Commit
•
7672994
1
Parent(s):
c7528b3
feat
Browse files- .DS_Store +0 -0
- VieGLUE.py +581 -0
- data/ax/test.tar.gz +3 -0
- data/cola/test.tar.gz +3 -0
- data/cola/train.tar.gz +3 -0
- data/cola/validation.tar.gz +3 -0
- data/mnli/test_matched.tar.gz +3 -0
- data/mnli/test_mismatched.tar.gz +3 -0
- data/mnli/train.tar.gz +3 -0
- data/mnli/validation_matched.tar.gz +3 -0
- data/mnli/validation_mismatched.tar.gz +3 -0
- data/mrpc/test.tar.gz +3 -0
- data/mrpc/train.tar.gz +3 -0
- data/mrpc/validation.tar.gz +3 -0
- data/qnli/test.tar.gz +3 -0
- data/qnli/train.tar.gz +3 -0
- data/qnli/validation.tar.gz +3 -0
- data/qqp/test.tar.gz +3 -0
- data/qqp/train.tar.gz +3 -0
- data/qqp/validation.tar.gz +3 -0
- data/rte/test.tar.gz +3 -0
- data/rte/train.tar.gz +3 -0
- data/rte/validation.tar.gz +3 -0
- data/sst2/test.tar.gz +3 -0
- data/sst2/train.tar.gz +3 -0
- data/sst2/validation.tar.gz +3 -0
- data/stsb/test.tar.gz +3 -0
- data/stsb/train.tar.gz +3 -0
- data/stsb/validation.tar.gz +3 -0
- data/wnli/test.tar.gz +3 -0
- data/wnli/train.tar.gz +3 -0
- data/wnli/validation.tar.gz +3 -0
.DS_Store
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Binary file (6.15 kB). View file
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VieGLUE.py
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1 |
+
# coding=utf-8
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# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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#
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4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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+
# You may obtain a copy of the License at
|
7 |
+
#
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8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
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9 |
+
#
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+
# Unless required by applicable law or agreed to in writing, software
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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 |
+
|
16 |
+
# Lint as: python3
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17 |
+
"""The CC-News dataset is based on Common Crawl News Dataset by Sebastian Nagel"""
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18 |
+
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19 |
+
import json
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20 |
+
import os
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21 |
+
from fnmatch import fnmatch
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22 |
+
import io
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23 |
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import textwrap
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24 |
+
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25 |
+
import datasets
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+
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27 |
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logger = datasets.logging.get_logger(__name__)
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+
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+
######################
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31 |
+
#### DESCRIPTIONS ####
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32 |
+
######################
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+
_DESCRIPTION = """\
|
34 |
+
"""
|
35 |
+
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36 |
+
###################
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37 |
+
#### CITATIONS ####
|
38 |
+
###################
|
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+
_CITATION = """\
|
40 |
+
"""
|
41 |
+
|
42 |
+
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43 |
+
#######################
|
44 |
+
#### DOWNLOAD URLs ####
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45 |
+
#######################
|
46 |
+
_DOWNLOAD_URL = {
|
47 |
+
"ax": {
|
48 |
+
"test": [os.path.join("data", "ax", "test.tar.gz")],
|
49 |
+
},
|
50 |
+
"cola": {
|
51 |
+
"train": [os.path.join("data", "cola", "train.tar.gz")],
|
52 |
+
"test": [os.path.join("data", "cola", "test.tar.gz")],
|
53 |
+
"validation": [os.path.join("data", "cola", "validation.tar.gz")],
|
54 |
+
},
|
55 |
+
"mnli": {
|
56 |
+
"train": [os.path.join("data", "mnli", "train.tar.gz")],
|
57 |
+
"test_matched": [os.path.join("data", "mnli", "test.tar.gz")],
|
58 |
+
"validation_matched": [
|
59 |
+
os.path.join("data", "mnli", "validation_matched.tar.gz")
|
60 |
+
],
|
61 |
+
"test_mismatched": [os.path.join("data", "mnli", "test_mismatched.tar.gz")],
|
62 |
+
"validation_mismatched": [
|
63 |
+
os.path.join("data", "mnli", "validation_mismatched.tar.gz")
