Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
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+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
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+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- found
+languages:
+- en
+licenses:
+- cc-by-sa-3-0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- extended|natural_questions
+- original
+task_categories:
+- question-answering
+task_ids:
+- open-domain-qa
+---
+
+# Dataset Card for AmbigQA: Answering Ambiguous Open-domain Questions
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [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)
+
+## Dataset Description
+
+- [**Homepage:**](https://nlp.cs.washington.edu/ambigqa/)
+- [**Repository:**](https://github.com/shmsw25/AmbigQA)
+- [**Paper:**](https://arxiv.org/pdf/2004.10645.pdf)
+
+### Dataset Summary
+
+AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus.  AMBIGNQ, a dataset with
+14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity.
+We provide two distributions of our new dataset AmbigNQ: a `full` version with all annotation metadata and a `light` version with only inputs and outputs.
+
+### Supported Tasks and Leaderboards
+
+`question-answering`
+
+### Languages
+
+English
+
+## Dataset Structure
+### Data Instances
+
+An example from the data set looks as follows:
+```
+{'annotations': {'answer': [[]],
+  'qaPairs': [{'answer': [['April 19, 1987'], ['December 17, 1989']],
+    'question': ['When did the Simpsons first air on television as an animated short on the Tracey Ullman Show?',
+     'When did the Simpsons first air as a half-hour prime time show?']}],
+  'type': ['multipleQAs']},
+ 'id': '-4469503464110108318',
+ 'nq_answer': ['December 17 , 1989'],
+ 'nq_doc_title': 'The Simpsons',
+ 'question': 'When did the simpsons first air on television?',
+ 'used_queries': {'query': ['When did the simpsons first air on television?'],
+  'results': [{'snippet': ['The <b>Simpsons</b> is an American animated <b>television</b> sitcom starring the animated \nSimpson family, ... Since its <b>debut</b> on December 17, 1989, the show <b>has</b> \nbroadcast 673 episodes and its 30th season started ... The <b>Simpsons first</b> season \n<b>was</b> the Fox network&#39;s <b>first TV</b> series to rank among a season&#39;s top 30 highest-\nrated shows.',
+     'The <b>Simpsons</b> is an American animated sitcom created by Matt Groening for the \nFox ... Since its <b>debut</b> on December 17, 1989, 674 episodes of The <b>Simpsons</b> \nhave been broadcast. ... When producer James L. Brooks <b>was</b> working on the \n<b>television</b> variety show The Tracey Ullman Show, he decided to include small \nanimated&nbsp;...',
+     '... in shorts from The Tracey Ullman Show as their <b>television debut</b> in 1987. The \n<b>Simpsons</b> shorts are a series of animated shorts that <b>aired</b> as a recurring \nsegment on Fox variety <b>television</b> series The Tracey ... The final short to <b>air was</b> &quot;\n<b>TV Simpsons</b>&quot;, originally airing on May 14, 1989. The <b>Simpsons</b> later debuted on\n&nbsp;...',
+     'The <b>first</b> season of the American animated <b>television</b> series The <b>Simpsons</b> \noriginally <b>aired</b> on the Fox network between December 17, 1989, and May 13, \n1990, beginning with the Christmas special &quot;<b>Simpsons</b> Roasting on an Open Fire\n&quot;. The executive producers for the <b>first</b> production season <b>were</b> Matt Groening,&nbsp;...',
+     'The <b>Simpsons</b> is an American animated <b>television</b> sitcom created by Matt \nGroening for the Fox ... Since its <b>debut</b> on December 17, 1989, The <b>Simpsons</b> \n<b>has</b> broadcast 674 episodes. The show holds several American <b>television</b> \nlongevity&nbsp;...',
+     'The opening sequence of the American animated <b>television</b> series The <b>Simpsons</b> \nis among the most popular opening sequences in <b>television</b> and is accompanied \nby one of <b>television&#39;s</b> most recognizable theme songs. The <b>first</b> episode to use \nthis intro <b>was</b> the series&#39; second episode &quot;Bart the ... <b>was</b> the <b>first</b> episode of The \n<b>Simpsons</b> to <b>air</b> in 720p high-definition <b>television</b>,&nbsp;...',
+     '&quot;<b>Simpsons</b> Roasting on an Open Fire&quot;, titled onscreen as &quot;The <b>Simpsons</b> \nChristmas Special&quot;, is the premiere episode of the American animated <b>TV</b> series \nThe <b>Simpsons</b>, ... The show <b>was</b> originally intended to <b>debut</b> earlier in 1989 with &quot;\nSome Enchanted Evening&quot;, but due to animation problems with that episode, the \nshow&nbsp;...',
