- Description:
The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets.
Additional Documentation: Explore on Papers With Code
Homepage: https://ai.google.com/research/NaturalQuestions/dataset
Source code:
tfds.datasets.natural_questions.Builder
Versions:
0.0.2
: No release notes.0.1.0
(default): No release notes.
Download size:
41.97 GiB
Auto-cached (documentation): No
Splits:
Split | Examples |
---|---|
'train' |
307,373 |
'validation' |
7,830 |
Supervised keys (See
as_supervised
doc):None
Figure (tfds.show_examples): Not supported.
Citation:
@article{47761,
title = {Natural Questions: a Benchmark for Question Answering Research},
author = {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},
year = {2019},
journal = {Transactions of the Association of Computational Linguistics}
}
natural_questions/default (default config)
Config description: Default natural_questions config
Dataset size:
90.26 GiB
Feature structure:
FeaturesDict({
'annotations': Sequence({
'id': string,
'long_answer': FeaturesDict({
'end_byte': int64,
'end_token': int64,
'start_byte': int64,
'start_token': int64,
}),
'short_answers': Sequence({
'end_byte': int64,
'end_token': int64,
'start_byte': int64,
'start_token': int64,
'text': Text(shape=(), dtype=string),
}),
'yes_no_answer': ClassLabel(shape=(), dtype=int64, num_classes=2),
}),
'document': FeaturesDict({
'html': Text(shape=(), dtype=string),
'title': Text(shape=(), dtype=string),
'tokens': Sequence({
'is_html': bool,
'token': Text(shape=(), dtype=string),
}),
'url': Text(shape=(), dtype=string),
}),
'id': string,
'question': FeaturesDict({
'text': Text(shape=(), dtype=string),
'tokens': Sequence(string),
}),
})
- Feature documentation:
Feature | Class | Shape | Dtype | Description |
---|---|---|---|---|
FeaturesDict | ||||
annotations | Sequence | |||
annotations/id | Tensor | string | ||
annotations/long_answer | FeaturesDict | |||
annotations/long_answer/end_byte | Tensor | int64 | ||
annotations/long_answer/end_token | Tensor | int64 | ||
annotations/long_answer/start_byte | Tensor | int64 | ||
annotations/long_answer/start_token | Tensor | int64 | ||
annotations/short_answers | Sequence | |||
annotations/short_answers/end_byte | Tensor | int64 | ||
annotations/short_answers/end_token | Tensor | int64 | ||
annotations/short_answers/start_byte | Tensor | int64 | ||
annotations/short_answers/start_token | Tensor | int64 | ||
annotations/short_answers/text | Text | string | ||
annotations/yes_no_answer | ClassLabel | int64 | ||
document | FeaturesDict | |||
document/html | Text | string | ||
document/title | Text | string | ||
document/tokens | Sequence | |||
document/tokens/is_html | Tensor | bool | ||
document/tokens/token | Text | string | ||
document/url | Text | string | ||
id | Tensor | string | ||
question | FeaturesDict | |||
question/text | Text | string | ||
question/tokens | Sequence(Tensor) | (None,) | string |
- Examples (tfds.as_dataframe):
natural_questions/longt5
Config description: natural_questions preprocessed as in the longT5 benchmark
Dataset size:
8.91 GiB
Feature structure:
FeaturesDict({
'all_answers': Sequence(Text(shape=(), dtype=string)),
'answer': Text(shape=(), dtype=string),
'context': Text(shape=(), dtype=string),
'id': Text(shape=(), dtype=string),
'question': Text(shape=(), dtype=string),
'title': Text(shape=(), dtype=string),
})
- Feature documentation:
Feature | Class | Shape | Dtype | Description |
---|---|---|---|---|
FeaturesDict | ||||
all_answers | Sequence(Text) | (None,) | string | |
answer | Text | string | ||
context | Text | string | ||
id | Text | string | ||
question | Text | string | ||
title | Text | string |
- Examples (tfds.as_dataframe):