Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
open-domain-qa
Languages:
English
Size:
10K - 100K
License:
seirasto
commited on
Commit
•
efe2c92
1
Parent(s):
2122f7b
Add long answer candidates to natural questions dataset (#4368)
Browse files* add long answer candidates to natural questions
* formatting add long answer candidates to natural questions
* update nq readme and json
* Update README.md
add extra space to long_answer_candidates features
* added additional missing fields
* fixes to field an instance
* Fix data instance in cataset card
Fix:
- Rename "example_id" to "id"
- Set value of "id" as str
- Move "question" below "document"
- Move "document">"title" above "document">"url"
- Set value of "document">"title" as str
- Rename "document">"document_tokens" to "document">"tokens"
- Rename "Token" to "token"
- Add missing closing `}` to value of "document"
- Add missing "id" inside each "annotations"
- Add missing "text" inside each "short_answer"
- Set value of "yes_no_answer" to corresponding int
* Update datasets/natural_questions/natural_questions.py
Co-authored-by: Albert Villanova del Moral <8515462+albertvillanova@users.noreply.github.com>
* Update datasets/natural_questions/natural_questions.py
Co-authored-by: Albert Villanova del Moral <8515462+albertvillanova@users.noreply.github.com>
* Update datasets/natural_questions/natural_questions.py
Co-authored-by: Albert Villanova del Moral <8515462+albertvillanova@users.noreply.github.com>
* Update datasets/natural_questions/natural_questions.py
Co-authored-by: Albert Villanova del Moral <8515462+albertvillanova@users.noreply.github.com>
Co-authored-by: Albert Villanova del Moral <8515462+albertvillanova@users.noreply.github.com>
Commit from https://github.com/huggingface/datasets/commit/f5847a304aa1b38b3a3c54a8318b4df60f1299bc
- README.md +78 -18
- dataset_infos.json +1 -1
- natural_questions.py +26 -3
README.md
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@@ -50,8 +50,8 @@ paperswithcode_id: natural-questions
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## Dataset Description
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- **Homepage:** [https://ai.google.com/research/NaturalQuestions/dataset](https://ai.google.com/research/NaturalQuestions/dataset)
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- **Repository:** [
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- **Paper:** [
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- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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- **Size of downloaded dataset files:** 42981.34 MB
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- **Size of the generated dataset:** 95175.86 MB
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### Supported Tasks and Leaderboards
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[
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### Languages
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## Dataset Structure
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### Data Instances
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#### default
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-
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- **Size of downloaded dataset files:** 42981.34 MB
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- **Size of the generated dataset:** 95175.86 MB
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- **Total amount of disk used:** 138157.19 MB
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An example of 'train' looks as follows.
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```
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-
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```
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### Data Fields
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#### default
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- `id`: a `string` feature.
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- `
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- `
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- `
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- `start_token`: a `int64` feature.
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- `end_token`: a `int64` feature.
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- `start_byte`: a `int64` feature.
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- `end_byte`: a `int64` feature.
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- `short_answers`: a dictionary feature containing:
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- `start_token`: a `int64` feature.
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- `end_token`: a `int64` feature.
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## Dataset Description
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- **Homepage:** [https://ai.google.com/research/NaturalQuestions/dataset](https://ai.google.com/research/NaturalQuestions/dataset)
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- **Repository:** [https://github.com/google-research-datasets/natural-questions](https://github.com/google-research-datasets/natural-questions)
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- **Paper:** [https://research.google/pubs/pub47761/](https://research.google/pubs/pub47761/)
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- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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- **Size of downloaded dataset files:** 42981.34 MB
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- **Size of the generated dataset:** 95175.86 MB
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### Supported Tasks and Leaderboards
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[https://ai.google.com/research/NaturalQuestions](https://ai.google.com/research/NaturalQuestions)
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### Languages
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en
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## Dataset Structure
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### Data Instances
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- **Size of downloaded dataset files:** 42981.34 MB
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- **Size of the generated dataset:** 95175.86 MB
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- **Total amount of disk used:** 138157.19 MB
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An example of 'train' looks as follows. This is a toy example.
