albertvillanova HF staff commited on
Commit
0cc4353
1 Parent(s): 12ecea9

Convert dataset to Parquet (#3)

Browse files

- Convert dataset to Parquet (611791ece3eae804b94d65af61c09574c98fbca3)
- Add tsv_format data files (473d8d4e3150ece57226deb7dcfaceba2b3c7c70)
- Add dgem_format data files (29c8a87fb777db3422bf06475d6b9f81468678a5)
- Add predictor_format data files (7510be0424b17bcd7f83875007fd5d19eb96ae14)
- Delete loading script (5dfd01a9156817b834b7b7ffdbaa06ccbe17630c)
- Delete legacy dataset_infos.json (22f702f8fbe77e901fdf09e0ae3cb31018b955a5)

README.md CHANGED
@@ -4,102 +4,135 @@ language:
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  paperswithcode_id: scitail
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  pretty_name: SciTail
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  dataset_info:
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  dtype: string
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- - name: annotator_labels
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- sequence: string
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- - name: gold_label
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  dtype: string
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  splits:
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  num_examples: 2126
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  - name: validation
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  num_examples: 1304
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- download_size: 14174621
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- dataset_size: 25770993
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- - name: hypothesis
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  dtype: string
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  dtype: string
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  - name: validation
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- download_size: 14174621
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- dataset_size: 10193289
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
103
  ---
104
 
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  # Dataset Card for "scitail"
 
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  paperswithcode_id: scitail
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  pretty_name: SciTail
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  dataset_info:
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+ - config_name: dgem_format
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  num_examples: 1304
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  ---
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  # Dataset Card for "scitail"
dataset_infos.json DELETED
@@ -1 +0,0 @@
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- {"snli_format": {"description": "The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question \nand the correct answer choice are converted into an assertive statement to form the hypothesis. We use information \nretrieval to obtain relevant text from a large text corpus of web sentences, and use these sentences as a premise P. We \ncrowdsource the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order to create \nthe SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples \nwith neutral label\n", "citation": "inproceedings{scitail,\n Author = {Tushar Khot and Ashish Sabharwal and Peter Clark},\n Booktitle = {AAAI},\n Title = {{SciTail}: A Textual Entailment Dataset from Science Question Answering},\n Year = {2018}\n}\n", "homepage": "https://allenai.org/data/scitail", "license": "", "features": {"sentence1_binary_parse": {"dtype": "string", "id": null, "_type": "Value"}, "sentence1_parse": {"dtype": "string", "id": null, "_type": "Value"}, "sentence1": {"dtype": "string", "id": null, "_type": "Value"}, "sentence2_parse": {"dtype": "string", "id": null, "_type": "Value"}, "sentence2": {"dtype": "string", "id": null, "_type": "Value"}, "annotator_labels": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "gold_label": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "scitail", "config_name": "snli_format", "version": {"version_str": "1.1.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 22495833, "num_examples": 23596, "dataset_name": "scitail"}, "test": {"name": "test", "num_bytes": 2008631, "num_examples": 2126, "dataset_name": "scitail"}, "validation": {"name": "validation", "num_bytes": 1266529, "num_examples": 1304, "dataset_name": "scitail"}}, "download_checksums": {"http://data.allenai.org.s3.amazonaws.com/downloads/SciTailV1.1.zip": {"num_bytes": 14174621, "checksum": "3fccd37350a94ca280b75998568df85fc2fc62843a3198d644fcbf858e6943d5"}}, "download_size": 14174621, "dataset_size": 25770993, "size_in_bytes": 39945614}, "tsv_format": {"description": "The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. 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scitail.py DELETED
@@ -1,298 +0,0 @@
1
- """TODO(sciTail): Add a description here."""
