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albertvillanova HF staff commited on
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80596c4
1 Parent(s): e4d7191

Convert dataset to Parquet (#6)

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- Convert dataset to Parquet (167c717c7aed0d8300f27651433c35daff1440f2)
- Delete loading script (bc1432158a3d147e8bdda46a43cc84471ac2433d)
- Delete legacy dataset_infos.json (dccd52e142ef4ad27083cc78299b15ebccb8fd13)

README.md CHANGED
@@ -1,5 +1,4 @@
1
  ---
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- pretty_name: SQuAD2.0
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  annotations_creators:
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  - crowdsourced
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  language_creators:
@@ -20,23 +19,9 @@ task_ids:
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  - open-domain-qa
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  - extractive-qa
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  paperswithcode_id: squad
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- train-eval-index:
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- - config: squad_v2
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- task: question-answering
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- task_id: extractive_question_answering
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- splits:
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- train_split: train
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- eval_split: validation
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- col_mapping:
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- question: question
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- context: context
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- answers:
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- text: text
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- answer_start: answer_start
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- metrics:
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- - type: squad_v2
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- name: SQuAD v2
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  dataset_info:
 
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  features:
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  - name: id
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  dtype: string
@@ -52,16 +37,39 @@ dataset_info:
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  dtype: string
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  - name: answer_start
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  dtype: int32
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- config_name: squad_v2
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  splits:
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  - name: train
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- num_bytes: 116699950
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  num_examples: 130319
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  - name: validation
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- num_bytes: 11660302
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  num_examples: 11873
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- download_size: 46494161
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- dataset_size: 128360252
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
65
  ---
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  # Dataset Card for "squad_v2"
 
1
  ---
 
2
  annotations_creators:
3
  - crowdsourced
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  language_creators:
 
19
  - open-domain-qa
20
  - extractive-qa
21
  paperswithcode_id: squad
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+ pretty_name: SQuAD2.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
23
  dataset_info:
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+ config_name: squad_v2
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  features:
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  - name: id
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  dtype: string
 
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  dtype: string
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  - name: answer_start
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  dtype: int32
 
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  splits:
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  - name: train
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+ num_bytes: 116732025
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  num_examples: 130319
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  - name: validation
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+ num_bytes: 11661091
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  num_examples: 11873
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+ download_size: 17720493
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+ dataset_size: 128393116
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+ configs:
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+ - config_name: squad_v2
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+ data_files:
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+ - split: train
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+ path: squad_v2/train-*
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+ - split: validation
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+ path: squad_v2/validation-*
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+ default: true
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+ train-eval-index:
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+ - config: squad_v2
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+ task: question-answering
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+ task_id: extractive_question_answering
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+ splits:
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+ train_split: train
63
+ eval_split: validation
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+ col_mapping:
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+ question: question
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+ context: context
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+ answers:
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+ text: text
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+ answer_start: answer_start
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+ metrics:
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+ - type: squad_v2
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+ name: SQuAD v2
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  ---
74
 
75
  # Dataset Card for "squad_v2"
dataset_infos.json DELETED
@@ -1 +0,0 @@
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- {"squad_v2": {"description": "combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers\n to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but\n also determine when no answer is supported by the paragraph and abstain from answering.\n", "citation": "@article{2016arXiv160605250R,\n author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},\n Konstantin and {Liang}, Percy},\n title = \"{SQuAD: 100,000+ Questions for Machine Comprehension of Text}\",\n journal = {arXiv e-prints},\n year = 2016,\n eid = {arXiv:1606.05250},\n pages = {arXiv:1606.05250},\narchivePrefix = {arXiv},\n eprint = {1606.05250},\n}\n", "homepage": "https://rajpurkar.github.io/SQuAD-explorer/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "question-answering-extractive", "question_column": "question", "context_column": "context", "answers_column": "answers"}], "builder_name": "squad_v2", "config_name": "squad_v2", "version": {"version_str": "2.0.0", "description": null, "major": 2, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 116699950, "num_examples": 130319, "dataset_name": "squad_v2"}, "validation": {"name": "validation", "num_bytes": 11660302, "num_examples": 11873, "dataset_name": "squad_v2"}}, "download_checksums": {"https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json": {"num_bytes": 42123633, "checksum": "68dcfbb971bd3e96d5b46c7177b16c1a4e7d4bdef19fb204502738552dede002"}, "https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json": {"num_bytes": 4370528, "checksum": "80a5225e94905956a6446d296ca1093975c4d3b3260f1d6c8f68bc2ab77182d8"}}, "download_size": 46494161, "post_processing_size": null, "dataset_size": 128360252, "size_in_bytes": 174854413}}
 
