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README.md

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----
-annotations_creators:
-- no-annotation
-language_creators:
-- crowdsourced
-language:
-- en
-license:
-- cc-by-sa-3.0
-multilinguality:
-- monolingual
-pretty_name: Natural Questions
-size_categories:
-- 100K<n<1M
-source_datasets:
-- original
-task_categories:
-- question-answering
-task_ids:
-- open-domain-qa
-paperswithcode_id: natural-questions
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: document
-    struct:
-    - name: title
-      dtype: string
-    - name: url
-      dtype: string
-    - name: html
-      dtype: string
-    - name: tokens
-      sequence:
-      - name: token
-        dtype: string
-      - name: is_html
-        dtype: bool
-  - name: question
-    struct:
-    - name: text
-      dtype: string
-    - name: tokens
-      sequence: string
-  - name: annotations
-    sequence:
-    - name: id
-      dtype: string
-    - name: long_answer
-      struct:
-      - name: start_token
-        dtype: int64
-      - name: end_token
-        dtype: int64
-      - name: start_byte
-        dtype: int64
-      - name: end_byte
-        dtype: int64
-    - name: short_answers
-      sequence:
-      - name: start_token
-        dtype: int64
-      - name: end_token
-        dtype: int64
-      - name: start_byte
-        dtype: int64
-      - name: end_byte
-        dtype: int64
-      - name: text
-        dtype: string
-    - name: yes_no_answer
-      dtype:
-        class_label:
-          names:
-            0: 'NO'
-            1: 'YES'
-    - name: long_answer_candidates
-      sequence:
-      - name: start_token
-        dtype: int64
-      - name: end_token
-        dtype: int64
-      - name: start_byte
-        dtype: int64
-      - name: end_byte
-        dtype: int64
-      - name: top_label
-        dtype: bool
-  splits:
-  - name: train
-    num_bytes: 97445142568
-    num_examples: 307373
-  - name: validation
-    num_bytes: 2353975312
-    num_examples: 7830
-  download_size: 45069199013
-  dataset_size: 99799117880
----
-
-# Dataset Card for Natural Questions
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** [https://ai.google.com/research/NaturalQuestions/dataset](https://ai.google.com/research/NaturalQuestions/dataset)
-- **Repository:** [https://github.com/google-research-datasets/natural-questions](https://github.com/google-research-datasets/natural-questions)
-- **Paper:** [https://research.google/pubs/pub47761/](https://research.google/pubs/pub47761/)
-- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Size of downloaded dataset files:** 42981.34 MB
-- **Size of the generated dataset:** 95175.86 MB
-- **Total amount of disk used:** 138157.19 MB
-
-### Dataset Summary
-
-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.
-
-### Supported Tasks and Leaderboards
-
-[https://ai.google.com/research/NaturalQuestions](https://ai.google.com/research/NaturalQuestions)
-
-### Languages
-
-en
-
-## Dataset Structure
-
-### Data Instances
-
-- **Size of downloaded dataset files:** 42981.34 MB
-- **Size of the generated dataset:** 95175.86 MB
-- **Total amount of disk used:** 138157.19 MB
-
-An example of 'train' looks as follows. This is a toy example.
