Datasets:
File size: 33,235 Bytes
94fb979 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 |
# coding=utf-8
# 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.
"""The IndoNLU benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems for Bahasa Indonesia"""
from __future__ import absolute_import, division, print_function
import ast
import csv
import textwrap
import six
import datasets
_INDONLU_CITATION = """\
@inproceedings{wilie2020indonlu,
title = {{IndoNLU}: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding},
authors={Bryan Wilie and Karissa Vincentio and Genta Indra Winata and Samuel Cahyawijaya and X. Li and Zhi Yuan Lim and S. Soleman and R. Mahendra and Pascale Fung and Syafri Bahar and A. Purwarianti},
booktitle={Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing},
year={2020}
}
"""
_INDONLU_DESCRIPTION = """\
The IndoNLU benchmark is a collection of resources for training, evaluating, \
and analyzing natural language understanding systems for Bahasa Indonesia.
"""
_INDONLU_HOMEPAGE = "https://www.indobenchmark.com/"
_INDONLU_LICENSE = "https://raw.githubusercontent.com/indobenchmark/indonlu/master/LICENSE"
class IndonluConfig(datasets.BuilderConfig):
"""BuilderConfig for IndoNLU"""
def __init__(
self,
text_features,
label_column,
label_classes,
train_url,
valid_url,
test_url,
citation,
**kwargs,
):
"""BuilderConfig for IndoNLU.
Args:
text_features: `dict[string, string]`, map from the name of the feature
dict for each text field to the name of the column in the txt/csv/tsv file
label_column: `string`, name of the column in the txt/csv/tsv file corresponding
to the label
label_classes: `list[string]`, the list of classes if the label is categorical
train_url: `string`, url to train file from
valid_url: `string`, url to valid file from
test_url: `string`, url to test file from
citation: `string`, citation for the data set
**kwargs: keyword arguments forwarded to super.
"""
super(IndonluConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
self.text_features = text_features
self.label_column = label_column
self.label_classes = label_classes
self.train_url = train_url
self.valid_url = valid_url
self.test_url = test_url
self.citation = citation
class Indonlu(datasets.GeneratorBasedBuilder):
"""Indonesian Natural Language Understanding (IndoNLU) benchmark"""
BUILDER_CONFIGS = [
IndonluConfig(
name="emot",
description=textwrap.dedent(
"""\
An emotion classification dataset collected from the social media
platform Twitter (Saputri et al., 2018). The dataset consists of
around 4000 Indonesian colloquial language tweets, covering five
different emotion labels: sadness, anger, love, fear, and happy."""
),
text_features={"tweet": "tweet"},
# label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
label_classes=["sadness", "anger", "love", "fear", "happy"],
label_column="label",
train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/emot_emotion-twitter/train_preprocess.csv",
valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/emot_emotion-twitter/valid_preprocess.csv",
test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/emot_emotion-twitter/test_preprocess_masked_label.csv",
citation=textwrap.dedent(
"""\
@inproceedings{saputri2018emotion,
title={Emotion Classification on Indonesian Twitter Dataset},
author={Mei Silviana Saputri, Rahmad Mahendra, and Mirna Adriani},
booktitle={Proceedings of the 2018 International Conference on Asian Language Processing(IALP)},
pages={90--95},
year={2018},
organization={IEEE}
}"""
),
),
IndonluConfig(
name="smsa",
description=textwrap.dedent(
"""\
This sentence-level sentiment analysis dataset (Purwarianti and Crisdayanti, 2019)
is a collection of comments and reviews in Indonesian obtained from multiple online
platforms. The text was crawled and then annotated by several Indonesian linguists
to construct this dataset. There are three possible sentiments on the SmSA
dataset: positive, negative, and neutral."""
),
text_features={"text": "text"},
# label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
label_classes=["positive", "neutral", "negative"],
label_column="label",
train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/smsa_doc-sentiment-prosa/train_preprocess.tsv",
valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/smsa_doc-sentiment-prosa/valid_preprocess.tsv",
test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/smsa_doc-sentiment-prosa/test_preprocess_masked_label.tsv",
citation=textwrap.dedent(
"""\
@inproceedings{purwarianti2019improving,
title={Improving Bi-LSTM Performance for Indonesian Sentiment Analysis Using Paragraph Vector},
author={Ayu Purwarianti and Ida Ayu Putu Ari Crisdayanti},
booktitle={Proceedings of the 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA)},
pages={1--5},
year={2019},
organization={IEEE}
}"""
),
),
IndonluConfig(
name="casa",
description=textwrap.dedent(
"""\
An aspect-based sentiment analysis dataset consisting of around a thousand car reviews collected
from multiple Indonesian online automobile platforms (Ilmania et al., 2018). The dataset covers
six aspects of car quality. We define the task to be a multi-label classification task, where
each label represents a sentiment for a single aspect with three possible values: positive,
negative, and neutral."""