|
64 |
+
],
|
65 |
+
},
|
66 |
+
"mrpc": {
|
67 |
+
"train": [os.path.join("data", "mrpc", "train.tar.gz")],
|
68 |
+
"test": [os.path.join("data", "mrpc", "test.tar.gz")],
|
69 |
+
"validation": [os.path.join("data", "mrpc", "validation.tar.gz")],
|
70 |
+
},
|
71 |
+
"qnli": {
|
72 |
+
"train": [os.path.join("data", "qnli", "train.tar.gz")],
|
73 |
+
"test": [os.path.join("data", "qnli", "test.tar.gz")],
|
74 |
+
"validation": [os.path.join("data", "qnli", "validation.tar.gz")],
|
75 |
+
},
|
76 |
+
"qqp": {
|
77 |
+
"train": [os.path.join("data", "qqp", "train.tar.gz")],
|
78 |
+
"test": [os.path.join("data", "qqp", "test.tar.gz")],
|
79 |
+
"validation": [os.path.join("data", "qqp", "validation.tar.gz")],
|
80 |
+
},
|
81 |
+
"rte": {
|
82 |
+
"train": [os.path.join("data", "rte", "train.tar.gz")],
|
83 |
+
"test": [os.path.join("data", "rte", "test.tar.gz")],
|
84 |
+
"validation": [os.path.join("data", "rte", "validation.tar.gz")],
|
85 |
+
},
|
86 |
+
"sst2": {
|
87 |
+
"train": [os.path.join("data", "sst2", "train.tar.gz")],
|
88 |
+
"test": [os.path.join("data", "sst2", "test.tar.gz")],
|
89 |
+
"validation": [os.path.join("data", "sst2", "validation.tar.gz")],
|
90 |
+
},
|
91 |
+
"stsb": {
|
92 |
+
"train": [os.path.join("data", "stsb", "train.tar.gz")],
|
93 |
+
"test": [os.path.join("data", "stsb", "test.tar.gz")],
|
94 |
+
"validation": [os.path.join("data", "stsb", "validation.tar.gz")],
|
95 |
+
},
|
96 |
+
"wnli": {
|
97 |
+
"train": [os.path.join("data", "wnli", "train.tar.gz")],
|
98 |
+
"test": [os.path.join("data", "wnli", "test.tar.gz")],
|
99 |
+
"validation": [os.path.join("data", "wnli", "validation.tar.gz")],
|
100 |
+
},
|
101 |
+
}
|
102 |
+
# VieGLUEConfig(
|
103 |
+
# name="cola",
|
104 |
+
# description=textwrap.dedent(
|
105 |
+
# """\
|
106 |
+
# The Corpus of Linguistic Acceptability consists of English
|
107 |
+
# acceptability judgments drawn from books and journal articles on
|
108 |
+
# linguistic theory. Each example is a sequence of words annotated
|
109 |
+
# with whether it is a grammatical English sentence."""
|
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+
# ),
|
111 |
+
# text_features={"sentence": "sentence"},
|
112 |
+
# label_classes=["unacceptable", "acceptable"],
|
113 |
+
# label_column="is_acceptable",
|
114 |
+
# data_url="https://dl.fbaipublicfiles.com/glue/data/CoLA.zip",
|
115 |
+
# data_dir="CoLA",
|
116 |
+
# citation=textwrap.dedent(
|
117 |
+
# """\
|
118 |
+
# @article{warstadt2018neural,
|
119 |
+
# title={Neural Network Acceptability Judgments},
|
120 |
+
# author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},
|
121 |
+
# journal={arXiv preprint arXiv:1805.12471},
|
122 |
+
# year={2018}
|
123 |
+
# }"""
|
124 |
+
# ),
|
125 |
+
# url="https://nyu-mll.github.io/CoLA/",
|
126 |
+
# ),
|
127 |
+
# VieGLUEConfig(
|
128 |
+
# name="mnli",
|
129 |
+
# description=textwrap.dedent(
|
130 |
+
# """\
|
131 |
+
# The Multi-Genre Natural Language Inference Corpus is a crowdsourced
|
132 |
+
# collection of sentence pairs with textual entailment annotations. Given a premise sentence
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133 |
+
# and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis
|
134 |
+
# (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are
|
135 |
+
# gathered from ten different sources, including transcribed speech, fiction, and government reports.
|