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```
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{
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"id": "797803103760793766",
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"document": {
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"title": "Google",
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"url": "http://www.wikipedia.org/Google",
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"html": "<html><body><h1>Google Inc.</h1><p>Google was founded in 1998 By:<ul><li>Larry</li><li>Sergey</li></ul></p></body></html>",
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"tokens":[
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{"token": "<h1>", "start_byte": 12, "end_byte": 16, "is_html": True},
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{"token": "Google", "start_byte": 16, "end_byte": 22, "is_html": False},
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{"token": "inc", "start_byte": 23, "end_byte": 26, "is_html": False},
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{"token": ".", "start_byte": 26, "end_byte": 27, "is_html": False},
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{"token": "</h1>", "start_byte": 27, "end_byte": 32, "is_html": True},
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{"token": "<p>", "start_byte": 32, "end_byte": 35, "is_html": True},
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{"token": "Google", "start_byte": 35, "end_byte": 41, "is_html": False},
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{"token": "was", "start_byte": 42, "end_byte": 45, "is_html": False},
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{"token": "founded", "start_byte": 46, "end_byte": 53, "is_html": False},
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{"token": "in", "start_byte": 54, "end_byte": 56, "is_html": False},
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{"token": "1998", "start_byte": 57, "end_byte": 61, "is_html": False},
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{"token": "by", "start_byte": 62, "end_byte": 64, "is_html": False},
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{"token": ":", "start_byte": 64, "end_byte": 65, "is_html": False},
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{"token": "<ul>", "start_byte": 65, "end_byte": 69, "is_html": True},
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{"token": "<li>", "start_byte": 69, "end_byte": 73, "is_html": True},
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{"token": "Larry", "start_byte": 73, "end_byte": 78, "is_html": False},
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{"token": "</li>", "start_byte": 78, "end_byte": 83, "is_html": True},
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{"token": "<li>", "start_byte": 83, "end_byte": 87, "is_html": True},
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{"token": "Sergey", "start_byte": 87, "end_byte": 92, "is_html": False},
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{"token": "</li>", "start_byte": 92, "end_byte": 97, "is_html": True},
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{"token": "</ul>", "start_byte": 97, "end_byte": 102, "is_html": True},
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{"token": "</p>", "start_byte": 102, "end_byte": 106, "is_html": True}
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],
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},
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"question" :{
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"text": "who founded google",
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"tokens": ["who", "founded", "google"]
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},
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"long_answer_candidates": [
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{"start_byte": 32, "end_byte": 106, "start_token": 5, "end_token": 22, "top_level": True},
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{"start_byte": 65, "end_byte": 102, "start_token": 13, "end_token": 21, "top_level": False},
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{"start_byte": 69, "end_byte": 83, "start_token": 14, "end_token": 17, "top_level": False},
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{"start_byte": 83, "end_byte": 92, "start_token": 17, "end_token": 20 , "top_level": False}
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],
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"annotations": [{
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"id": "6782080525527814293",
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"long_answer": {"start_byte": 32, "end_byte": 106, "start_token": 5, "end_token": 22, "candidate_index": 0},
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"short_answers": [
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{"start_byte": 73, "end_byte": 78, "start_token": 15, "end_token": 16, "text": "Larry"},
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{"start_byte": 87, "end_byte": 92, "start_token": 18, "end_token": 19, "text": "Sergey"}
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],
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"yes_no_answer": -1
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}]
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}
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```
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### Data Fields
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#### default
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- `id`: a `string` feature.
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- `document` a dictionary feature containing:
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- `title`: a `string` feature.
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- `url`: a `string` feature.
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- `html`: a `string` feature.
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- `tokens`: a dictionary feature containing:
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- `token`: a `string` feature.
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- `is_html`: a `bool` feature.
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- `start_byte`: a `int64` feature.
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- `end_byte`: a `int64` feature.
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- `question`: a dictionary feature containing:
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- `text`: a `string` feature.
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- `tokens`: a `list` of `string` features.
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- `long_answer_candidates`: a dictionary feature containing:
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- `start_token`: a `int64` feature.
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- `end_token`: a `int64` feature.
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- `start_byte`: a `int64` feature.