2
-
3
-
4
- import csv
5
- import json
6
- import os
7
- import textwrap
8
-
9
- import datasets
10
-
11
-
12
- # TODO(sciTail): BibTeX citation
13
- _CITATION = """\
14
- inproceedings{scitail,
15
- Author = {Tushar Khot and Ashish Sabharwal and Peter Clark},
16
- Booktitle = {AAAI},
17
- Title = {{SciTail}: A Textual Entailment Dataset from Science Question Answering},
18
- Year = {2018}
19
- }
20
- """
21
-
22
- # TODO(sciTail):
23
- _DESCRIPTION = """\
24
- The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question
25
- and the correct answer choice are converted into an assertive statement to form the hypothesis. We use information
26
- retrieval to obtain relevant text from a large text corpus of web sentences, and use these sentences as a premise P. We
27
- crowdsource the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order to create
28
- the SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples
29
- with neutral label
30
- """
31
-
32
- _URL = "http://data.allenai.org.s3.amazonaws.com/downloads/SciTailV1.1.zip"
33
-
34
-
35
- class ScitailConfig(datasets.BuilderConfig):
36
-
37
- """BuilderConfig for Xquad"""
38
-
39
- def __init__(self, **kwargs):
40
- """
41
-
42
- Args:
43
- **kwargs: keyword arguments forwarded to super.
44
- """
45
- super(ScitailConfig, self).__init__(version=datasets.Version("1.1.0", ""), **kwargs)
46
-
47
-
48
- class Scitail(datasets.GeneratorBasedBuilder):
49
- """TODO(sciTail): Short description of my dataset."""
50
-
51
- # TODO(sciTail): Set up version.
52
- VERSION = datasets.Version("1.1.0")
53
- BUILDER_CONFIGS = [
54
- ScitailConfig(
55
- name="snli_format",
56
- description="JSONL format used by SNLI with a JSON object corresponding to each entailment example in each line.",
57
- ),
58
- ScitailConfig(
59
- name="tsv_format", description="Tab-separated format with three columns: premise hypothesis label"
60
- ),
61
- ScitailConfig(
62
- name="dgem_format",
63
- description="Tab-separated format used by the DGEM model: premise hypothesis label hypothesis graph structure",
64
- ),
65
- ScitailConfig(
66
- name="predictor_format",
67
- description=textwrap.dedent(
68
- """\
69
- AllenNLP predictors work only with JSONL format. This folder contains the SciTail train/dev/test in JSONL format
70
- so that it can be loaded into the predictors. Each line is a JSON object with the following keys:
71
- gold_label : the example label from {entails, neutral}
72
- sentence1: the premise
73
- sentence2: the hypothesis
74
- sentence2_structure: structure from the hypothesis """
75
- ),
76
- ),
77
- ]
78
-
79
- def _info(self):
80
- # TODO(sciTail): Specifies the datasets.DatasetInfo object
81
- if self.config.name == "snli_format":
82
- return datasets.DatasetInfo(
83
- # This is the description that will appear on the datasets page.
84
- description=_DESCRIPTION,
85
- # datasets.features.FeatureConnectors
86
- features=datasets.Features(
87
- {
88
- "sentence1_binary_parse": datasets.Value("string"),
89
- "sentence1_parse": datasets.Value("string"),
90
- "sentence1": datasets.Value("string"),
91
- "sentence2_parse": datasets.Value("string"),
92
- "sentence2": datasets.Value("string"),
93
- "annotator_labels": datasets.features.Sequence(datasets.Value("string")),
94
- "gold_label": datasets.Value("string")
95
- # These are the features of your dataset like images, labels ...
96
- }
97
- ),
98
- # If there's a common (input, target) tuple from the features,
99
- # specify them here. They'll be used if as_supervised=True in
100
- # builder.as_dataset.
101
- supervised_keys=None,
102
- # Homepage of the dataset for documentation
103
- homepage="https://allenai.org/data/scitail",
104
- citation=_CITATION,
105
- )
106
- elif self.config.name == "tsv_format":
107
- return datasets.DatasetInfo(
108
- # This is the description that will appear on the datasets page.
109
- description=_DESCRIPTION,
110
- # datasets.features.FeatureConnectors
111
- features=datasets.Features(
112
- {
113
- "premise": datasets.Value("string"),
114
- "hypothesis": datasets.Value("string"),
115
- "label": datasets.Value("string")
116
- # These are the features of your dataset like images, labels ...