136 |
+
# We use the standard test set, for which we obtained private labels from the authors, and evaluate
|
137 |
+
# on both the matched (in-domain) and mismatched (cross-domain) section. We also use and recommend
|
138 |
+
# the SNLI corpus as 550k examples of auxiliary training data."""
|
139 |
+
# ),
|
140 |
+
# text_features={
|
141 |
+
# "premise": "sentence1",
|
142 |
+
# "hypothesis": "sentence2",
|
143 |
+
# },
|
144 |
+
# label_classes=["entailment", "neutral", "contradiction"],
|
145 |
+
# label_column="gold_label",
|
146 |
+
# data_url="https://dl.fbaipublicfiles.com/glue/data/MNLI.zip",
|
147 |
+
# data_dir="MNLI",
|
148 |
+
# citation=textwrap.dedent(
|
149 |
+
# """\
|
150 |
+
# @InProceedings{N18-1101,
|
151 |
+
# author = "Williams, Adina
|
152 |
+
# and Nangia, Nikita
|
153 |
+
# and Bowman, Samuel",
|
154 |
+
# title = "A Broad-Coverage Challenge Corpus for
|
155 |
+
# Sentence Understanding through Inference",
|
156 |
+
# booktitle = "Proceedings of the 2018 Conference of
|
157 |
+
# the North American Chapter of the
|
158 |
+
# Association for Computational Linguistics:
|
159 |
+
# Human Language Technologies, Volume 1 (Long
|
160 |
+
# Papers)",
|
161 |
+
# year = "2018",
|
162 |
+
# publisher = "Association for Computational Linguistics",
|
163 |
+
# pages = "1112--1122",
|
164 |
+
# location = "New Orleans, Louisiana",
|
165 |
+
# url = "http://aclweb.org/anthology/N18-1101"
|
166 |
+
# }
|
167 |
+
# @article{bowman2015large,
|
168 |
+
# title={A large annotated corpus for learning natural language inference},
|
169 |
+
# author={Bowman, Samuel R and Angeli, Gabor and Potts, Christopher and Manning, Christopher D},
|
170 |
+
# journal={arXiv preprint arXiv:1508.05326},
|
171 |
+
# year={2015}
|
172 |
+
# }"""
|
173 |
+
# ),
|
174 |
+
# url="http://www.nyu.edu/projects/bowman/multinli/",
|
175 |
+
# ),
|
176 |
+
SUBSET_KWARGS = {
|
177 |
+
"ax": {
|
178 |
+
"name": "ax",
|
179 |
+
"text_features": ["premise", "hypothesis"],
|
180 |
+
"label_classes": ["entailment", "neutral", "contradiction"],
|
181 |
+
"label_column": "",
|
182 |
+
"citation": "",
|
183 |
+
"description": textwrap.dedent(
|
184 |
+
"""\
|
185 |
+
A manually-curated evaluation dataset for fine-grained analysis of
|
186 |
+
system performance on a broad range of linguistic phenomena. This
|
187 |
+
dataset evaluates sentence understanding through Natural Language
|
188 |
+
Inference (NLI) problems. Use a model trained on MulitNLI to produce
|
189 |
+
predictions for this dataset."""
|
190 |
+
),
|
191 |
+
},
|
192 |
+
"cola": {
|
193 |
+
"name": "cola",
|
194 |
+
"text_features": ["sentence"],
|
195 |
+
"label_classes": ["unacceptable", "acceptable"],
|
196 |
+
"label_column": "is_acceptable",
|
197 |
+
"citation": textwrap.dedent(
|
198 |
+
"""\
|
199 |
+
@article{warstadt2018neural,
|
200 |
+
title={Neural Network Acceptability Judgments},
|
201 |
+
author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},
|
202 |
+
journal={arXiv preprint arXiv:1805.12471},
|
203 |
+
year={2018}
|
204 |
+
}"""
|
205 |
+
),
|
206 |
+
"description": textwrap.dedent(
|
207 |
+
"""\
|
208 |
+
The Corpus of Linguistic Acceptability consists of English
|
209 |
+
acceptability judgments drawn from books and journal articles on
|
210 |
+
linguistic theory. Each example is a sequence of words annotated
|
211 |
+
with whether it is a grammatical English sentence."""