),
text_features={"sentence": "sentence"},
# label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
label_classes=["negative", "neutral", "positive"],
label_column=["fuel", "machine", "others", "part", "price", "service"],
train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/casa_absa-prosa/train_preprocess.csv",
valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/casa_absa-prosa/valid_preprocess.csv",
test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/casa_absa-prosa/test_preprocess_masked_label.csv",
citation=textwrap.dedent(
"""\
@inproceedings{ilmania2018aspect,
title={Aspect Detection and Sentiment Classification Using Deep Neural Network for Indonesian Aspect-based Sentiment Analysis},
author={Arfinda Ilmania, Abdurrahman, Samuel Cahyawijaya, Ayu Purwarianti},
booktitle={Proceedings of the 2018 International Conference on Asian Language Processing(IALP)},
pages={62--67},
year={2018},
organization={IEEE}
}"""
),
),
IndonluConfig(
name="hoasa",
description=textwrap.dedent(
"""\
An aspect-based sentiment analysis dataset consisting of hotel reviews collected from the hotel
aggregator platform, AiryRooms (Azhar et al., 2019). The dataset covers ten different aspects of
hotel quality. Each review is labeled with a single sentiment label for each aspect. There are
four possible sentiment classes for each sentiment label: positive, negative, neutral, and
positive-negative. The positivenegative label is given to a review that contains multiple sentiments
of the same aspect but for different objects (e.g., cleanliness of bed and toilet)."""
),
text_features={"sentence": "sentence"},
# label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
label_classes=["neg", "neut", "pos", "neg_pos"],
label_column=[
"ac",
"air_panas",
"bau",
"general",
"kebersihan",
"linen",
"service",
"sunrise_meal",
"tv",
"wifi",
],
train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/hoasa_absa-airy/train_preprocess.csv",
valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/hoasa_absa-airy/valid_preprocess.csv",
test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/hoasa_absa-airy/test_preprocess_masked_label.csv",
citation=textwrap.dedent(
"""\
@inproceedings{azhar2019multi,
title={Multi-label Aspect Categorization with Convolutional Neural Networks and Extreme Gradient Boosting},
author={A. N. Azhar, M. L. Khodra, and A. P. Sutiono}
booktitle={Proceedings of the 2019 International Conference on Electrical Engineering and Informatics (ICEEI)},
pages={35--40},
year={2019}
}"""
),
),
IndonluConfig(
name="wrete",
description=textwrap.dedent(
"""\
The Wiki Revision Edits Textual Entailment dataset (Setya and Mahendra, 2018) consists of 450 sentence pairs
constructed from Wikipedia revision history. The dataset contains pairs of sentences and binary semantic
relations between the pairs. The data are labeled as entailed when the meaning of the second sentence can be
derived from the first one, and not entailed otherwise."""
),
text_features={
"premise": "premise",
"hypothesis": "hypothesis",
"category": "category",
},
# label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
label_classes=["NotEntail", "Entail_or_Paraphrase"],
label_column="label",
train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/wrete_entailment-ui/train_preprocess.csv",
valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/wrete_entailment-ui/valid_preprocess.csv",
test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/wrete_entailment-ui/test_preprocess_masked_label.csv",
citation=textwrap.dedent(
"""\
@inproceedings{setya2018semi,
title={Semi-supervised Textual Entailment on Indonesian Wikipedia Data},
author={Ken Nabila Setya and Rahmad Mahendra},
booktitle={Proceedings of the 2018 International Conference on Computational Linguistics and Intelligent Text Processing (CICLing)},
year={2018}
}"""
),
),
IndonluConfig(
name="posp",
description=textwrap.dedent(
"""\
This Indonesian part-of-speech tagging (POS) dataset (Hoesen and Purwarianti, 2018) is collected from Indonesian
news websites. The dataset consists of around 8000 sentences with 26 POS tags. The POS tag labels follow the
Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention."""