117
- }
118
- ),
119
- # If there's a common (input, target) tuple from the features,
120
- # specify them here. They'll be used if as_supervised=True in
121
- # builder.as_dataset.
122
- supervised_keys=None,
123
- # Homepage of the dataset for documentation
124
- homepage="https://allenai.org/data/scitail",
125
- citation=_CITATION,
126
- )
127
- elif self.config.name == "predictor_format":
128
- return datasets.DatasetInfo(
129
- # This is the description that will appear on the datasets page.
130
- description=_DESCRIPTION,
131
- # datasets.features.FeatureConnectors
132
- features=datasets.Features(
133
- {
134
- "answer": datasets.Value("string"),
135
- "sentence2_structure": datasets.Value("string"),
136
- "sentence1": datasets.Value("string"),
137
- "sentence2": datasets.Value("string"),
138
- "gold_label": datasets.Value("string"),
139
- "question": datasets.Value("string")
140
- # These are the features of your dataset like images, labels ...
141
- }
142
- ),
143
- # If there's a common (input, target) tuple from the features,
144
- # specify them here. They'll be used if as_supervised=True in
145
- # builder.as_dataset.
146
- supervised_keys=None,
147
- # Homepage of the dataset for documentation
148
- homepage="https://allenai.org/data/scitail",
149
- citation=_CITATION,
150
- )
151
- elif self.config.name == "dgem_format":
152
- return datasets.DatasetInfo(
153
- # This is the description that will appear on the datasets page.
154
- description=_DESCRIPTION,
155
- # datasets.features.FeatureConnectors
156
- features=datasets.Features(
157
- {
158
- "premise": datasets.Value("string"),
159
- "hypothesis": datasets.Value("string"),
160
- "label": datasets.Value("string"),
161
- "hypothesis_graph_structure": datasets.Value("string")
162
- # These are the features of your dataset like images, labels ...
163
- }
164
- ),
165
- # If there's a common (input, target) tuple from the features,
166
- # specify them here. They'll be used if as_supervised=True in
167
- # builder.as_dataset.
168
- supervised_keys=None,
169
- # Homepage of the dataset for documentation
170
- homepage="https://allenai.org/data/scitail",
171
- citation=_CITATION,
172
- )
173
-
174
- def _split_generators(self, dl_manager):
175
- """Returns SplitGenerators."""
176
- # TODO(sciTail): Downloads the data and defines the splits
177
- # dl_manager is a datasets.download.DownloadManager that can be used to
178
- # download and extract URLs
179
- dl_dir = dl_manager.download_and_extract(_URL)
180
- data_dir = os.path.join(dl_dir, "SciTailV1.1")
181
- snli = os.path.join(data_dir, "snli_format")
182
- dgem = os.path.join(data_dir, "dgem_format")
183
- tsv = os.path.join(data_dir, "tsv_format")
184
- predictor = os.path.join(data_dir, "predictor_format")
185
- if self.config.name == "snli_format":
186
- return [
187
- datasets.SplitGenerator(
188
- name=datasets.Split.TRAIN,
189
- # These kwargs will be passed to _generate_examples
190
- gen_kwargs={"filepath": os.path.join(snli, "scitail_1.0_train.txt")},
191
- ),
192
- datasets.SplitGenerator(
193
- name=datasets.Split.TEST,
194
- # These kwargs will be passed to _generate_examples
195
- gen_kwargs={"filepath": os.path.join(snli, "scitail_1.0_test.txt")},
196
- ),
197
- datasets.SplitGenerator(
198
- name=datasets.Split.VALIDATION,
199
- # These kwargs will be passed to _generate_examples
200
- gen_kwargs={"filepath": os.path.join(snli, "scitail_1.0_dev.txt")},
201
- ),
202
- ]
203
- elif self.config.name == "tsv_format":
204
- return [
205
- datasets.SplitGenerator(
206
- name=datasets.Split.TRAIN,
207
- # These kwargs will be passed to _generate_examples