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- `end_byte`: a `int64` feature.
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- `top_level`: a `bool` feature.
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- `annotations`: a dictionary feature containing:
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- `id`: a `string` feature.
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- `long_answers`: a dictionary feature containing:
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- `start_token`: a `int64` feature.
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- `end_token`: a `int64` feature.
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- `start_byte`: a `int64` feature.
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- `end_byte`: a `int64` feature.
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- `candidate_index`: a `int64` feature.
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- `short_answers`: a dictionary feature containing:
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- `start_token`: a `int64` feature.
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- `end_token`: a `int64` feature.
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dataset_infos.json
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{"default": {"description": "\nThe NQ corpus contains questions from real users, and it requires QA systems to\nread and comprehend an entire Wikipedia article that may or may not contain the\nanswer to the question. The inclusion of real user questions, and the\nrequirement that solutions should read an entire page to find the answer, cause\nNQ to be a more realistic and challenging task than prior QA datasets.\n", "citation": "\n@article{47761,\ntitle\t= {Natural Questions: a Benchmark for Question Answering Research},\nauthor\t= {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le and Slav Petrov},\nyear\t= {2019},\njournal\t= {Transactions of the Association of Computational Linguistics}\n}\n", "homepage": "https://ai.google.com/research/NaturalQuestions/dataset", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "document": {"title": {"dtype": "string", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}, "html": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"token": {"dtype": "string", "id": null, "_type": "Value"}, "is_html": {"dtype": "bool", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "question": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "annotations": {"feature": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "long_answer": {"start_token": {"dtype": "int64", "id": null, "_type": "Value"}, "end_token": {"dtype": "int64", "id": null, "_type": "Value"}, "start_byte": {"dtype": "int64", "id": null, "_type": "Value"}, "end_byte": {"dtype": "int64", "id": null, "_type": "Value"}}, "short_answers": {"feature": {"start_token": {"dtype": "int64", "id": null, "_type": "Value"}, "end_token": {"dtype": "int64", "id": null, "_type": "Value"}, "start_byte": {"dtype": "int64", "id": null, "_type": "Value"}, "end_byte": {"dtype": "int64", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "yes_no_answer": {"num_classes": 2, "names": ["NO", "YES"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "natural_questions", "config_name": "default", "version": {"version_str": "0.0.2", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 0, "patch": 2}, "splits": {"train": {"name": "train", "num_bytes": 97445142568, "num_examples": 307373, "dataset_name": "natural_questions"}, "validation": {"name": "validation", "num_bytes": 2353975312, "num_examples": 7830, "dataset_name": "natural_questions"}}, "download_checksums": {"https://storage.googleapis.com/natural_questions/v1.0/train/nq-train-00.jsonl.gz": {"num_bytes": 858728609, "checksum": "fb63ed2a5af2921898d566a4e8e514ed17bd079735f5a37f9b0c5e83ce087106"}, "https://storage.googleapis.com/natural_questions/v1.0/train/nq-train-01.jsonl.gz": {"num_bytes": 891498165, "checksum": "bbccdbc261ced6ee6351ede78c8be5af43d1024c72a60070ea658767d4c3023a"}, "https://storage.googleapis.com/natural_questions/v1.0/train/nq-train-02.jsonl.gz": {"num_bytes": 885374316, "checksum": "923afd3c645b0bd887f7b6a43c03889936226708ec7a66d83e5e5fa9cee98f4e"}, "https://storage.googleapis.com/natural_questions/v1.0/train/nq-train-03.jsonl.gz": {"num_bytes": 