+     '&quot;Stark Raving Dad&quot; is the <b>first</b> episode of the third season of the American \nanimated <b>television</b> series The <b>Simpsons</b>. It <b>first aired</b> on the Fox network in the \nUnited States on September 19, 1991. ... The <b>Simpsons was</b> the second highest \nrated show on Fox the week it <b>aired</b>, behind Married... with Children. &quot;Stark \nRaving Dad,&quot;&nbsp;...',
+     'The <b>Simpsons</b>&#39; twentieth season <b>aired</b> on Fox from September 28, 2008 to May \n17, 2009. With this season, the show tied Gunsmoke as the longest-running \nAmerican primetime <b>television</b> series in terms of total number ... It <b>was</b> the <b>first</b>-\never episode of the show to <b>air</b> in Europe before being seen in the United States.',
+     'The animated <b>TV</b> show The <b>Simpsons</b> is an American English language \nanimated sitcom which ... The <b>Simpsons was</b> dubbed for the <b>first</b> time in Punjabi \nand <b>aired</b> on Geo <b>TV</b> in Pakistan. The name of the localised Punjabi version is \nTedi Sim&nbsp;...'],
+    'title': ['History of The Simpsons',
+     'The Simpsons',
+     'The Simpsons shorts',
+     'The Simpsons (season 1)',
+     'List of The Simpsons episodes',
+     'The Simpsons opening sequence',
+     'Simpsons Roasting on an Open Fire',
+     'Stark Raving Dad',
+     'The Simpsons (season 20)',
+     'Non-English versions of The Simpsons']}]},
+ 'viewed_doc_titles': ['The Simpsons']}
+```
+
+### Data Fields
+
+Full
+```
+{'id': Value(dtype='string', id=None),
+ 'question': Value(dtype='string', id=None),
+ 'annotations': Sequence(feature={'type': Value(dtype='string', id=None), 'answer': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'qaPairs': Sequence(feature={'question': Value(dtype='string', id=None), 'answer': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, length=-1, id=None)}, length=-1, id=None),
+ 'viewed_doc_titles': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),
+ 'used_queries': Sequence(feature={'query': Value(dtype='string', id=None), 'results': Sequence(feature={'title': Value(dtype='string', id=None), 'snippet': Value(dtype='string', id=None)}, length=-1, id=None)}, length=-1, id=None),
+ 'nq_answer': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),
+ 'nq_doc_title': Value(dtype='string', id=None)}
+```
+In the original data format `annotations` have different keys depending on the `type` field = `singleAnswer` or `multipleQAs`. But this implementation uses an empty list `[]` for the unavailable keys 
+
+please refer to Dataset Contents(https://github.com/shmsw25/AmbigQA#dataset-contents) for more details.
+
+```
+for example in train_light_dataset:
+    for i,t in enumerate(example['annotations']['type']):
+        if t =='singleAnswer':
+            # use the example['annotations']['answer'][i]
+            # example['annotations']['qaPairs'][i] - > is []
+            print(example['annotations']['answer'][i])
+        else:
+            # use the example['annotations']['qaPairs'][i]
+            # example['annotations']['answer'][i] - > is []
+            print(example['annotations']['qaPairs'][i])
+```
+
+please refer to Dataset Contents(https://github.com/shmsw25/AmbigQA#dataset-contents) for more details.
+
+Light version only has `id`, `question`, `annotations` fields
+
+### Data Splits
+
+- train: 10036
+- validation: 2002
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+- Wikipedia
+- NQ-open:
+```
+@article{ kwiatkowski2019natural,
+  title={ Natural questions: a benchmark for question answering research},
+  author={ Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and others },
+  journal={ Transactions of the Association for Computational Linguistics },
+  year={ 2019 }
+}
+```
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[CC BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/)
+
+### Citation Information
+```
+@inproceedings{ min2020ambigqa,
+    title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions },
+    author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke },
+    booktitle={ EMNLP },
+    year={2020}
+}
+```

ambig_qa.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+
+"""AmbigQA: Answering Ambiguous Open-domain Questions"""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{ min2020ambigqa,
+    title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions },
+    author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke },
+    booktitle={ EMNLP },
+    year={2020}
+}
+"""
+
+_DESCRIPTION = """\
+AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus.  AMBIGNQ, a dataset with
+14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity.
+We provide two distributions of our new dataset AmbigNQ: a full version with all annotation metadata and a light version with only inputs and outputs.
