File size: 19,808 Bytes
9b65a04 28c621e 9b65a04 28c621e 9b65a04 28c621e 9b65a04 28c621e 9b65a04 28c621e 9b65a04 28c621e 5150720 28c621e 9b65a04 28c621e 9b65a04 bfcbdd5 9b65a04 9e76666 9b65a04 873d6ed 9b65a04 873d6ed 9e76666 9b65a04 28c621e 9b65a04 28c621e 9b65a04 28c621e 9b65a04 28c621e 9b65a04 28c621e 9b65a04 28c621e 9b65a04 28c621e 9b65a04 28c621e 9b65a04 28c621e 9b65a04 28c621e 9b65a04 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 |
# für kompletten text tokens mit labels liefern
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""
import json
from pathlib import Path
import datasets
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {a fancy dataset},
author={Hugo Meinhof, Elisa Luebbers},
year={2024}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This dataset contains 402 argumentative essays from non-native """
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
# _URLS = {
# "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
# "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
# }
class Fancy(datasets.GeneratorBasedBuilder):
"""
TODO: Short description of my dataset.
"""
VERSION = datasets.Version("1.1.0")
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
# If you need to make complex sub-parts in the datasets with configurable options
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
# BUILDER_CONFIG_CLASS = MyBuilderConfig
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('my_dataset', 'first_domain')
# data = datasets.load_dataset('my_dataset', 'second_domain')
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="full_labels",
version=VERSION,
description="get all the data conveyed by the labels, O, B-Claim, I-Claim, etc.",
),
datasets.BuilderConfig(
name="spans",
version=VERSION,
description="get the spans, O, B-Span, I-Span.",
),
datasets.BuilderConfig(
name="simple",
version=VERSION,
description="get the labels without B/I, O, MajorClaim, Claim, Premise",
),
datasets.BuilderConfig(
name="sep_tok",
version=VERSION,
description="get the labels without B/I, meaning O, Claim, Premise"
+ ", etc.\n insert seperator tokens <s> ... </s>",
),
datasets.BuilderConfig(
name="sep_tok_full_labels",
version=VERSION,
description="get the labels with B/I, meaning O, I-Claim, I-Premise"
+ ", etc.\n insert seperator tokens <s> ... </s>",
),
]
DEFAULT_CONFIG_NAME = "full_labels"
def _info(self):
# This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
if (
self.config.name == "full_labels"
): # This is the name of the configuration selected in BUILDER_CONFIGS above
features = datasets.Features(
{
"id": datasets.Value("int16"),
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.ClassLabel(
names=[
"O",
"B-MajorClaim",
"I-MajorClaim",
"B-Claim",
"I-Claim",
"B-Premise",
"I-Premise",
]
)
),
"text": datasets.Value("string"),
"span_begins": datasets.Sequence(datasets.Value("int16")),
"span_ends": datasets.Sequence(datasets.Value("int16")),
}
)
elif (
self.config.name == "spans"
): # This is an example to show how to have different features for "first_domain" and "second_domain"
features = datasets.Features(
{
"id": datasets.Value("int16"),
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.ClassLabel(
names=[
"O",
"B",
"I",
]
)
),
"text": datasets.Value("string"),
"span_begins": datasets.Sequence(datasets.Value("int16")),
"span_ends": datasets.Sequence(datasets.Value("int16")),
}
)
elif (
self.config.name == "simple"
): # This is an example to show how to have different features for "first_domain" and "second_domain"
features = datasets.Features(
{
"id": datasets.Value("int16"),
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.ClassLabel(
names=[
"O",
"X_placeholder_X",
"MajorClaim",
"Claim",
"Premise",
]
)
),
"text": datasets.Value("string"),
"span_begins": datasets.Sequence(datasets.Value("int16")),
"span_ends": datasets.Sequence(datasets.Value("int16")),
}
)
elif self.config.name == "sep_tok":
features = datasets.Features(
{
"id": datasets.Value("int16"),
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.ClassLabel(
names=[
"O",
"X_placeholder_X",
"MajorClaim",
"Claim",
"Premise",
]
)
),
"text": datasets.Value("string"),
"span_begins": datasets.Sequence(datasets.Value("int16")),
"span_ends": datasets.Sequence(datasets.Value("int16")),
}
)
elif self.config.name == "sep_tok_full_labels":
features = datasets.Features(
{
"id": datasets.Value("int16"),
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.ClassLabel(
names=[
"O",
"B-MajorClaim",
"I-MajorClaim",
"B-Claim",
"I-Claim",
"B-Premise",
"I-Premise",
]
)
),
"text": datasets.Value("string"),
"span_begins": datasets.Sequence(datasets.Value("int16")),
"span_ends": datasets.Sequence(datasets.Value("int16")),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _range_generator(self, train=0.8, test=0.2):
"""
returns three range objects to access the list of essays
these are the train, test, and validate range, where the size of the
validation range is dictated by the other two ranges
"""
return (
range(0, int(402 * train)), # train
range(int(402 * train), int(402 * (train + test))), # test
range(int(402 * (train + test)), 402), # validate
)
@staticmethod
def _find_data():
"""
try to find the data folder and return the path to it if found,
otherwise return none
returns:
path to data folder or None
"""
# get path to the current working directory
cwd = Path.cwd()