-```
-{
-  "id": "797803103760793766",
-  "document": {
-    "title": "Google",
-    "url": "http://www.wikipedia.org/Google",
-    "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>",
-    "tokens":[
-      {"token": "<h1>", "start_byte": 12, "end_byte": 16, "is_html": True},
-      {"token": "Google", "start_byte": 16, "end_byte": 22, "is_html": False},
-      {"token": "inc", "start_byte": 23, "end_byte": 26, "is_html": False},
-      {"token": ".", "start_byte": 26, "end_byte": 27, "is_html": False},
-      {"token": "</h1>", "start_byte": 27, "end_byte": 32, "is_html": True},
-      {"token": "<p>", "start_byte": 32, "end_byte": 35, "is_html": True},
-      {"token": "Google", "start_byte": 35, "end_byte": 41, "is_html": False},
-      {"token": "was", "start_byte": 42, "end_byte": 45, "is_html": False},
-      {"token": "founded", "start_byte": 46, "end_byte": 53, "is_html": False},
-      {"token": "in", "start_byte": 54, "end_byte": 56, "is_html": False},
-      {"token": "1998", "start_byte": 57, "end_byte": 61, "is_html": False},
-      {"token": "by", "start_byte": 62, "end_byte": 64, "is_html": False},
-      {"token": ":", "start_byte": 64, "end_byte": 65, "is_html": False},
-      {"token": "<ul>", "start_byte": 65, "end_byte": 69, "is_html": True},
-      {"token": "<li>", "start_byte": 69, "end_byte": 73, "is_html": True},
-      {"token": "Larry", "start_byte": 73, "end_byte": 78, "is_html": False},
-      {"token": "</li>", "start_byte": 78, "end_byte": 83, "is_html": True},
-      {"token": "<li>", "start_byte": 83, "end_byte": 87, "is_html": True},
-      {"token": "Sergey", "start_byte": 87, "end_byte": 92, "is_html": False},
-      {"token": "</li>", "start_byte": 92, "end_byte": 97, "is_html": True},
-      {"token": "</ul>", "start_byte": 97, "end_byte": 102, "is_html": True},
-      {"token": "</p>", "start_byte": 102, "end_byte": 106, "is_html": True}
-    ],
-  },
-  "question" :{
-    "text": "who founded google",
-    "tokens": ["who", "founded", "google"]
-  },
-  "long_answer_candidates": [
-    {"start_byte": 32, "end_byte": 106, "start_token": 5, "end_token": 22, "top_level": True},
-    {"start_byte": 65, "end_byte": 102, "start_token": 13, "end_token": 21, "top_level": False},
-    {"start_byte": 69, "end_byte": 83, "start_token": 14, "end_token": 17, "top_level": False},
-    {"start_byte": 83, "end_byte": 92, "start_token": 17, "end_token": 20 , "top_level": False}
-  ],
-  "annotations": [{
-    "id": "6782080525527814293",
-    "long_answer": {"start_byte": 32, "end_byte": 106, "start_token": 5, "end_token": 22, "candidate_index": 0},
-    "short_answers": [
-      {"start_byte": 73, "end_byte": 78, "start_token": 15, "end_token": 16, "text": "Larry"},
-      {"start_byte": 87, "end_byte": 92, "start_token": 18, "end_token": 19, "text": "Sergey"}
-    ],
-    "yes_no_answer": -1
-  }]
-}
-```
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-#### default
-- `id`: a `string` feature.
-- `document` a dictionary feature containing:
-  - `title`: a `string` feature.
-  - `url`: a `string` feature.
-  - `html`: a `string` feature.
-  - `tokens`: a dictionary feature containing:
-    - `token`: a `string` feature.
-    - `is_html`: a `bool` feature.
-    - `start_byte`: a `int64` feature.
-    - `end_byte`: a `int64` feature.
-- `question`: a dictionary feature containing:
-  - `text`: a `string` feature.
-  - `tokens`: a `list` of `string` features.
-- `long_answer_candidates`: a dictionary feature containing:
-  - `start_token`: a `int64` feature.
-  - `end_token`: a `int64` feature.
-  - `start_byte`: a `int64` feature.
-  - `end_byte`: a `int64` feature.
-  - `top_level`: a `bool` feature.
-- `annotations`: a dictionary feature containing:
-  - `id`: a `string` feature.
-  - `long_answers`: a dictionary feature containing:
-    - `start_token`: a `int64` feature.
-    - `end_token`: a `int64` feature.
-    - `start_byte`: a `int64` feature.
-    - `end_byte`: a `int64` feature.
-    - `candidate_index`: a `int64` feature.
-  - `short_answers`: a dictionary feature containing:
-    - `start_token`: a `int64` feature.
