File size: 16,521 Bytes
9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 3bb3188 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 3bb3188 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 9f36bd8 8910c0a 3bb3188 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 |
# coding=utf-8
# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""Dynabench.DynaSent"""
from __future__ import absolute_import, division, print_function
import json
import os
from collections import OrderedDict
import datasets
logger = datasets.logging.get_logger(__name__)
_VERSION = datasets.Version("1.1.0") # v1.1 fixed for example uid.
_NUM_ROUNDS = 2
_DESCRIPTION = """\
Dynabench.DynaSent is a Sentiment Analysis dataset collected using a
human-and-model-in-the-loop.
""".strip()
class DynabenchRoundDetails:
"""Round details for Dynabench.DynaSent datasets."""
def __init__(
self, citation, description, homepage, data_license, data_url,
data_features, data_subset_map=None
):
self.citation = citation
self.description = description
self.homepage = homepage
self.data_license = data_license
self.data_url = data_url
self.data_features = data_features
self.data_subset_map = data_subset_map
# Provide the details for each round
_ROUND_DETAILS = {
1: DynabenchRoundDetails(
citation="""\
@article{
potts-etal-2020-dynasent,
title={{DynaSent}: A Dynamic Benchmark for Sentiment Analysis},
author={Potts, Christopher and Wu, Zhengxuan and Geiger, Atticus
and Kiela, Douwe},
journal={arXiv preprint arXiv:2012.15349},
url={https://arxiv.org/abs/2012.15349},
year={2020}
}
""".strip(),
description="""\
DynaSent is an English-language benchmark task for ternary
(positive/negative/neutral) sentiment analysis.
For more details on the dataset construction process,
see https://github.com/cgpotts/dynasent.
""".strip(),
homepage="https://dynabench.org/tasks/3",
data_license="CC BY 4.0",
data_url="https://github.com/cgpotts/dynasent/raw/main/dynasent-v1.1.zip",
data_features=datasets.Features(
{
"id": datasets.Value("string"),
"hit_ids": datasets.features.Sequence(
datasets.Value("string")
),
"sentence": datasets.Value("string"),
"indices_into_review_text": datasets.features.Sequence(
datasets.Value("int32")
),
"model_0_label": datasets.Value("string"),
"model_0_probs": {
"negative": datasets.Value("float32"),
"positive": datasets.Value("float32"),
"neutral": datasets.Value("float32")
},
"text_id": datasets.Value("string"),
"review_id": datasets.Value("string"),
"review_rating": datasets.Value("int32"),
"label_distribution": {
"positive": datasets.features.Sequence(
datasets.Value("string")
),
"negative": datasets.features.Sequence(
datasets.Value("string")
),
"neutral": datasets.features.Sequence(
datasets.Value("string")
),
"mixed": datasets.features.Sequence(
datasets.Value("string")
)
},
"gold_label": datasets.Value("string"),
"metadata": {
"split": datasets.Value("string"),
"round": datasets.Value("int32"),
"subset": datasets.Value("string"),
"model_in_the_loop": datasets.Value("string"),
}
}
),
data_subset_map=OrderedDict({
"all": {
"dir": "dynasent-v1.1",
"file_prefix": "dynasent-v1.1-round01-yelp-",
"model": "RoBERTa"
}
}),
),
2: DynabenchRoundDetails(
citation="""\
@article{
potts-etal-2020-dynasent,
title={{DynaSent}: A Dynamic Benchmark for Sentiment Analysis},
author={Potts, Christopher and Wu, Zhengxuan and Geiger, Atticus
and Kiela, Douwe},
journal={arXiv preprint arXiv:2012.15349},
url={https://arxiv.org/abs/2012.15349},
year={2020}
}
""".strip(),
description="""\
DynaSent is an English-language benchmark task for ternary
(positive/negative/neutral) sentiment analysis.
For more details on the dataset construction process,
see https://github.com/cgpotts/dynasent.
""".strip(),
homepage="https://dynabench.org/tasks/3",
data_license="CC BY 4.0",
data_url="https://github.com/cgpotts/dynasent/raw/main/dynasent-v1.1.zip",
data_features=datasets.Features(
{
"id": datasets.Value("string"),
"hit_ids": datasets.features.Sequence(
datasets.Value("string")
),
"sentence": datasets.Value("string"),
"sentence_author": datasets.Value("string"),
"has_prompt": datasets.Value("bool"),
"prompt_data": {
"indices_into_review_text": datasets.features.Sequence(
datasets.Value("int32")
),
"review_rating": datasets.Value("int32"),
"prompt_sentence": datasets.Value("string"),
"review_id": datasets.Value("string")
},
"model_1_label": datasets.Value("string"),
"model_1_probs": {
"negative": datasets.Value("float32"),
"positive": datasets.Value("float32"),
"neutral": datasets.Value("float32")
},
"text_id": datasets.Value("string"),
"label_distribution": {
"positive": datasets.features.Sequence(
datasets.Value("string")
),
"negative": datasets.features.Sequence(
datasets.Value("string")
),
"neutral": datasets.features.Sequence(
datasets.Value("string")
),
"mixed": datasets.features.Sequence(
datasets.Value("string")
)
},
"gold_label": datasets.Value("string"),
"metadata": {
"split": datasets.Value("string"),
"round": datasets.Value("int32"),
"subset": datasets.Value("string"),
"model_in_the_loop": datasets.Value("string"),
}
}
),
data_subset_map=OrderedDict({
"all": {
"dir": "dynasent-v1.1",
"file_prefix": "dynasent-v1.1-round02-dynabench-",
"model": "RoBERTa"
}
}),
)
}
class DynabenchDynaSentConfig(datasets.BuilderConfig):
"""BuilderConfig for Dynabench.DynaSent datasets."""
def __init__(self, round, subset='all', **kwargs):
"""BuilderConfig for Dynabench.DynaSent.