|
212 |
+
),
|
213 |
+
},
|
214 |
+
"mnli": {
|
215 |
+
"name": "mnli",
|
216 |
+
"text_features": ["premise", "hypothesis"],
|
217 |
+
"label_classes": ["entailment", "neutral", "contradiction"],
|
218 |
+
"label_column": "gold_label",
|
219 |
+
"citation": textwrap.dedent(
|
220 |
+
"""\
|
221 |
+
@InProceedings{N18-1101,
|
222 |
+
author = "Williams, Adina
|
223 |
+
and Nangia, Nikita
|
224 |
+
and Bowman, Samuel",
|
225 |
+
title = "A Broad-Coverage Challenge Corpus for
|
226 |
+
Sentence Understanding through Inference",
|
227 |
+
booktitle = "Proceedings of the 2018 Conference of
|
228 |
+
the North American Chapter of the
|
229 |
+
Association for Computational Linguistics:
|
230 |
+
Human Language Technologies, Volume 1 (Long
|
231 |
+
Papers)",
|
232 |
+
year = "2018",
|
233 |
+
publisher = "Association for Computational Linguistics",
|
234 |
+
pages = "1112--1122",
|
235 |
+
location = "New Orleans, Louisiana",
|
236 |
+
url = "http://aclweb.org/anthology/N18-1101"
|
237 |
+
}
|
238 |
+
@article{bowman2015large,
|
239 |
+
title={A large annotated corpus for learning natural language inference},
|
240 |
+
author={Bowman, Samuel R and Angeli, Gabor and Potts, Christopher and Manning, Christopher D},
|
241 |
+
journal={arXiv preprint arXiv:1508.05326},
|
242 |
+
year={2015}
|
243 |
+
}"""
|
244 |
+
),
|
245 |
+
"description": textwrap.dedent(
|
246 |
+
"""\
|
247 |
+
The Multi-Genre Natural Language Inference Corpus is a crowdsourced
|
248 |
+
collection of sentence pairs with textual entailment annotations. Given a premise sentence
|
249 |
+
and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis
|
250 |
+
(entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are
|
251 |
+
gathered from ten different sources, including transcribed speech, fiction, and government reports.
|
252 |
+
We use the standard test set, for which we obtained private labels from the authors, and evaluate
|
253 |
+
on both the matched (in-domain) and mismatched (cross-domain) section. We also use and recommend
|
254 |
+
the SNLI corpus as 550k examples of auxiliary training data."""
|
255 |
+
),
|
256 |
+
},
|
257 |
+
"mrpc": {
|
258 |
+
"name": "mrpc",
|
259 |
+
"text_features": ["sentence1", "sentence2"],
|
260 |
+
"label_classes": ["not_equivalent", "equivalent"],
|
261 |
+
"label_column": "Quality",
|
262 |
+
"citation": textwrap.dedent(
|
263 |
+
"""\
|
264 |
+
@inproceedings{dolan2005automatically,
|
265 |
+
title={Automatically constructing a corpus of sentential paraphrases},
|
266 |
+
author={Dolan, William B and Brockett, Chris},
|
267 |
+
booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)},
|
268 |
+
year={2005}
|
269 |
+
}"""
|
270 |
+
),
|
271 |
+
"description": textwrap.dedent(
|
272 |
+
"""\
|
273 |
+
The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of
|
274 |
+
sentence pairs automatically extracted from online news sources, with human annotations
|
275 |
+
for whether the sentences in the pair are semantically equivalent."""
|
276 |
+
), # pylint: disable=line-too-long
|
277 |
+
},
|
278 |
+
"qnli": {
|
279 |
+
"name": "qnli",
|
280 |
+
"text_features": ["question", "sentence"],
|
281 |
+
"label_classes": ["entailment", "not_entailment"],
|
282 |
+
"label_column": "label",
|
283 |
+
"citation": textwrap.dedent(
|
284 |
+
"""\
|
285 |
+
@article{rajpurkar2016squad,
|
286 |
+
title={Squad: 100,000+ questions for machine comprehension of text},
|
287 |
+
author={Rajpurkar, Pranav and Zhang, Jian and Lopyrev, Konstantin and Liang, Percy},
|
288 |
+
journal={arXiv preprint arXiv:1606.05250},
|
289 |
+
year={2016}
|
290 |
+
}"""
|
291 |
+
),
|
292 |
+
"description": textwrap.dedent(
|
293 |
+
"""\
|
294 |
+
The Stanford Question Answering Dataset is a question-answering
|
295 |
+
dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn
|
296 |
+
from Wikipedia) contains the answer to the corresponding question (written by an annotator). We
|
297 |
+
convert the task into sentence pair classification by forming a pair between each question and each
|
298 |
+
sentence in the corresponding context, and filtering out pairs with low lexical overlap between the
|
299 |
+
question and the context sentence. The task is to determine whether the context sentence contains
|
300 |
+
the answer to the question. This modified version of the original task removes the requirement that
|
301 |
+
the model select the exact answer, but also removes the simplifying assumptions that the answer
|
302 |
+
is always present in the input and that lexical overlap is a reliable cue."""
