2021-xGQA, title={{xGQA: Cross-Lingual Visual Question Answering}}, author={ Jonas Pfeiffer and Gregor Geigle and Aishwarya Kamath and Jan-Martin O. Steitz and Stefan Roth and Ivan Vuli{\'{c}} and Iryna Gurevych}, booktitle = "Findings of the Association for Computational Linguistics: ACL 2022", month = May, year = "2022", url = "https://arxiv.org/pdf/2109.06082.pdf", publisher = "Association for Computational Linguistics", } ```

数据集信息: 特征字段: - 名称:问题(question),数据类型:字符串 - 名称:答案(answer),数据类型:字符串 - 名称:完整答案(full_answer),数据类型:字符串 - 名称:图像ID(image_id),数据类型:字符串 - 名称:图像(image),数据类型:图像 拆分集: - 拆分名称:孟加拉语(bn),数据字节数:498517814,样本数量:9666 - 拆分名称:德语(de),数据字节数:498108367,样本数量:9666 - 拆分名称:英语(en),数据字节数:498078827,样本数量:9666 - 拆分名称:印尼语(id),数据字节数:498180441,样本数量:9666 - 拆分名称:韩语(ko),数据字节数:498157980,样本数量:9666 - 拆分名称:葡萄牙语(pt),数据字节数:498078408,样本数量:9666 - 拆分名称:俄语(ru),数据字节数:498298164,样本数量:9666 - 拆分名称:中文(zh),数据字节数:498005624,样本数量:9666 下载大小:2692912777 数据集总大小:3985425625 配置项: - 配置名称:默认(default),数据文件: - 拆分集:bn,路径:data/bn-* - 拆分集:de,路径:data/de-* - 拆分集:en,路径:data/en-* - 拆分集:id,路径:data/id-* - 拆分集:ko,路径:data/ko-* - 拆分集:pt,路径:data/pt-* - 拆分集:ru,路径:data/ru-* - 拆分集:zh,路径:data/zh-* 许可协议:CC BY 4.0 任务类别:视觉问答(visual-question-answering) 语言: - 孟加拉语(bn) - 德语(de) - 英语(en) - 印尼语(id) - 韩语(ko) - 葡萄牙语(pt) - 俄语(ru) - 中文(zh) 友好名称:xGQA 规模类别:1万 < 样本数 < 10万 # xGQA ### 本数据集为xGQA数据集`少样本测试(few_shot-test)`拆分的复刻版本。 原始仓库地址:https://github.com/adapter-hub/xGQA 若您使用本数据集,请引用原作者的研究: bibtex @inproceedings{pfeiffer-etal-2021-xGQA, title={{xGQA:跨语言视觉问答(xGQA: Cross-Lingual Visual Question Answering)}}, author={ Jonas Pfeiffer and Gregor Geigle and Aishwarya Kamath and Jan-Martin O. Steitz and Stefan Roth and Ivan Vulić and Iryna Gurevych}, booktitle = "《计算语言学协会研究发现:ACL 2022》", month = "5月", year = "2022", url = "https://arxiv.org/pdf/2109.06082.pdf", publisher = "计算语言学协会", }
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