# check for whether the data folder is in cwd.
# if it isnt, change cwd to its parent directory
# do this three times only (dont want infinite recursion)
for _ in range(5):
# if Path.is_dir(cwd / "essays_SuG"):
# print(f"found 'essays_SuG' folder at {cwd}")
# # input(f"returning {cwd / 'essays_SuG'}")
# return cwd / "essays_SuG"
if Path.is_dir(cwd / "data"):
print(f"found 'data' folder at {cwd}")
# input(f"returning {cwd / 'data'}")
return cwd / "data"
cwd = cwd.parent
raise FileNotFoundError("data directory has not been found")
def _get_essay_list(self):
"""
read the essay.json and return a list of dicts, where each dict is an essay
"""
path = self._find_data() / "essay.json"
with open(path, "r") as r:
lines = r.readlines()
essays = []
for line in lines:
essays.append(json.loads(line))
return essays
def _split_generators(self, dl_manager):
# This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
# this dataset will return a "train" split only, allowing for
# 5-fold cross-validation
train, test, validate = self._range_generator(1, 0)
essays = self._get_essay_list()
if len(validate) > 0 and len(test) > 0:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"data": essays,
"id_range": train,
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"data": essays,
"id_range": validate,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"data": essays,
"id_range": test,
},
),
]
elif len(test) > 0:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"data": essays,
"id_range": train,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"data": essays,
"id_range": test,
},
),
]
elif len(validate) > 0:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"data": essays,
"id_range": train,
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"data": essays,
"id_range": validate,
},
),
]
else:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"data": essays,
"id_range": train,
},
),
]
def _get_id(self, essay):
return int(essay["docID"].split("_")[-1])
def _get_tokens(self, essay):
tokens = []
for sentence in essay["sentences"]:
for token in sentence["tokens"]:
tokens.append(
(
token["surface"],
token["gid"],
token["characterOffsetBegin"],
token["characterOffsetEnd"],
)
)
return tokens
def _get_label_dict(self, essay):
label_dict = {}
for unit in essay["argumentation"]["units"]:
if self.config.name == "spans":
label = "Span"
else:
label = unit["attributes"]["role"]
for i, gid in enumerate(unit["tokens"]):
if i == 0:
location = "B-"
else:
location = "I-"
label_dict[gid] = location + label
return label_dict
def _match_tokens(self, tokens, label_dict):
text = []
labels = []
begins = []
ends = []
last_end = 0
for surface, gid, begin, end in tokens:
# for each token, unpack it into its surface and gid
# then match the gid to the label and pack them back together
if label_dict.get(gid, "O")[0] == "B":
# if we are at the beginning of a span
# insert begin of sequence token (BOS) and "O" label
if (
self.config.name == "sep_tok"
or self.config.name == "sep_tok_full_labels"
):
# if the config requires separator tokens
text.append("<s>")
labels.append("O")
begins.append(begin)
elif (
label_dict.get(gid, "O") == "O"
and len(labels) != 0
and labels[-1][0] != "O"
):
# if we are not in a span, and the previous label was
# of a span
# intert end of sequence token (EOS) and "O" label
if (
self.config.name == "sep_tok"
or self.config.name == "sep_tok_full_labels"
):
# if the config requires separator tokens
text.append("</s>")
labels.append("O")
ends.append(last_end)
# always append the surface form
text.append(surface)
last_end = end
# append the correct type of label, depending on the config
if self.config.name == "full_labels":
labels.append(label_dict.get(gid, "O"))
elif self.config.name == "spans":
labels.append(label_dict.get(gid, "O")[0])
elif self.config.name == "simple":
labels.append(label_dict.get(gid, "__O")[2:])
elif self.config.name == "sep_tok":
labels.append(label_dict.get(gid, "__O")[2:])
elif self.config.name == "sep_tok_full_labels":
labels.append(label_dict.get(gid, "O"))
else:
raise KeyError()
return text, labels, begins, ends
def _get_text(self, essay):
return essay["text"]
def _process_essay(self, essay):
id = self._get_id(essay)
# input(id)
tokens = self._get_tokens(essay)
# input(tokens)
label_dict = self._get_label_dict(essay)
# input(label_dict)
tokens, labels, begins, ends = self._match_tokens(tokens, label_dict)
# input(tokens)
# input(labels)
text = self._get_text(essay)
return {
"id": id,
"tokens": tokens,
"ner_tags": labels,
"text": text,
"span_begins": begins,
"span_ends": ends,
}
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, data, id_range):
# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
for id in id_range:
# input(data[id])
yield id, self._process_essay(data[id])
|