|
303 |
+
), # pylint: disable=line-too-long
|
304 |
+
},
|
305 |
+
"qqp": {
|
306 |
+
"name": "qqp",
|
307 |
+
"text_features": ["question1", "question2"],
|
308 |
+
"label_classes": ["not_duplicate", "duplicate"],
|
309 |
+
"label_column": "is_duplicate",
|
310 |
+
"citation": textwrap.dedent(
|
311 |
+
"""\
|
312 |
+
@online{WinNT,
|
313 |
+
author = {Iyer, Shankar and Dandekar, Nikhil and Csernai, Kornel},
|
314 |
+
title = {First Quora Dataset Release: Question Pairs},
|
315 |
+
year = {2017},
|
316 |
+
url = {https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs},
|
317 |
+
urldate = {2019-04-03}
|
318 |
+
}"""
|
319 |
+
),
|
320 |
+
"description": textwrap.dedent(
|
321 |
+
"""\
|
322 |
+
The Quora Question Pairs2 dataset is a collection of question pairs from the
|
323 |
+
community question-answering website Quora. The task is to determine whether a
|
324 |
+
pair of questions are semantically equivalent."""
|
325 |
+
),
|
326 |
+
},
|
327 |
+
"rte": {
|
328 |
+
"name": "rte",
|
329 |
+
"text_features": ["sentence1", "sentence2"],
|
330 |
+
"label_classes": ["entailment", "not_entailment"],
|
331 |
+
"label_column": "label",
|
332 |
+
"citation": textwrap.dedent(
|
333 |
+
"""\
|
334 |
+
@inproceedings{dagan2005pascal,
|
335 |
+
title={The PASCAL recognising textual entailment challenge},
|
336 |
+
author={Dagan, Ido and Glickman, Oren and Magnini, Bernardo},
|
337 |
+
booktitle={Machine Learning Challenges Workshop},
|
338 |
+
pages={177--190},
|
339 |
+
year={2005},
|
340 |
+
organization={Springer}
|
341 |
+
}
|
342 |
+
@inproceedings{bar2006second,
|
343 |
+
title={The second pascal recognising textual entailment challenge},
|
344 |
+
author={Bar-Haim, Roy and Dagan, Ido and Dolan, Bill and Ferro, Lisa and Giampiccolo, Danilo and Magnini, Bernardo and Szpektor, Idan},
|
345 |
+
booktitle={Proceedings of the second PASCAL challenges workshop on recognising textual entailment},
|
346 |
+
volume={6},
|
347 |
+
number={1},
|
348 |
+
pages={6--4},
|
349 |
+
year={2006},
|
350 |
+
organization={Venice}
|
351 |
+
}
|
352 |
+
@inproceedings{giampiccolo2007third,
|
353 |
+
title={The third pascal recognizing textual entailment challenge},
|
354 |
+
author={Giampiccolo, Danilo and Magnini, Bernardo and Dagan, Ido and Dolan, Bill},
|
355 |
+
booktitle={Proceedings of the ACL-PASCAL workshop on textual entailment and paraphrasing},
|
356 |
+
pages={1--9},
|
357 |
+
year={2007},
|
358 |
+
organization={Association for Computational Linguistics}
|
359 |
+
}
|
360 |
+
@inproceedings{bentivogli2009fifth,
|
361 |
+
title={The Fifth PASCAL Recognizing Textual Entailment Challenge.},
|
362 |
+
author={Bentivogli, Luisa and Clark, Peter and Dagan, Ido and Giampiccolo, Danilo},
|
363 |
+
booktitle={TAC},
|
364 |
+
year={2009}
|
365 |
+
}"""
|
366 |
+
),
|
367 |
+
"description": textwrap.dedent(
|
368 |
+
"""\
|
369 |
+
The Recognizing Textual Entailment (RTE) datasets come from a series of annual textual
|
370 |
+
entailment challenges. We combine the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim
|
371 |
+
et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009).4 Examples are
|
372 |
+
constructed based on news and Wikipedia text. We convert all datasets to a two-class split, where
|
373 |
+
for three-class datasets we collapse neutral and contradiction into not entailment, for consistency."""
|
374 |
+
), # pylint: disable=line-too-long
|
375 |
+
},
|
376 |
+
"sst2": {
|
377 |
+
"name": "sst2",
|
378 |
+
"text_features": ["sentence"],
|
379 |
+
"label_classes": ["negative", "positive"],
|
380 |
+
"label_column": "label",
|
381 |
+
"citation": textwrap.dedent(
|
382 |
+
"""\
|
383 |
+
@inproceedings{socher2013recursive,
|
384 |
+
title={Recursive deep models for semantic compositionality over a sentiment treebank},
|
385 |
+
author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher},
|
386 |
+
booktitle={Proceedings of the 2013 conference on empirical methods in natural language processing},
|
387 |
+
pages={1631--1642},
|
388 |
+
year={2013}
|
389 |
+
}"""
|
390 |
+
),
|
391 |
+
"description": textwrap.dedent(
|
392 |
+
"""\
|
393 |
+
The Stanford Sentiment Treebank consists of sentences from movie reviews and
|
394 |
+
human annotations of their sentiment. The task is to predict the sentiment of a
|
395 |
+
given sentence. We use the two-way (positive/negative) class split, and use only
|
396 |
+
sentence-level labels."""