208
- gen_kwargs={"filepath": os.path.join(tsv, "scitail_1.0_train.tsv")},
209
- ),
210
- datasets.SplitGenerator(
211
- name=datasets.Split.TEST,
212
- # These kwargs will be passed to _generate_examples
213
- gen_kwargs={"filepath": os.path.join(tsv, "scitail_1.0_test.tsv")},
214
- ),
215
- datasets.SplitGenerator(
216
- name=datasets.Split.VALIDATION,
217
- # These kwargs will be passed to _generate_examples
218
- gen_kwargs={"filepath": os.path.join(tsv, "scitail_1.0_dev.tsv")},
219
- ),
220
- ]
221
- elif self.config.name == "predictor_format":
222
- return [
223
- datasets.SplitGenerator(
224
- name=datasets.Split.TRAIN,
225
- # These kwargs will be passed to _generate_examples
226
- gen_kwargs={"filepath": os.path.join(predictor, "scitail_1.0_structure_train.jsonl")},
227
- ),
228
- datasets.SplitGenerator(
229
- name=datasets.Split.TEST,
230
- # These kwargs will be passed to _generate_examples
231
- gen_kwargs={"filepath": os.path.join(predictor, "scitail_1.0_structure_test.jsonl")},
232
- ),
233
- datasets.SplitGenerator(
234
- name=datasets.Split.VALIDATION,
235
- # These kwargs will be passed to _generate_examples
236
- gen_kwargs={"filepath": os.path.join(predictor, "scitail_1.0_structure_dev.jsonl")},
237
- ),
238
- ]
239
- elif self.config.name == "dgem_format":
240
- return [
241
- datasets.SplitGenerator(
242
- name=datasets.Split.TRAIN,
243
- # These kwargs will be passed to _generate_examples
244
- gen_kwargs={"filepath": os.path.join(dgem, "scitail_1.0_structure_train.tsv")},
245
- ),
246
- datasets.SplitGenerator(
247
- name=datasets.Split.TEST,
248
- # These kwargs will be passed to _generate_examples
249
- gen_kwargs={"filepath": os.path.join(dgem, "scitail_1.0_structure_test.tsv")},
250
- ),
251
- datasets.SplitGenerator(
252
- name=datasets.Split.VALIDATION,
253
- # These kwargs will be passed to _generate_examples
254
- gen_kwargs={"filepath": os.path.join(dgem, "scitail_1.0_structure_dev.tsv")},
255
- ),
256
- ]
257
-
258
- def _generate_examples(self, filepath):
259
- """Yields examples."""
260
- # TODO(sciTail): Yields (key, example) tuples from the dataset
261
- with open(filepath, encoding="utf-8") as f:
262
- if self.config.name == "snli_format":
263
- for id_, row in enumerate(f):
264
- data = json.loads(row)
265
-
266
- yield id_, {
267
- "sentence1_binary_parse": data["sentence1_binary_parse"],
268
- "sentence1_parse": data["sentence1_parse"],
269
- "sentence1": data["sentence1"],
270
- "sentence2_parse": data["sentence2_parse"],
271
- "sentence2": data["sentence2"],
272
- "annotator_labels": data["annotator_labels"],
273
- "gold_label": data["gold_label"],
274
- }
275
- elif self.config.name == "tsv_format":
276
- data = csv.reader(f, delimiter="\t")
277
- for id_, row in enumerate(data):
278
- yield id_, {"premise": row[0], "hypothesis": row[1], "label": row[2]}
279
- elif self.config.name == "dgem_format":
280
- data = csv.reader(f, delimiter="\t")
281
- for id_, row in enumerate(data):
282
- yield id_, {
283
- "premise": row[0],
284
- "hypothesis": row[1],
285
- "label": row[2],
286
- "hypothesis_graph_structure": row[3],
287
- }
288
- elif self.config.name == "predictor_format":
289
- for id_, row in enumerate(f):
290
- data = json.loads(row)
291
- yield id_, {
292
- "answer": data["answer"],
293
- "sentence2_structure": data["sentence2_structure"],
294
- "sentence1": data["sentence1"],
295
- "sentence2": data["sentence2"],
296
- "gold_label": data["gold_label"],
297
- "question": data["question"],
298
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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