-    - `end_token`: a `int64` feature.
-    - `start_byte`: a `int64` feature.
-    - `end_byte`: a `int64` feature.
-    - `text`: a `string` feature.
-  - `yes_no_answer`: a classification label, with possible values including `NO` (0), `YES` (1).
-
-### Data Splits
-
-| name    |  train | validation |
-|---------|-------:|-----------:|
-| default | 307373 |       7830 |
-| dev     |    N/A |       7830 |
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the source language producers?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the annotators?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Personal and Sensitive Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Discussion of Biases
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Other Known Limitations
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Licensing Information
-
-[Creative Commons Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/).
-
-### Citation Information
-
-```
-
-@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}
-}
-
-```
-
-
-### Contributions
-
-Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

dataset_infos.json

@@ -1 +0,0 @@
-{"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", 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natural_questions.py

@@ -1,222 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Lint as: python3
-"""Natural Questions: A Benchmark for Question Answering Research."""
-
-
-import html
-import json
-import re
-
-import datasets
-
-
-_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}
-}
-"""
-
-_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.
-"""
-
-_URL = "https://ai.google.com/research/NaturalQuestions/dataset"
-
-_BASE_DOWNLOAD_URL = "https://storage.googleapis.com/natural_questions/v1.0"
-_DOWNLOAD_URLS = {
-    "train": ["%s/train/nq-train-%02d.jsonl.gz" % (_BASE_DOWNLOAD_URL, i) for i in range(50)],
-    "validation": ["%s/dev/nq-dev-%02d.jsonl.gz" % (_BASE_DOWNLOAD_URL, i) for i in range(5)],
-}
-
-_VERSION = datasets.Version("0.0.4")
-
-
-class NaturalQuestions(datasets.BeamBasedBuilder):
-    """Natural Questions: A Benchmark for Question Answering Research."""
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="default", version=_VERSION),
-        datasets.BuilderConfig(name="dev", version=_VERSION, description="Only dev split"),
-    ]
-    DEFAULT_CONFIG_NAME = "default"
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "document": {
-                        "title": datasets.Value("string"),
-                        "url": datasets.Value("string"),
-                        "html": datasets.Value("string"),
-                        "tokens": datasets.features.Sequence(
-                            {
-                                "token": datasets.Value("string"),
-                                "is_html": datasets.Value("bool"),
-                                "start_byte": datasets.Value("int64"),
-                                "end_byte": datasets.Value("int64"),
-                            }
-                        ),
-                    },
-                    "question": {
-                        "text": datasets.Value("string"),
-                        "tokens": datasets.features.Sequence(datasets.Value("string")),
-                    },
-                    "long_answer_candidates": datasets.features.Sequence(
-                        {
-                            "start_token": datasets.Value("int64"),
-                            "end_token": datasets.Value("int64"),
-                            "start_byte": datasets.Value("int64"),
-                            "end_byte": datasets.Value("int64"),
-                            "top_level": datasets.Value("bool"),
-                        }
-                    ),
-                    "annotations": datasets.features.Sequence(
-                        {
-                            "id": datasets.Value("string"),
-                            "long_answer": {
-                                "start_token": datasets.Value("int64"),
-                                "end_token": datasets.Value("int64"),
-                                "start_byte": datasets.Value("int64"),
-                                "end_byte": datasets.Value("int64"),
-                                "candidate_index": datasets.Value("int64"),
-                            },
-                            "short_answers": datasets.features.Sequence(
-                                {
-                                    "start_token": datasets.Value("int64"),
-                                    "end_token": datasets.Value("int64"),
-                                    "start_byte": datasets.Value("int64"),
-                                    "end_byte": datasets.Value("int64"),
-                                    "text": datasets.Value("string"),
-                                }
-                            ),
-                            "yes_no_answer": datasets.features.ClassLabel(
-                                names=["NO", "YES"]
-                            ),  # Can also be -1 for NONE.
