分隊質問生成
コレクションでコンテンツを整理
必要に応じて、コンテンツの保存と分類を行います。
「テキストからのニューラル質問生成: 予備研究」(Zhou et al, 2017) および「質問することの学習: 読解のためのニューラル質問生成」(Du et al, 2017) で説明されているデータ分割を使用した分隊データセットを使用した質問生成。
ホームページ: https://github.com/xinyadu/nqg @inproceedings{du-etal-2017-learning, title = "尋ねることを学ぶ: 読解のためのニューラル質問生成", author = "Du, Xinya and Shao, Junru and Cardie, Claire", booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2017", address = "バンクーバー、カナダ", publisher = "計算言語学協会", url = "https://aclanthology.org/P17-1123", doi = "10.18653/v1/P17-1123", ページ = "1342--1352", } ", 月 = 7 月, year = "2017", address = "バンクーバー、カナダ", publisher = "計算言語学協会", url = "https://aclanthology.org/P17-1123", doi = "10.18653/v1/P17-1123" 、ページ = "1342--1352"、} )
ソース コード: tfds.text.squad_question_generation.SquadQuestionGeneration
バージョン:
1.0.0
: パッセージ レベルのコンテキストを使用して、各分割で一意の SQuAD QAS ID を持つ初期ビルド (Zhou et al, 2017)。
2.0.0
: (Zhou et al, 2017) の元の分割に一致し、文レベルとパッセージ レベルの両方のコンテキストを許可し、(Zhou et al, 2017) からの回答を使用します。
3.0.0
(デフォルト): (Du et al, 2017) の分割も追加しました。
自動キャッシュ(ドキュメント): はい
監視されたキー( as_supervised
docを参照): ('context_passage', 'question')
図( tfds.show_examples ): サポートされていません。
引用:
@inproceedings{du-etal-2017-learning,
title = "Learning to Ask: Neural Question Generation for Reading Comprehension",
author = "Du, Xinya and Shao, Junru and Cardie, Claire",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1123",
doi = "10.18653/v1/P17-1123",
pages = "1342--1352",
}
@inproceedings{rajpurkar-etal-2016-squad,
title = "{SQ}u{AD}: 100,000+ Questions for Machine Comprehension of Text",
author = "Rajpurkar, Pranav and Zhang, Jian and Lopyrev, Konstantin and Liang, Percy",
booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2016",
address = "Austin, Texas",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D16-1264",
doi = "10.18653/v1/D16-1264",
pages = "2383--2392",
}
分隊質問生成/split_du (デフォルト設定)
スプリット | 例 |
---|
'test' | 11,877 |
'train' | 75,722 |
'validation' | 10,570 |
FeaturesDict({
'answer': Text(shape=(), dtype=string),
'context_passage': Text(shape=(), dtype=string),
'question': Text(shape=(), dtype=string),
})
特徴 | クラス | 形 | Dtype | 説明 |
---|
| 特徴辞書 | | | |
答え | 文章 | | ストリング | |
context_passage | 文章 | | ストリング | |
質問 | 文章 | | ストリング | |
分隊質問世代/split_zhou
スプリット | 例 |
---|
'test' | 8,964 |
'train' | 86,635 |
'validation' | 8,965 |
FeaturesDict({
'answer': Text(shape=(), dtype=string),
'context_passage': Text(shape=(), dtype=string),
'context_sentence': Text(shape=(), dtype=string),
'question': Text(shape=(), dtype=string),
})
特徴 | クラス | 形 | Dtype | 説明 |
---|
| 特徴辞書 | | | |
答え | 文章 | | ストリング | |
context_passage | 文章 | | ストリング | |
context_sentence | 文章 | | ストリング | |
質問 | 文章 | | ストリング | |
特に記載のない限り、このページのコンテンツはクリエイティブ・コモンズの表示 4.0 ライセンスにより使用許諾されます。コードサンプルは Apache 2.0 ライセンスにより使用許諾されます。詳しくは、Google Developers サイトのポリシーをご覧ください。Java は Oracle および関連会社の登録商標です。
最終更新日 2022-12-06 UTC。
[null,null,["最終更新日 2022-12-06 UTC。"],[],[],null,["# squad_question_generation\n\n- **Description**:\n\nQuestion generation using squad dataset using data splits described in 'Neural\nQuestion Generation from Text: A Preliminary Study' (Zhou et al, 2017) and\n'Learning to Ask: Neural Question Generation for Reading Comprehension' (Du et\nal, 2017).\n\n- **Homepage** : [https://github.com/xinyadu/nqg\n @inproceedings{du-etal-2017-learning, title = \"Learning to Ask: Neural\n Question Generation for Reading Comprehension\", author = \"Du, Xinya and\n Shao, Junru and Cardie, Claire\", booktitle = \"Proceedings of the 55th Annual\n Meeting of the Association for Computational Linguistics (Volume 1: Long\n Papers)\", month = jul, year = \"2017\", address = \"Vancouver, Canada\",\n publisher = \"Association for Computational Linguistics\", url =\n \"https://aclanthology.org/P17-1123\", doi = \"10.18653/v1/P17-1123\", pages =\n \"1342--1352\",\n }](https://github.com/xinyadu/nqg%20@inproceedings%7Bdu-etal-2017-learning,%20title%20=%20%22Learning%20to%20Ask:%20Neural%20Question%20Generation%20for%20Reading%20Comprehension%22,%20author%20=%20%22Du,%20Xinya%20and%20Shao,%20Junru%20and%20Cardie,%20Claire%22,%20booktitle%20=%20%22Proceedings%20of%20the%2055th%20Annual%20Meeting%20of%20the%20Association%20for%20Computational%20Linguistics%20(Volume%201:%20Long%20Papers)\",\n month = jul, year = \"2017\", address = \"Vancouver, Canada\", publisher =\n \"Association for Computational Linguistics\", url =\n \"https://aclanthology.org/P17-1123\", doi = \"10.18653/v1/P17-1123\", pages =\n \"1342--1352\", } )\n\n- **Source code** :\n [`tfds.text.squad_question_generation.SquadQuestionGeneration`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/text/squad_question_generation/squad_question_generation.py)\n\n- **Versions**:\n\n - `1.0.0`: Initial build with unique SQuAD QAS ids in each split, using\n passage-level context (Zhou et al, 2017).