+"""
+_HOMEPAGE = "https://nlp.cs.washington.edu/ambigqa/"
+_LICENSE = "CC BY-SA 3.0"
+
+_URL = "https://nlp.cs.washington.edu/ambigqa/data/"
+_URLS = {
+    "light": _URL + "ambignq_light.zip",
+    "full": _URL + "ambignq.zip",
+}
+
+
+class AmbigQa(datasets.GeneratorBasedBuilder):
+    """AmbigQA dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="light",
+            version=VERSION,
+            description="AmbigNQ light version with only inputs and outputs",
+        ),
+        datasets.BuilderConfig(
+            name="full",
+            version=VERSION,
+            description="AmbigNQ full version with all annotation metadata",
+        ),
+    ]
+    DEFAULT_CONFIG_NAME = "full"
+
+    def _info(self):
+        features_dict = {
+            "id": datasets.Value("string"),
+            "question": datasets.Value("string"),
+            "annotations": datasets.features.Sequence(
+                {
+                    "type": datasets.Value("string"),  # datasets.ClassLabel(names = ["singleAnswer","multipleQAs"])
+                    "answer": datasets.features.Sequence(datasets.Value("string")),
+                    "qaPairs": datasets.features.Sequence(
+                        {
+                            "question": datasets.Value("string"),
+                            "answer": datasets.features.Sequence(datasets.Value("string")),
+                        }
+                    ),
+                }
+            ),
+        }
+        if self.config.name == "full":
+
+            detail_features = {
+                "viewed_doc_titles": datasets.features.Sequence(datasets.Value("string")),
+                "used_queries": datasets.features.Sequence(
+                    {
+                        "query": datasets.Value("string"),
+                        "results": datasets.features.Sequence(
+                            {
+                                "title": datasets.Value("string"),
+                                "snippet": datasets.Value("string"),
+                            }
+                        ),
+                    }
+                ),
+                "nq_answer": datasets.features.Sequence(datasets.Value("string")),
+                "nq_doc_title": datasets.Value("string"),
+            }
+            features_dict.update(detail_features)
+
+        features = datasets.Features(features_dict)
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # download and extract URLs
+        urls_to_download = _URLS
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        train_file_name = "train.json" if self.config.name == "full" else "train_light.json"
+        dev_file_name = "dev.json" if self.config.name == "full" else "dev_light.json"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": os.path.join(downloaded_files[self.config.name], train_file_name)},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": os.path.join(downloaded_files[self.config.name], dev_file_name)},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+            for example in data:
+                id_ = example["id"]
+                annotations = example["annotations"]
+                # Add this because we cannot have None values (all keys in the schema should be present)
+                for an in annotations:
+                    if "qaPairs" not in an:
+                        an["qaPairs"] = []
+                    if "answer" not in an:
+                        an["answer"] = []
+
+                yield id_, example

dataset_infos.json

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+{"light": {"description": "AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus.  AMBIGNQ, a dataset with\n14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity.\nWe provide two distributions of our new dataset AmbigNQ: a full version with all annotation metadata and a light version with only inputs and outputs.\n", "citation": "@inproceedings{ min2020ambigqa,\n    title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions },\n    author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke },\n    booktitle={ EMNLP },\n    year={2020}\n}\n", "homepage": "https://nlp.cs.washington.edu/ambigqa/", "license": "CC BY-SA 3.0", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "annotations": {"feature": {"type": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "qaPairs": {"feature": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "ambig_qa", "config_name": "light", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2739732, "num_examples": 10036, "dataset_name": "ambig_qa"}, "validation": {"name": "validation", "num_bytes": 805808, "num_examples": 2002, "dataset_name": "ambig_qa"}}, "download_checksums": {"https://nlp.cs.washington.edu/ambigqa/data/ambignq_light.zip": {"num_bytes": 1061383, "checksum": "3f5dada69dec05cef1533a64945cd7bafde1aa94b0cdd6fa9a22f881206220db"}, "https://nlp.cs.washington.edu/ambigqa/data/ambignq.zip": {"num_bytes": 18639517, "checksum": "e85cec5909f076c6f584322c7f05cae44dcacaec93758c110a26fcceaa8da0ce"}}, "download_size": 19700900, "post_processing_size": null, "dataset_size": 3545540, "size_in_bytes": 23246440}, "full": {"description": "AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus.  AMBIGNQ, a dataset with\n14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity.\nWe provide two distributions of our new dataset AmbigNQ: a full version with all annotation metadata and a light version with only inputs and outputs.\n", "citation": "@inproceedings{ min2020ambigqa,\n    title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions },\n    author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke },\n    booktitle={ EMNLP },\n    year={2020}\n}\n", "homepage": "https://nlp.cs.washington.edu/ambigqa/", "license": "CC BY-SA 3.0", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "annotations": {"feature": {"type": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "qaPairs": {"feature": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "viewed_doc_titles": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "used_queries": {"feature": {"query": {"dtype": "string", "id": null, "_type": "Value"}, "results": {"feature": {"title": {"dtype": "string", "id": null, "_type": "Value"}, "snippet": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "nq_answer": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "nq_doc_title": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "ambig_qa", "config_name": "full", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 43538733, "num_examples": 10036, "dataset_name": "ambig_qa"}, "validation": {"name": "validation", "num_bytes": 15383368, "num_examples": 2002, "dataset_name": "ambig_qa"}}, "download_checksums": {"https://nlp.cs.washington.edu/ambigqa/data/ambignq_light.zip": {"num_bytes": 1061383, "checksum": "3f5dada69dec05cef1533a64945cd7bafde1aa94b0cdd6fa9a22f881206220db"}, "https://nlp.cs.washington.edu/ambigqa/data/ambignq.zip": {"num_bytes": 18639517, "checksum": "e85cec5909f076c6f584322c7f05cae44dcacaec93758c110a26fcceaa8da0ce"}}, "download_size": 19700900, "post_processing_size": null, "dataset_size": 58922101, "size_in_bytes": 78623001}}

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