),
text_features={"tokens": "tokens"},
# label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
label_classes=[
"B-PPO",
"B-KUA",
"B-ADV",
"B-PRN",
"B-VBI",
"B-PAR",
"B-VBP",
"B-NNP",
"B-UNS",
"B-VBT",
"B-VBL",
"B-NNO",
"B-ADJ",
"B-PRR",
"B-PRK",
"B-CCN",
"B-$$$",
"B-ADK",
"B-ART",
"B-CSN",
"B-NUM",
"B-SYM",
"B-INT",
"B-NEG",
"B-PRI",
"B-VBE",
],
label_column="pos_tags",
train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/posp_pos-prosa/train_preprocess.txt",
valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/posp_pos-prosa/valid_preprocess.txt",
test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/posp_pos-prosa/test_preprocess_masked_label.txt",
citation=textwrap.dedent(
"""\
@inproceedings{hoesen2018investigating,
title={Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger},
author={Devin Hoesen and Ayu Purwarianti},
booktitle={Proceedings of the 2018 International Conference on Asian Language Processing (IALP)},
pages={35--38},
year={2018},
organization={IEEE}
}"""
),
),
IndonluConfig(
name="bapos",
description=textwrap.dedent(
"""\
This POS tagging dataset (Dinakaramani et al., 2014) contains about 1000 sentences, collected from the PAN Localization
Project. In this dataset, each word is tagged by one of 23 POS tag classes. Data splitting used in this benchmark follows
the experimental setting used by Kurniawan and Aji (2018)"""
),
text_features={"tokens": "tokens"},
# label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
label_classes=[
"B-PR",
"B-CD",
"I-PR",
"B-SYM",
"B-JJ",
"B-DT",
"I-UH",
"I-NND",
"B-SC",
"I-WH",
"I-IN",
"I-NNP",
"I-VB",
"B-IN",
"B-NND",
"I-CD",
"I-JJ",
"I-X",
"B-OD",
"B-RP",
"B-RB",
"B-NNP",
"I-RB",
"I-Z",
"B-CC",
"B-NEG",
"B-VB",
"B-NN",
"B-MD",
"B-UH",
"I-NN",
"B-PRP",
"I-SC",
"B-Z",
"I-PRP",
"I-OD",
"I-SYM",
"B-WH",
"B-FW",
"I-CC",
"B-X",
],
label_column="pos_tags",
train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/bapos_pos-idn/train_preprocess.txt",
valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/bapos_pos-idn/valid_preprocess.txt",
test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/bapos_pos-idn/test_preprocess_masked_label.txt",
citation=textwrap.dedent(
"""\
@inproceedings{dinakaramani2014designing,
title={Designing an Indonesian Part of Speech Tagset and Manually Tagged Indonesian Corpus},
author={Arawinda Dinakaramani, Fam Rashel, Andry Luthfi, and Ruli Manurung},
booktitle={Proceedings of the 2014 International Conference on Asian Language Processing (IALP)},
pages={66--69},
year={2014},
organization={IEEE}
}
@inproceedings{kurniawan2019toward,
title={Toward a Standardized and More Accurate Indonesian Part-of-Speech Tagging},
author={Kemal Kurniawan and Alham Fikri Aji},
booktitle={Proceedings of the 2018 International Conference on Asian Language Processing (IALP)},
pages={303--307},
year={2018},
organization={IEEE}
}"""
),
),
IndonluConfig(
name="terma",
description=textwrap.dedent(
"""\
This span-extraction dataset is collected from the hotel aggregator platform, AiryRooms (Septiandri and Sutiono, 2019;
Fernando et al., 2019). The dataset consists of thousands of hotel reviews, which each contain a span label for aspect
and sentiment words representing the opinion of the reviewer on the corresponding aspect. The labels use
Inside-Outside-Beginning (IOB) tagging representation with two kinds of tags, aspect and sentiment."""
),
text_features={"tokens": "tokens"},
# label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
label_classes=["I-SENTIMENT", "O", "I-ASPECT", "B-SENTIMENT", "B-ASPECT"],
label_column="seq_label",
train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/terma_term-extraction-airy/train_preprocess.txt",
valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/terma_term-extraction-airy/valid_preprocess.txt",
test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/terma_term-extraction-airy/test_preprocess_masked_label.txt",
citation=textwrap.dedent(
"""\
@article{winatmoko2019aspect,
title={Aspect and Opinion Term Extraction for Hotel Reviews Using Transfer Learning and Auxiliary Labels},
author={Yosef Ardhito Winatmoko, Ali Akbar Septiandri, Arie Pratama Sutiono},
journal={arXiv preprint arXiv:1909.11879},
year={2019}
}
@article{fernando2019aspect,
title={Aspect and Opinion Terms Extraction Using Double Embeddings and Attention Mechanism for Indonesian Hotel Reviews},
author={Jordhy Fernando, Masayu Leylia Khodra, Ali Akbar Septiandri},
journal={arXiv preprint arXiv:1908.04899},
year={2019}
}"""
),
),
IndonluConfig(
name="keps",
description=textwrap.dedent(
"""\
This keyphrase extraction dataset (Mahfuzh et al., 2019) consists of text from Twitter discussing
banking products and services and is written in the Indonesian language. A phrase containing
important information is considered a keyphrase. Text may contain one or more keyphrases since
important phrases can be located at different positions. The dataset follows the IOB chunking format,
which represents the position of the keyphrase."""