Args:
round: integer, the dynabench round to load.
subset: string, the subset of that round's data to load or 'all'.
**kwargs: keyword arguments forwarded to super.
"""
assert isinstance(round, int), "round ({}) must be set and of type integer".format(round)
assert 0 < round <= _NUM_ROUNDS, \
"round (received {}) must be between 1 and {}".format(round, _NUM_ROUNDS)
super(DynabenchDynaSentConfig, self).__init__(
name="dynabench.dynasent.r{}.{}".format(round, subset),
description="Dynabench DynaSent dataset for round {}, showing dataset selection: {}.".format(round, subset),
**kwargs,
)
self.round = round
self.subset = subset
class DynabenchDynaSent(datasets.GeneratorBasedBuilder):
"""Dynabench.DynaSent"""
BUILDER_CONFIG_CLASS = DynabenchDynaSentConfig
BUILDER_CONFIGS = [
DynabenchDynaSentConfig(
version=_VERSION,
round=round,
subset=subset,
) # pylint:disable=g-complex-comprehension
for round in range(1, _NUM_ROUNDS+1) for subset in _ROUND_DETAILS[round].data_subset_map
]
def _info(self):
round_details = _ROUND_DETAILS[self.config.round]
return datasets.DatasetInfo(
description=round_details.description,
features=round_details.data_features,
homepage=round_details.homepage,
citation=round_details.citation,
supervised_keys=None
)
@staticmethod
def _get_filepath(dl_dir, round, subset, split):
round_details = _ROUND_DETAILS[round]
return os.path.join(
dl_dir,
round_details.data_subset_map[subset]["dir"],
round_details.data_subset_map[subset]["file_prefix"] + split + ".jsonl"
)
def _split_generators(self, dl_manager):
round_details = _ROUND_DETAILS[self.config.round]
dl_dir = dl_manager.download_and_extract(round_details.data_url)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": self._get_filepath(
dl_dir, self.config.round, self.config.subset, "train"
),
"split": "train",
"round": self.config.round,
"subset": self.config.subset,
"model_in_the_loop": round_details.data_subset_map[self.config.subset]["model"],
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": self._get_filepath(
dl_dir, self.config.round, self.config.subset, "dev"
),
"split": "validation",
"round": self.config.round,
"subset": self.config.subset,
"model_in_the_loop": round_details.data_subset_map[self.config.subset]["model"],
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": self._get_filepath(
dl_dir, self.config.round, self.config.subset, "test"
),
"split": "test",
"round": self.config.round,
"subset": self.config.subset,
"model_in_the_loop": round_details.data_subset_map[self.config.subset]["model"],
},
),
]
def _generate_examples(self, filepath, split, round, subset, model_in_the_loop):
"""This function returns the examples in the raw (text) form."""
ternary_labels = ('positive', 'negative', 'neutral') # Enforce to be the tenary version now.
logger.info("generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
for line in f:
d = json.loads(line)
if d['gold_label'] in ternary_labels:
if round == 1:
# Construct DynaSent features.
yield d["text_id"], {
"id": d["text_id"],
# DynaSent Example.
"hit_ids": d["hit_ids"],
"sentence": d["sentence"],
"indices_into_review_text": d["indices_into_review_text"],
"model_0_label": d["model_0_label"],
"model_0_probs": d["model_0_probs"],
"text_id": d["text_id"],
"review_id": d["review_id"],
"review_rating": d["review_rating"],
"label_distribution": d["label_distribution"],
"gold_label": d["gold_label"],
# Metadata.
"metadata": {
"split": split,
"round": round,
"subset": subset,
"model_in_the_loop": model_in_the_loop
}
}
elif round == 2:
# Construct DynaSent features.
if d["has_prompt"]:
if "indices_into_review_text" in d["prompt_data"]:
indices_into_review_text = d["prompt_data"]["indices_into_review_text"]
else:
indices_into_review_text = []
if "review_rating" in d["prompt_data"]:
review_rating = d["prompt_data"]["review_rating"]
else:
review_rating = -1 # -1 means unknown.
if "review_id" in d["prompt_data"]:
review_id = d["prompt_data"]["review_id"]
else:
review_id = ""
if "prompt_sentence" in d["prompt_data"]:
prompt_sentence = d["prompt_data"]["prompt_sentence"]
else:
prompt_sentence = ""
prompt_data = {
"indices_into_review_text": indices_into_review_text,
"review_rating": review_rating,
"prompt_sentence": prompt_sentence,
"review_id": review_id,
}
else:
prompt_data = {
"indices_into_review_text": [],
"review_rating": -1, # -1 means unknown.
"prompt_sentence": "",
"review_id": "",
}
yield d["text_id"], {
"id": d["text_id"],
# DynaSent Example.
"hit_ids": d["hit_ids"],
"sentence": d["sentence"],
"sentence_author": d["sentence_author"],
"has_prompt": d["has_prompt"],
"prompt_data": prompt_data,
"model_1_label": d["model_1_label"],
"model_1_probs": d["model_1_probs"],
"text_id": d["text_id"],
"label_distribution": d["label_distribution"],
"gold_label": d["gold_label"],
# Metadata.
"metadata": {
"split": split,
"round": round,
"subset": subset,
"model_in_the_loop": model_in_the_loop
}
} |