885313666, "checksum": "272b2fcdc37cf23ab4bcdf831a84e3b755da066ad4727cdded57a383a18f45de"}, "https://storage.googleapis.com/natural_questions/v1.0/train/nq-train-04.jsonl.gz": {"num_bytes": 890873425, "checksum": "8a9eb2dcf818ab7a44c4fa4b73112547e7f250ec85bdf83d2a3f32542fc3e8c2"}, "https://storage.googleapis.com/natural_questions/v1.0/train/nq-train-05.jsonl.gz": {"num_bytes": 873023109, "checksum": "2566560a3ad89300552385c3aba0cb51f9968083f01f04c494623542619cdaca"}, "https://storage.googleapis.com/natural_questions/v1.0/train/nq-train-06.jsonl.gz": {"num_bytes": 866509301, "checksum": "8ae5491a1d86fea5025e9ec27fed574fe5886fb36a7b3567ab0dba498603728d"}, "https://storage.googleapis.com/natural_questions/v1.0/train/nq-train-07.jsonl.gz": {"num_bytes": 838940867, "checksum": "7d1ee955d5a8dee1dc024e7b6a278314c85514f046d40d56ad5f1c2bb1fd794a"}, 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"https://storage.googleapis.com/natural_questions/v1.0/dev/nq-dev-01.jsonl.gz": {"num_bytes": 200209706, "checksum": "9cebaa5eb69cf4ce067079370456b2939d4154a17da88faf73844d8c418cfb9e"}, "https://storage.googleapis.com/natural_questions/v1.0/dev/nq-dev-02.jsonl.gz": {"num_bytes": 210446574, "checksum": "7b82aa74a35025ed91f514ad21e05c4a66cdec56ac1f6b77767a578156ff3bfc"}, "https://storage.googleapis.com/natural_questions/v1.0/dev/nq-dev-03.jsonl.gz": {"num_bytes": 216859801, "checksum": "c7d45bb464bda3da7788c985b07def313ab5bed69bcc258acbe6f0918050bf6e"}, "https://storage.googleapis.com/natural_questions/v1.0/dev/nq-dev-04.jsonl.gz": {"num_bytes": 220929521, "checksum": "00969275e9fb6a5dcc7e20ec9589c23ac00de61c979c8b957f4180b5b9a3043a"}}, "download_size": 45069199013, "dataset_size": 99799117880, "size_in_bytes": 144868316893}}
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natural_questions.py
CHANGED
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@@ -51,7 +51,7 @@ _DOWNLOAD_URLS = {
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| 51 |
"validation": ["%s/dev/nq-dev-%02d.jsonl.gz" % (_BASE_DOWNLOAD_URL, i) for i in range(5)],
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}
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-
_VERSION = datasets.Version("0.0.
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| 57 |
class NaturalQuestions(datasets.BeamBasedBuilder):
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@@ -74,13 +74,27 @@ class NaturalQuestions(datasets.BeamBasedBuilder):
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| 74 |
"url": datasets.Value("string"),
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"html": datasets.Value("string"),
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| 76 |
"tokens": datasets.features.Sequence(
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| 77 |
-
{
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| 78 |
),
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| 79 |
},
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| 80 |
"question": {
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| 81 |
"text": datasets.Value("string"),
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| 82 |
"tokens": datasets.features.Sequence(datasets.Value("string")),
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| 83 |
},
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| 84 |
"annotations": datasets.features.Sequence(
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{
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| 86 |
"id": datasets.Value("string"),
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@@ -89,6 +103,7 @@ class NaturalQuestions(datasets.BeamBasedBuilder):
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| 89 |
"end_token": datasets.Value("int64"),
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| 90 |
"start_byte": datasets.Value("int64"),
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| 91 |
"end_byte": datasets.Value("int64"),
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| 92 |
},
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| 93 |
"short_answers": datasets.features.Sequence(
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| 94 |
{
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@@ -162,6 +177,7 @@ class NaturalQuestions(datasets.BeamBasedBuilder):
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| 162 |
"end_token": an_json["long_answer"]["end_token"],