),
text_features={"tokens": "tokens"},
# label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
label_classes=["O", "B", "I"],
label_column="seq_label",
train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/keps_keyword-extraction-prosa/train_preprocess.txt",
valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/keps_keyword-extraction-prosa/valid_preprocess.txt",
test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/keps_keyword-extraction-prosa/test_preprocess_masked_label.txt",
citation=textwrap.dedent(
"""\
@inproceedings{mahfuzh2019improving,
title={Improving Joint Layer RNN based Keyphrase Extraction by Using Syntactical Features},
author={Miftahul Mahfuzh, Sidik Soleman, and Ayu Purwarianti},
booktitle={Proceedings of the 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA)},
pages={1--6},
year={2019},
organization={IEEE}
}"""
),
),
IndonluConfig(
name="nergrit",
description=textwrap.dedent(
"""\
This NER dataset is taken from the Grit-ID repository, and the labels are spans in IOB chunking representation.
The dataset consists of three kinds of named entity tags, PERSON (name of person), PLACE (name of location), and
ORGANIZATION (name of organization)."""
),
text_features={"tokens": "tokens"},
# label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
label_classes=["I-PERSON", "B-ORGANISATION", "I-ORGANISATION", "B-PLACE", "I-PLACE", "O", "B-PERSON"],
label_column="ner_tags",
train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/nergrit_ner-grit/train_preprocess.txt",
valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/nergrit_ner-grit/valid_preprocess.txt",
test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/nergrit_ner-grit/test_preprocess_masked_label.txt",
citation=textwrap.dedent(
"""\
@online{nergrit2019,
title={NERGrit Corpus},
author={NERGrit Developers},
year={2019},
url={https://github.com/grit-id/nergrit-corpus}
}"""
),
),
IndonluConfig(
name="nerp",
description=textwrap.dedent(
"""\
This NER dataset (Hoesen and Purwarianti, 2018) contains texts collected from several Indonesian news websites.
There are five labels available in this dataset, PER (name of person), LOC (name of location), IND (name of product or brand),
EVT (name of the event), and FNB (name of food and beverage). The NERP dataset uses the IOB chunking format."""
),
text_features={"tokens": "tokens"},
# label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
label_classes=[
"I-PPL",
"B-EVT",
"B-PLC",
"I-IND",
"B-IND",
"B-FNB",
"I-EVT",
"B-PPL",
"I-PLC",
"O",
"I-FNB",
],
label_column="ner_tags",
train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/nerp_ner-prosa/train_preprocess.txt",
valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/nerp_ner-prosa/valid_preprocess.txt",
test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/nerp_ner-prosa/test_preprocess_masked_label.txt",
citation=textwrap.dedent(
"""\
@inproceedings{hoesen2018investigating,
title={Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger},
author={Devin Hoesen and Ayu Purwarianti},
booktitle={Proceedings of the 2018 International Conference on Asian Language Processing (IALP)},
pages={35--38},
year={2018},
organization={IEEE}
}"""
),
),
IndonluConfig(
name="facqa",
description=textwrap.dedent(
"""\
The goal of the FacQA dataset is to find the answer to a question from a provided short passage from
a news article (Purwarianti et al., 2007). Each row in the FacQA dataset consists of a question,
a short passage, and a label phrase, which can be found inside the corresponding short passage.
There are six categories of questions: date, location, name, organization, person, and quantitative."""