-                        }
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_URL,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager, pipeline):
-        """Returns SplitGenerators."""
-        urls = _DOWNLOAD_URLS
-        if self.config.name == "dev":
-            urls = {"validation": urls["validation"]}
-        files = dl_manager.download(urls)
-        if not pipeline.is_local():
-            files = dl_manager.ship_files_with_pipeline(files, pipeline)
-        return [
-            datasets.SplitGenerator(
-                name=split,
-                gen_kwargs={"filepaths": files[split]},
-            )
-            for split in [datasets.Split.TRAIN, datasets.Split.VALIDATION]
-            if split in files
-        ]
-
-    def _build_pcollection(self, pipeline, filepaths):
-        """Build PCollection of examples."""
-        try:
-            import apache_beam as beam
-        except ImportError as err:
-            raise ImportError(
-                "To be able to load natural_questions, you need to install apache_beam: 'pip install apache_beam'"
-            ) from err
-
-        def _parse_example(line):
-            """Parse a single json line and emit an example dict."""
-            ex_json = json.loads(line)
-            html_bytes = ex_json["document_html"].encode("utf-8")
-
-            def _parse_short_answer(short_ans):
-                """Extract text of short answer."""
-                ans_bytes = html_bytes[short_ans["start_byte"] : short_ans["end_byte"]]
-                # Remove non-breaking spaces.
-                ans_bytes = ans_bytes.replace(b"\xc2\xa0", b" ")
-                text = ans_bytes.decode("utf-8")
-                # Remove HTML markup.
-                text = re.sub("<([^>]*)>", "", html.unescape(text))
-                # Replace \xa0 characters with spaces.
-                return {
-                    "start_token": short_ans["start_token"],
-                    "end_token": short_ans["end_token"],
-                    "start_byte": short_ans["start_byte"],
-                    "end_byte": short_ans["end_byte"],
-                    "text": text,
-                }
-
-            def _parse_annotation(an_json):
-                return {
-                    # Convert to str since some IDs cannot be represented by datasets.Value('int64').
-                    "id": str(an_json["annotation_id"]),
-                    "long_answer": {
-                        "start_token": an_json["long_answer"]["start_token"],
-                        "end_token": an_json["long_answer"]["end_token"],
-                        "start_byte": an_json["long_answer"]["start_byte"],
-                        "end_byte": an_json["long_answer"]["end_byte"],
-                        "candidate_index": an_json["long_answer"]["candidate_index"],
-                    },
-                    "short_answers": [_parse_short_answer(ans) for ans in an_json["short_answers"]],
-                    "yes_no_answer": (-1 if an_json["yes_no_answer"] == "NONE" else an_json["yes_no_answer"]),
-                }
-
-            beam.metrics.Metrics.counter("nq", "examples").inc()
-            # Convert to str since some IDs cannot be represented by datasets.Value('int64').
-            id_ = str(ex_json["example_id"])
-            return (
-                id_,
-                {
-                    "id": id_,
-                    "document": {
-                        "title": ex_json["document_title"],
-                        "url": ex_json["document_url"],
-                        "html": ex_json["document_html"],
-                        "tokens": [
-                            {
-                                "token": t["token"],
-                                "is_html": t["html_token"],
-                                "start_byte": t["start_byte"],
-                                "end_byte": t["end_byte"],
-                            }
-                            for t in ex_json["document_tokens"]
-                        ],
-                    },
-                    "question": {"text": ex_json["question_text"], "tokens": ex_json["question_tokens"]},
-                    "long_answer_candidates": [lac_json for lac_json in ex_json["long_answer_candidates"]],
-                    "annotations": [_parse_annotation(an_json) for an_json in ex_json["annotations"]],
-                },
-            )
-
-        return (
-            pipeline
-            | beam.Create(filepaths)
-            | beam.io.ReadAllFromText(compression_type=beam.io.textio.CompressionTypes.GZIP)
-            | beam.Map(_parse_example)
-        )