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Convert dataset to Parquet (#6)

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- Convert dataset to Parquet (416d7e9acdddb433fd3c2dd6e497751754b55499)
- Delete loading script (c63d43c5c9ec7bb96e13206b00304313fc56a654)

README.md CHANGED
@@ -71,8 +71,17 @@ dataset_info:
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  - name: validation
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  num_bytes: 1211859418
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  num_examples: 3461
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- download_size: 192528922
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  dataset_size: 16315602701
 
 
 
 
 
 
 
 
 
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  ---
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  # Dataset Card for Narrative QA
 
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  - name: validation
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  num_bytes: 1211859418
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  num_examples: 3461
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+ download_size: 3232805701
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  dataset_size: 16315602701
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+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: train
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+ path: data/train-*
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+ - split: test
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+ path: data/test-*
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+ - split: validation
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+ path: data/validation-*
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  ---
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87
  # Dataset Card for Narrative QA
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narrativeqa.py DELETED
@@ -1,174 +0,0 @@
1
- # coding=utf-8
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- # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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- #
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- # Licensed under the Apache License, Version 2.0 (the "License");
5
- # you may not use this file except in compliance with the License.
6
- # You may obtain a copy of the License at
7
- #
8
- # http://www.apache.org/licenses/LICENSE-2.0
9
- #
10
- # Unless required by applicable law or agreed to in writing, software
11
- # distributed under the License is distributed on an "AS IS" BASIS,
12
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
- # See the License for the specific language governing permissions and
14
- # limitations under the License.
15
- """NarrativeQA Reading Comprehension Challenge"""
16
-
17
-
18
- import csv
19
- import os
20
-
21
- import datasets
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-
23
-
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- _CITATION = """\
25
- @article{kocisky-etal-2018-narrativeqa,
26
- title = "The {N}arrative{QA} Reading Comprehension Challenge",
27
- author = "Ko{\v{c}}isk{\'y}, Tom{\'a}{\v{s}} and
28
- Schwarz, Jonathan and
29
- Blunsom, Phil and
30
- Dyer, Chris and
31
- Hermann, Karl Moritz and
32
- Melis, G{\'a}bor and
33
- Grefenstette, Edward",
34
- editor = "Lee, Lillian and
35
- Johnson, Mark and
36
- Toutanova, Kristina and
37
- Roark, Brian",
38
- journal = "Transactions of the Association for Computational Linguistics",
39
- volume = "6",
40
- year = "2018",
41
- address = "Cambridge, MA",
42
- publisher = "MIT Press",
43
- url = "https://aclanthology.org/Q18-1023",
44
- doi = "10.1162/tacl_a_00023",
45
- pages = "317--328",
46
- abstract = "Reading comprehension (RC){---}in contrast to information retrieval{---}requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess RC ability, in both artificial agents and children learning to read. However, existing RC datasets and tasks are dominated by questions that can be solved by selecting answers using superficial information (e.g., local context similarity or global term frequency); they thus fail to test for the essential integrative aspect of RC. To encourage progress on deeper comprehension of language, we present a new dataset and set of tasks in which the reader must answer questions about stories by reading entire books or movie scripts. These tasks are designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience. We show that although humans solve the tasks easily, standard RC models struggle on the tasks presented here. We provide an analysis of the dataset and the challenges it presents.",
47
- }
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- """
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-