),
text_features={"question": "question", "passage": "passage"},
# label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
label_classes=["O", "B", "I"],
label_column="seq_label",
train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/facqa_qa-factoid-itb/train_preprocess.csv",
valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/facqa_qa-factoid-itb/valid_preprocess.csv",
test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/facqa_qa-factoid-itb/test_preprocess_masked_label.csv",
citation=textwrap.dedent(
"""\
@inproceedings{purwarianti2007machine,
title={A Machine Learning Approach for Indonesian Question Answering System},
author={Ayu Purwarianti, Masatoshi Tsuchiya, and Seiichi Nakagawa},
booktitle={Proceedings of Artificial Intelligence and Applications },
pages={573--578},
year={2007}
}"""
),
),
]
def _info(self):
sentence_features = ["terma", "keps", "facqa"]
ner_ = ["nergrit", "nerp"]
pos_ = ["posp", "bapos"]
if self.config.name in (sentence_features + ner_ + pos_):
features = {
text_feature: datasets.Sequence(datasets.Value("string"))
for text_feature in six.iterkeys(self.config.text_features)
}
else:
features = {
text_feature: datasets.Value("string") for text_feature in six.iterkeys(self.config.text_features)
}
if self.config.label_classes:
if self.config.name in sentence_features:
features["seq_label"] = datasets.Sequence(
datasets.features.ClassLabel(names=self.config.label_classes)
)
elif self.config.name in ner_:
features["ner_tags"] = datasets.Sequence(datasets.features.ClassLabel(names=self.config.label_classes))
elif self.config.name in pos_:
features["pos_tags"] = datasets.Sequence(datasets.features.ClassLabel(names=self.config.label_classes))
elif self.config.name == "casa" or self.config.name == "hoasa":
for label in self.config.label_column:
features[label] = datasets.features.ClassLabel(names=self.config.label_classes)
else:
features["label"] = datasets.features.ClassLabel(names=self.config.label_classes)
return datasets.DatasetInfo(
description=self.config.description,
features=datasets.Features(features),
homepage=_INDONLU_HOMEPAGE,
citation=self.config.citation + "\n" + _INDONLU_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
train_path = dl_manager.download_and_extract(self.config.train_url)
valid_path = dl_manager.download_and_extract(self.config.valid_url)
test_path = dl_manager.download_and_extract(self.config.test_url)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
]
def _generate_examples(self, filepath):
""" Yields examples. """
csv_file = ["emot", "wrete", "facqa", "casa", "hoasa"]
tsv_file = ["smsa"]
txt_file = ["terma", "keps"]
txt_file_pos = ["posp", "bapos"]
txt_file_ner = ["nergrit", "nerp"]
with open(filepath, encoding="utf-8") as f:
if self.config.name in csv_file:
reader = csv.reader(f, delimiter=",", quotechar='"', quoting=csv.QUOTE_ALL)
next(reader) # skip first row which is header
for id_, row in enumerate(reader):
if self.config.name == "emot":
label, tweet = row
yield id_, {"tweet": tweet, "label": label}
elif self.config.name == "wrete":
premise, hypothesis, category, label = row
yield id_, {"premise": premise, "hypothesis": hypothesis, "category": category, "label": label}
elif self.config.name == "facqa":
question, passage, seq_label = row
yield id_, {
"question": ast.literal_eval(question),
"passage": ast.literal_eval(passage),
"seq_label": ast.literal_eval(seq_label),
}
elif self.config.name == "casa" or self.config.name == "hoasa":
sentence, *labels = row
sentence = {"sentence": sentence}
label = {l: labels[idx] for idx, l in enumerate(self.config.label_column)}
yield id_, {**sentence, **label}
elif self.config.name in tsv_file:
reader = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for id_, row in enumerate(reader):
if self.config.name == "smsa":
text, label = row
yield id_, {"text": text, "label": label}
elif self.config.name in (txt_file + txt_file_pos + txt_file_ner):
id_ = 0
tokens = []
seq_label = []
for line in f:
if len(line.strip()) > 0:
token, label = line[:-1].split("\t")
tokens.append(token)
seq_label.append(label)
else:
if self.config.name in txt_file:
yield id_, {"tokens": tokens, "seq_label": seq_label}
elif self.config.name in txt_file_pos:
yield id_, {"tokens": tokens, "pos_tags": seq_label}
elif self.config.name in txt_file_ner:
yield id_, {"tokens": tokens, "ner_tags": seq_label}
id_ += 1
tokens = []
seq_label = []
# add last example
if tokens:
if self.config.name in txt_file:
yield id_, {"tokens": tokens, "seq_label": seq_label}
elif self.config.name in txt_file_pos:
yield id_, {"tokens": tokens, "pos_tags": seq_label}
elif self.config.name in txt_file_ner:
yield id_, {"tokens": tokens, "ner_tags": seq_label}
|