|
397 |
+
),
|
398 |
+
},
|
399 |
+
"stsb": {
|
400 |
+
"name": "stsb",
|
401 |
+
"text_features": ["sentence1", "sentence2"],
|
402 |
+
"label_classes": None,
|
403 |
+
"label_column": "score",
|
404 |
+
"citation": textwrap.dedent(
|
405 |
+
"""\
|
406 |
+
@inproceedings{cer2017semeval,
|
407 |
+
title={Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation},
|
408 |
+
author={Cer, Daniel and Diab, Mona and Agirre, Eneko and Lopez-Gazpio, Inigo and Specia, Lucia},
|
409 |
+
booktitle={Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
|
410 |
+
pages={1--14},
|
411 |
+
year={2017}
|
412 |
+
}"""
|
413 |
+
),
|
414 |
+
"description": textwrap.dedent(
|
415 |
+
"""\
|
416 |
+
The Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of
|
417 |
+
sentence pairs drawn from news headlines, video and image captions, and natural language
|
418 |
+
inference data. Each pair is human-annotated with a similarity score from 1 to 5. We
|
419 |
+
convert this to a binary classification task by labeling examples with a similarity score
|
420 |
+
>= 4.5 as entailment and < 4.5 as not entailment."""
|
421 |
+
),
|
422 |
+
"process_label": lambda x: float(x),
|
423 |
+
},
|
424 |
+
"wnli": {
|
425 |
+
"name": "wnli",
|
426 |
+
"text_features": ["sentence1", "sentence2"],
|
427 |
+
"label_classes": ["not_entailment", "entailment"],
|
428 |
+
"label_column": "label",
|
429 |
+
"citation": textwrap.dedent(
|
430 |
+
"""\
|
431 |
+
@inproceedings{levesque2012winograd,
|
432 |
+
title={The winograd schema challenge},
|
433 |
+
author={Levesque, Hector and Davis, Ernest and Morgenstern, Leora},
|
434 |
+
booktitle={Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning},
|
435 |
+
year={2012}
|
436 |
+
}"""
|
437 |
+
),
|
438 |
+
"description": textwrap.dedent(
|
439 |
+
"""\
|
440 |
+
The Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task
|
441 |
+
in which a system must read a sentence with a pronoun and select the referent of that pronoun from
|
442 |
+
a list of choices. The examples are manually constructed to foil simple statistical methods: Each
|
443 |
+
one is contingent on contextual information provided by a single word or phrase in the sentence.
|
444 |
+
To convert the problem into sentence pair classification, we construct sentence pairs by replacing
|
445 |
+
the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the
|
446 |
+
pronoun substituted is entailed by the original sentence. We use a small evaluation set consisting of
|
447 |
+
new examples derived from fiction books that was shared privately by the authors of the original
|
448 |
+
corpus. While the included training set is balanced between two classes, the test set is imbalanced
|
449 |
+
between them (65% not entailment). Also, due to a data quirk, the development set is adversarial:
|
450 |
+
hypotheses are sometimes shared between training and development examples, so if a model memorizes the
|
451 |
+
training examples, they will predict the wrong label on corresponding development set
|
452 |
+
example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence
|
453 |
+
between a model's score on this task and its score on the unconverted original task. We
|
454 |
+
call converted dataset WNLI (Winograd NLI)."""
|
455 |
+
),
|
456 |
+
},
|
457 |
+
}
|
458 |
+
|
459 |
+
|
460 |
+
_VERSION = datasets.Version("1.0.0", "")
|
461 |
+
|
462 |
+
|
463 |
+
class VieGLUEConfig(datasets.BuilderConfig):
|
464 |
+
"""BuilderConfig for GLUE."""
|
465 |
+
|
466 |
+
def __init__(
|
467 |
+
self,
|
468 |
+
text_features,
|
469 |
+
label_column="",
|
470 |
+
data_url="",
|
471 |
+
data_dir="",
|
472 |
+
citation="",
|
473 |
+
url="",
|
474 |
+
label_classes=None,
|
475 |
+
process_label=lambda x: x,
|
476 |
+
**kwargs,
|
477 |
+
):
|
478 |
+
"""BuilderConfig for VieGLUE.