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- _DESCRIPTION = """\
51
- The NarrativeQA dataset for question answering on long documents (movie scripts, books). It includes the list of documents with Wikipedia summaries, links to full stories, and questions and answers.
52
- """
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-
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- # Source:
55
- # - full_text: https://storage.googleapis.com/huggingface-nlp/datasets/narrative_qa/narrativeqa_full_text.zip
56
- # - repo: https://github.com/deepmind/narrativeqa/archive/master.zip
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- _URLS = {
58
- "full_text": "data/narrativeqa_full_text.zip",
59
- "repo": "data/narrativeqa-master.zip",
60
- }
61
-
62
-
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- class NarrativeQa(datasets.GeneratorBasedBuilder):
64
- """NarrativeQA: Question answering on long-documents"""
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-
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- def _info(self):
67
- return datasets.DatasetInfo(
68
- description=_DESCRIPTION,
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- citation=_CITATION,
70
- features=datasets.Features(
71
- {
72
- "document": {
73
- "id": datasets.Value("string"),
74
- "kind": datasets.Value("string"),
75
- "url": datasets.Value("string"),
76
- "file_size": datasets.Value("int32"),
77
- "word_count": datasets.Value("int32"),
78
- "start": datasets.Value("string"),
79
- "end": datasets.Value("string"),
80
- "summary": {
81
- "text": datasets.Value("string"),
82
- "tokens": datasets.features.Sequence(datasets.Value("string")),
83
- "url": datasets.Value("string"),
84
- "title": datasets.Value("string"),
85
- },
86
- "text": datasets.Value("string"),
87
- },
88
- "question": {
89
- "text": datasets.Value("string"),
90
- "tokens": datasets.features.Sequence(datasets.Value("string")),
91
- },
92
- "answers": [
93
- {
94
- "text": datasets.Value("string"),
95
- "tokens": datasets.features.Sequence(datasets.Value("string")),
96
- }
97
- ],
98
- }
99
- ),
100
- homepage="https://github.com/deepmind/narrativeqa",
101
- )
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-
103
- def _split_generators(self, dl_manager):
104
- """Returns SplitGenerators."""
105
-
106
- dl_dir = dl_manager.download_and_extract(_URLS)
107
- dl_dir["repo"] = os.path.join(dl_dir["repo"], "narrativeqa-master")
108
-
109
- return [
110
- datasets.SplitGenerator(
111
- name=datasets.Split.TRAIN,
112
- gen_kwargs={"repo_dir": dl_dir["repo"], "full_text_dir": dl_dir["full_text"], "split": "train"},
113
- ),
114
- datasets.SplitGenerator(
115
- name=datasets.Split.TEST,
116
- gen_kwargs={"repo_dir": dl_dir["repo"], "full_text_dir": dl_dir["full_text"], "split": "test"},
117
- ),
118
- datasets.SplitGenerator(
119
- name=datasets.Split.VALIDATION,
120
- gen_kwargs={"repo_dir": dl_dir["repo"], "full_text_dir": dl_dir["full_text"], "split": "valid"},
121
- ),
122
- ]
123
-
124
- def _generate_examples(self, repo_dir, full_text_dir, split):
125
- """Yields examples."""
126
- documents = {}
127
- with open(os.path.join(repo_dir, "documents.csv"), encoding="utf-8") as f:
128
- reader = csv.DictReader(f)
129
- for row in reader:
130
- if row["set"] != split:
131
- continue
132
- documents[row["document_id"]] = row
133
-
134
- summaries = {}
135
- with open(os.path.join(repo_dir, "third_party", "wikipedia", "summaries.csv"), encoding="utf-8") as f:
136
- reader = csv.DictReader(f)
137
- for row in reader:
138
- if row["set"] != split:
139
- continue
140
- summaries[row["document_id"]] = row
141
-
142
- with open(os.path.join(repo_dir, "qaps.csv"), encoding="utf-8") as f:
143
- reader = csv.DictReader(f)
144
- for id_, row in enumerate(reader):
145
- if row["set"] != split:
146
- continue
147
- document_id = row["document_id"]
148
- document = documents[document_id]
149
- summary = summaries[document_id]
150
- full_text = open(os.path.join(full_text_dir, document_id + ".content"), encoding="latin-1").read()
151
- res = {
152
- "document": {
153
- "id": document["document_id"],
154
- "kind": document["kind"],
155
- "url": document["story_url"],
156
- "file_size": document["story_file_size"],
157
- "word_count": document["story_word_count"],
158
- "start": document["story_start"],
159
- "end": document["story_end"],
160
- "summary": {
161
- "text": summary["summary"],
162
- "tokens": summary["summary_tokenized"].split(),
163
- "url": document["wiki_url"],
164
- "title": document["wiki_title"],
165
- },
166
- "text": full_text,
167
- },
168
- "question": {"text": row["question"], "tokens": row["question_tokenized"].split()},
169
- "answers": [
170
- {"text": row["answer1"], "tokens": row["answer1_tokenized"].split()},
171
- {"text": row["answer2"], "tokens": row["answer2_tokenized"].split()},
172
- ],
173
- }
174
- yield id_, res