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| 163 |
"start_byte": an_json["long_answer"]["start_byte"],
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| 164 |
"end_byte": an_json["long_answer"]["end_byte"],
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| 165 |
},
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| 166 |
"short_answers": [_parse_short_answer(ans) for ans in an_json["short_answers"]],
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| 167 |
"yes_no_answer": (-1 if an_json["yes_no_answer"] == "NONE" else an_json["yes_no_answer"]),
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@@ -179,10 +195,17 @@ class NaturalQuestions(datasets.BeamBasedBuilder):
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| 179 |
"url": ex_json["document_url"],
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| 180 |
"html": ex_json["document_html"],
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| 181 |
"tokens": [
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| 182 |
-
{
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| 183 |
],
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| 184 |
},
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| 185 |
"question": {"text": ex_json["question_text"], "tokens": ex_json["question_tokens"]},
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| 186 |
"annotations": [_parse_annotation(an_json) for an_json in ex_json["annotations"]],
|
| 187 |
},
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| 188 |
)
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| 51 |
"validation": ["%s/dev/nq-dev-%02d.jsonl.gz" % (_BASE_DOWNLOAD_URL, i) for i in range(5)],
|
| 52 |
}
|
| 53 |
|
| 54 |
+
_VERSION = datasets.Version("0.0.4")
|
| 55 |
|
| 56 |
|
| 57 |
class NaturalQuestions(datasets.BeamBasedBuilder):
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|
|
|
| 74 |
"url": datasets.Value("string"),
|
| 75 |
"html": datasets.Value("string"),
|
| 76 |
"tokens": datasets.features.Sequence(
|
| 77 |
+
{
|
| 78 |
+
"token": datasets.Value("string"),
|
| 79 |
+
"is_html": datasets.Value("bool"),
|
| 80 |
+
"start_byte": datasets.Value("int64"),
|
| 81 |
+
"end_byte": datasets.Value("int64"),
|
| 82 |
+
}
|
| 83 |
),
|
| 84 |
},
|
| 85 |
"question": {
|
| 86 |
"text": datasets.Value("string"),
|
| 87 |
"tokens": datasets.features.Sequence(datasets.Value("string")),
|
| 88 |
},
|
| 89 |
+
"long_answer_candidates": datasets.features.Sequence(
|
| 90 |
+
{
|
| 91 |
+
"start_token": datasets.Value("int64"),
|
| 92 |
+
"end_token": datasets.Value("int64"),
|
| 93 |
+
"start_byte": datasets.Value("int64"),
|
| 94 |
+
"end_byte": datasets.Value("int64"),
|
| 95 |
+
"top_level": datasets.Value("bool"),
|
| 96 |
+
}
|
| 97 |
+
),
|
| 98 |
"annotations": datasets.features.Sequence(
|
| 99 |
{
|
| 100 |
"id": datasets.Value("string"),
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|
|
|
| 103 |
"end_token": datasets.Value("int64"),
|
| 104 |
"start_byte": datasets.Value("int64"),
|
| 105 |
"end_byte": datasets.Value("int64"),
|
| 106 |
+
"candidate_index": datasets.Value("int64"),
|
| 107 |
},
|
| 108 |
"short_answers": datasets.features.Sequence(
|
| 109 |
{
|
|
|
|
| 177 |
"end_token": an_json["long_answer"]["end_token"],
|
| 178 |
"start_byte": an_json["long_answer"]["start_byte"],
|
| 179 |
"end_byte": an_json["long_answer"]["end_byte"],
|
| 180 |
+
"candidate_index": an_json["long_answer"]["candidate_index"],
|
| 181 |
},
|
| 182 |
"short_answers": [_parse_short_answer(ans) for ans in an_json["short_answers"]],
|
| 183 |
"yes_no_answer": (-1 if an_json["yes_no_answer"] == "NONE" else an_json["yes_no_answer"]),
|
|
|
|
| 195 |
"url": ex_json["document_url"],
|
| 196 |
"html": ex_json["document_html"],
|
| 197 |
"tokens": [
|
| 198 |
+
{
|
| 199 |
+
"token": t["token"],
|
| 200 |
+
"is_html": t["html_token"],
|
| 201 |
+
"start_byte": t["start_byte"],
|
| 202 |
+
"end_byte": t["end_byte"],
|
| 203 |
+
}
|
| 204 |
+
for t in ex_json["document_tokens"]
|
| 205 |
],
|
| 206 |
},
|
| 207 |
"question": {"text": ex_json["question_text"], "tokens": ex_json["question_tokens"]},
|
| 208 |
+
"long_answer_candidates": [lac_json for lac_json in ex_json["long_answer_candidates"]],
|
| 209 |
"annotations": [_parse_annotation(an_json) for an_json in ex_json["annotations"]],
|
| 210 |
},
|
| 211 |
)
|