|
479 |
+
Args:
|
480 |
+
text_features: `dict[string, string]`, map from the name of the feature
|
481 |
+
dict for each text field to the name of the column in the tsv file
|
482 |
+
label_column: `string`, name of the column in the tsv file corresponding
|
483 |
+
to the label
|
484 |
+
data_url: `string`, url to download the zip file from
|
485 |
+
data_dir: `string`, the path to the folder containing the tsv files in the
|
486 |
+
downloaded zip
|
487 |
+
citation: `string`, citation for the data set
|
488 |
+
url: `string`, url for information about the data set
|
489 |
+
label_classes: `list[string]`, the list of classes if the label is
|
490 |
+
categorical. If not provided, then the label will be of type
|
491 |
+
`datasets.Value('float32')`.
|
492 |
+
process_label: `Function[string, any]`, function taking in the raw value
|
493 |
+
of the label and processing it to the form required by the label feature
|
494 |
+
**kwargs: keyword arguments forwarded to super.
|
495 |
+
"""
|
496 |
+
super(VieGLUEConfig, self).__init__(
|
497 |
+
version=datasets.Version("1.0.0", ""), **kwargs
|
498 |
+
)
|
499 |
+
self.text_features = text_features
|
500 |
+
self.label_column = label_column
|
501 |
+
self.label_classes = label_classes
|
502 |
+
self.data_url = data_url
|
503 |
+
self.data_dir = data_dir
|
504 |
+
self.citation = citation
|
505 |
+
self.url = url
|
506 |
+
self.process_label = process_label
|
507 |
+
|
508 |
+
|
509 |
+
class VNExpress(datasets.GeneratorBasedBuilder):
|
510 |
+
""""""
|
511 |
+
|
512 |
+
VERSION = _VERSION
|
513 |
+
DEFAULT_CONFIG_NAME = "mnli"
|
514 |
+
|
515 |
+
BUILDER_CONFIGS = [VieGLUEConfig(**config) for config in SUBSET_KWARGS.values()]
|
516 |
+
|
517 |
+
def _info(self):
|
518 |
+
features = {f: datasets.Value("string") for f in self.config.text_features}
|
519 |
+
if self.config.label_classes:
|
520 |
+
features["label"] = datasets.features.ClassLabel(
|
521 |
+
names=self.config.label_classes
|
522 |
+
)
|
523 |
+
else:
|
524 |
+
features["label"] = datasets.Value("float32")
|
525 |
+
features["idx"] = datasets.Value("int32")
|
526 |
+
return datasets.DatasetInfo(
|
527 |
+
description=_DESCRIPTION,
|
528 |
+
features=datasets.Features(features),
|
529 |
+
homepage=self.config.url,
|
530 |
+
citation=self.config.citation + "\n" + _CITATION,
|
531 |
+
)
|
532 |
+
|
533 |
+
def _split_generators(self, dl_manager):
|
534 |
+
_SPLIT_MAPPING = {
|
535 |
+
"train": datasets.Split.TRAIN,
|
536 |
+
"training": datasets.Split.TRAIN,
|
537 |
+
"test": datasets.Split.TEST,
|
538 |
+
"testing": datasets.Split.TEST,
|
539 |
+
"val": datasets.Split.VALIDATION,
|
540 |
+
"validation": datasets.Split.VALIDATION,
|
541 |
+
"valid": datasets.Split.VALIDATION,
|
542 |
+
"dev": datasets.Split.VALIDATION,
|
543 |
+
}
|
544 |
+
|
545 |
+
name = self.config.name
|
546 |
+
download_url = _DOWNLOAD_URL[name]
|
547 |
+
filepath = dl_manager.download_and_extract(download_url)
|
548 |
+
|
549 |
+
return_datasets = []
|
550 |
+
for split in download_url:
|
551 |
+
return_datasets.append(
|
552 |
+
datasets.SplitGenerator(
|
553 |
+
name=_SPLIT_MAPPING[split],
|
554 |
+
gen_kwargs={
|
555 |
+
"files": filepath[split],
|
556 |
+
"urls": download_url[split],
|
557 |
+
"stage": split,
|
558 |
+
"config": self.config,
|
559 |
+
},
|
560 |
+
)
|
561 |
+
)
|
562 |
+
|
563 |
+
return return_datasets
|
564 |
+
|
565 |
+
def _generate_examples(self, files, urls, stage):
|
566 |
+
# id_ = 0
|
567 |
+
|
568 |
+
if not isinstance(files, list):
|
569 |
+
files = [files]
|
570 |
+
for path, url in zip(files, urls):
|
571 |
+
print(f"Loading file from {url}...")