\n\n - `2.0.0`: Matches the original split of (Zhou et al, 2017), allows both\n sentence- and passage-level contexts, and uses answers from (Zhou et al,\n 2017).\n\n - **`3.0.0`** (default): Added the split of (Du et al, 2017) also.\n\n- **Auto-cached**\n ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):\n Yes\n\n- **Supervised keys** (See\n [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):\n `('context_passage', 'question')`\n\n- **Figure**\n ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):\n Not supported.\n\n- **Citation**:\n\n @inproceedings{du-etal-2017-learning,\n title = \"Learning to Ask: Neural Question Generation for Reading Comprehension\",\n author = \"Du, Xinya and Shao, Junru and Cardie, Claire\",\n booktitle = \"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n month = jul,\n year = \"2017\",\n address = \"Vancouver, Canada\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/P17-1123\",\n doi = \"10.18653/v1/P17-1123\",\n pages = \"1342--1352\",\n }\n\n @inproceedings{rajpurkar-etal-2016-squad,\n title = \"{SQ}u{AD}: 100,000+ Questions for Machine Comprehension of Text\",\n author = \"Rajpurkar, Pranav and Zhang, Jian and Lopyrev, Konstantin and Liang, Percy\",\n booktitle = \"Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing\",\n month = nov,\n year = \"2016\",\n address = \"Austin, Texas\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/D16-1264\",\n doi = \"10.18653/v1/D16-1264\",\n pages = \"2383--2392\",\n }\n\nsquad_question_generation/split_du (default config)\n---------------------------------------------------\n\n- **Config description**: Answer independent question generation from\n passage-level contexts (Du et al, 2017).\n\n- **Download size** : `62.83 MiB`\n\n- **Dataset size** : `84.67 MiB`\n\n- **Splits**:\n\n| Split | Examples |\n|----------------|----------|\n| `'test'` | 11,877 |\n| `'train'` | 75,722 |\n| `'validation'` | 10,570 |\n\n- **Feature structure**:\n\n FeaturesDict({\n 'answer': Text(shape=(), dtype=string),\n 'context_passage': Text(shape=(), dtype=string),\n 'question': Text(shape=(), dtype=string),\n })\n\n- **Feature documentation**:\n\n| Feature | Class | Shape | Dtype | Description |\n|-----------------|--------------|-------|--------|-------------|\n| | FeaturesDict | | | |\n| answer | Text | | string | |\n| context_passage | Text | | string | |\n| question | Text | | string | |\n\n- **Examples** ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples... \n\nsquad_question_generation/split_zhou\n------------------------------------\n\n- **Config description**: Answer-span dependent question generation from\n sentence- and passage-level contexts (Zhou et al, 2017).\n\n- **Download size** : `62.52 MiB`\n\n- **Dataset size** : `111.02 MiB`\n\n- **Splits**:\n\n| Split | Examples |\n|----------------|----------|\n| `'test'` | 8,964 |\n| `'train'` | 86,635 |\n| `'validation'` | 8,965 |\n\n- **Feature structure**:\n\n FeaturesDict({\n 'answer': Text(shape=(), dtype=string),\n 'context_passage': Text(shape=(), dtype=string),\n 'context_sentence': Text(shape=(), dtype=string),\n 'question': Text(shape=(), dtype=string),\n })\n\n- **Feature documentation**:\n\n| Feature | Class | Shape | Dtype | Description |\n|------------------|--------------|-------|--------|-------------|\n| | FeaturesDict | | | |\n| answer | Text | | string | |\n| context_passage | Text | | string | |\n| context_sentence | Text | | string | |\n| question | Text | | string | |\n\n- **Examples** ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples..."]]