 
squad_v2.py DELETED
@@ -1,133 +0,0 @@
1
- """TODO(squad_v2): Add a description here."""
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-
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-
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- import json
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-
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- import datasets
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- from datasets.tasks import QuestionAnsweringExtractive
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-
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-
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- # TODO(squad_v2): BibTeX citation
11
- _CITATION = """\
12
- @article{2016arXiv160605250R,
13
- author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
14
- Konstantin and {Liang}, Percy},
15
- title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
16
- journal = {arXiv e-prints},
17
- year = 2016,
18
- eid = {arXiv:1606.05250},
19
- pages = {arXiv:1606.05250},
20
- archivePrefix = {arXiv},
21
- eprint = {1606.05250},
22
- }
23
- """
24
-
25
- _DESCRIPTION = """\
26
- combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers
27
- to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but
28
- also determine when no answer is supported by the paragraph and abstain from answering.
29
- """
30
-
31
- _URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
32
- _URLS = {
33
- "train": _URL + "train-v2.0.json",
34
- "dev": _URL + "dev-v2.0.json",
35
- }
36
-
37
-
38
- class SquadV2Config(datasets.BuilderConfig):
39
- """BuilderConfig for SQUAD."""
40
-
41
- def __init__(self, **kwargs):
42
- """BuilderConfig for SQUADV2.
43
-
44
- Args:
45
- **kwargs: keyword arguments forwarded to super.
46
- """
47
- super(SquadV2Config, self).__init__(**kwargs)
48
-
49
-
50
- class SquadV2(datasets.GeneratorBasedBuilder):
51
- """TODO(squad_v2): Short description of my dataset."""
52
-
53
- # TODO(squad_v2): Set up version.
54
- BUILDER_CONFIGS = [
55
- SquadV2Config(name="squad_v2", version=datasets.Version("2.0.0"), description="SQuAD plaint text version 2"),
56
- ]
57
-
58
- def _info(self):
59
- # TODO(squad_v2): Specifies the datasets.DatasetInfo object
60
- return datasets.DatasetInfo(
61
- # This is the description that will appear on the datasets page.
62
- description=_DESCRIPTION,
63
- # datasets.features.FeatureConnectors
64
- features=datasets.Features(
65
- {
66
- "id": datasets.Value("string"),
67
- "title": datasets.Value("string"),
68
- "context": datasets.Value("string"),
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- "question": datasets.Value("string"),
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- "answers": datasets.features.Sequence(
71
- {
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- "text": datasets.Value("string"),
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- "answer_start": datasets.Value("int32"),
74
- }
75
- ),
76
- # These are the features of your dataset like images, labels ...
77
- }
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- ),
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- # If there's a common (input, target) tuple from the features,
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- # specify them here. They'll be used if as_supervised=True in
81
- # builder.as_dataset.
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- supervised_keys=None,
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- # Homepage of the dataset for documentation
84
- homepage="https://rajpurkar.github.io/SQuAD-explorer/",
85
- citation=_CITATION,
86
- task_templates=[
87
- QuestionAnsweringExtractive(
88
- question_column="question", context_column="context", answers_column="answers"
89
- )
90
- ],
91
- )
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-
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- def _split_generators(self, dl_manager):
94
- """Returns SplitGenerators."""
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- # TODO(squad_v2): Downloads the data and defines the splits
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- # dl_manager is a datasets.download.DownloadManager that can be used to
97
- # download and extract URLs
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- urls_to_download = _URLS
99
- downloaded_files = dl_manager.download_and_extract(urls_to_download)
100
-
101
- return [
102
- datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
103
- datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
104
- ]
105
-
106
- def _generate_examples(self, filepath):
107
- """Yields examples."""
108
- # TODO(squad_v2): Yields (key, example) tuples from the dataset
109
- with open(filepath, encoding="utf-8") as f:
110
- squad = json.load(f)
111
- for example in squad["data"]:
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- title = example.get("title", "")
113
- for paragraph in example["paragraphs"]:
114
- context = paragraph["context"] # do not strip leading blank spaces GH-2585
115
- for qa in paragraph["qas"]:
116
- question = qa["question"]
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- id_ = qa["id"]
118
-
119
- answer_starts = [answer["answer_start"] for answer in qa["answers"]]
120
- answers = [answer["text"] for answer in qa["answers"]]
121
-
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- # Features currently used are "context", "question", and "answers".
123
- # Others are extracted here for the ease of future expansions.
124
- yield id_, {
125
- "title": title,
126
- "context": context,
127
- "question": question,
128
- "id": id_,
129
- "answers": {
130
- "answer_start": answer_starts,
131
- "text": answers,
132
- },
133
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ size 16369982
squad_v2/validation-00000-of-00001.parquet ADDED
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+ size 1350511