|
572 |
+
for file in os.listdir(path):
|
573 |
+
if file.startswith("._"):
|
574 |
+
continue
|
575 |
+
file_path = os.path.join(path, file)
|
576 |
+
if not os.path.isfile(file_path):
|
577 |
+
continue
|
578 |
+
with open(file_path) as f:
|
579 |
+
all_samples = json.load(f)
|
580 |
+
for sample in all_samples:
|
581 |
+
yield sample
|
data/ax/test.tar.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ad60289d3dbd04204e9369cf7af380775c6c1d1aa6bcc14d9b7a8526c9a805e5
|
3 |
+
size 35767
|
data/cola/test.tar.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ca81585b0f058cbbf127e55c0e3ff502e5e3be8994e6bf7aa772499efec115da
|
3 |
+
size 23941
|
data/cola/train.tar.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
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oid sha256:5e26435b2297444d6f5e6849da66e0c9bf6dd3966337f4ebc6605e1c5b169b1f
|
3 |
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size 157789
|
data/cola/validation.tar.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
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oid sha256:6c5199213012f68aac23490c14a7d9c9e1808792a42b132373ba0e6289e33af5
|
3 |
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size 24119
|
data/mnli/test_matched.tar.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
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oid sha256:3a50fafcfcec9cd51e48f40e86ad97254abd764d4dbf3f4df2fe43f488bd2715
|
3 |
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size 804164
|
data/mnli/test_mismatched.tar.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
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version https://git-lfs.github.com/spec/v1
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|
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size 819403
|
data/mnli/train.tar.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
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oid sha256:03497c968e0a15eb9abd10fc224c7627d7d0e84ee6993a1cc0cdd21cf18925c7
|
3 |
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size 33001627
|
data/mnli/validation_matched.tar.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
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oid sha256:6bf263094bb5f66a9568c6afdff15515315f607196702a6864f9b60029d34f6f
|
3 |
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size 804245
|
data/mnli/validation_mismatched.tar.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
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oid sha256:616502498babe0591ba75c4ea02b186fa3deccc00239f05df956cefe44d94586
|
3 |
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size 820579
|
data/mrpc/test.tar.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:0db537bbbd376c82c18550018f0d41ab2c8fbc431e3057131f0d7b6b3504f1fc
|
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size 171292
|
data/mrpc/train.tar.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:246d993ce4af74b2df0e7e54dcbf5cd9a9f208ac8de7f91d888b2000abb319c0
|
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size 361063
|
data/mrpc/validation.tar.gz
ADDED
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:2c931958f8bd19f69cd8da866386ed1225d1d55b7cb933ff32a7e57b6d66ae08
|
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size 41857
|
data/qnli/test.tar.gz
ADDED
@@ -0,0 +1,3 @@
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|
|
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|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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|
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size 597166
|
data/qnli/train.tar.gz
ADDED
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|
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version https://git-lfs.github.com/spec/v1
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|
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size 11431368
|
data/qnli/validation.tar.gz
ADDED
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|
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version https://git-lfs.github.com/spec/v1
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|
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size 594909
|
data/qqp/test.tar.gz
ADDED
@@ -0,0 +1,3 @@
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|
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|
1 |
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version https://git-lfs.github.com/spec/v1
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|
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size 23005123
|
data/qqp/train.tar.gz
ADDED
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|
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version https://git-lfs.github.com/spec/v1
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size 20740924
|
data/qqp/validation.tar.gz
ADDED
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|
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version https://git-lfs.github.com/spec/v1
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|
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size 2304652
|
data/rte/test.tar.gz
ADDED
@@ -0,0 +1,3 @@
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|
|
|
|
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|
|
|
1 |
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version https://git-lfs.github.com/spec/v1
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|
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size 405248
|
data/rte/train.tar.gz
ADDED
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|
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version https://git-lfs.github.com/spec/v1
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|
data/rte/validation.tar.gz
ADDED
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|
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version https://git-lfs.github.com/spec/v1
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size 43009
|
data/sst2/test.tar.gz
ADDED
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|
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version https://git-lfs.github.com/spec/v1
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|
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size 100475
|
data/sst2/train.tar.gz
ADDED
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|
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version https://git-lfs.github.com/spec/v1
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|
data/sst2/validation.tar.gz
ADDED
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|
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|
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|
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version https://git-lfs.github.com/spec/v1
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|
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|
data/stsb/test.tar.gz
ADDED
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|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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|
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|
data/stsb/train.tar.gz
ADDED
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|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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|
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size 300955
|
data/stsb/validation.tar.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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|
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|
data/wnli/test.tar.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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|
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|
data/wnli/train.tar.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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|
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|
data/wnli/validation.tar.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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|
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