File size: 24,737 Bytes
8537242 833b866 8537242 0d03fdb 8537242 0d03fdb 8537242 0d03fdb 8537242 0d03fdb 8537242 0d03fdb 8537242 0d03fdb 8537242 0d03fdb 8537242 0d03fdb 8537242 0d03fdb 8537242 0d03fdb |
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 |
# coding=utf-8
# Copyright 2020 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
"""The General Language Understanding Evaluation (GLUE) benchmark."""
import json
import textwrap
import datasets
_XGLUE_CITATION = """\
@article{Liang2020XGLUEAN,
title={XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation},
author={Yaobo Liang and Nan Duan and Yeyun Gong and Ning Wu and Fenfei Guo and Weizhen Qi
and Ming Gong and Linjun Shou and Daxin Jiang and Guihong Cao and Xiaodong Fan and Ruofei
Zhang and Rahul Agrawal and Edward Cui and Sining Wei and Taroon Bharti and Ying Qiao
and Jiun-Hung Chen and Winnie Wu and Shuguang Liu and Fan Yang and Daniel Campos
and Rangan Majumder and Ming Zhou},
journal={arXiv},
year={2020},
volume={abs/2004.01401}
}
"""
_XGLUE_DESCRIPTION = """\
XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained
models with respect to cross-lingual natural language understanding and generation.
The benchmark is composed of the following 11 tasks:
- NER
- POS Tagging (POS)
- News Classification (NC)
- MLQA
- XNLI
- PAWS-X
- Query-Ad Matching (QADSM)
- Web Page Ranking (WPR)
- QA Matching (QAM)
- Question Generation (QG)
- News Title Generation (NTG)
For more information, please take a look at https://microsoft.github.io/XGLUE/.
"""
_XGLUE_ALL_DATA = "xglue_full_dataset.tar.gz"
_LANGUAGES = {
"ner": ["en", "de", "es", "nl"],
"pos": ["en", "de", "es", "nl", "bg", "el", "fr", "pl", "tr", "vi", "zh", "ur", "hi", "it", "ar", "ru", "th"],
"mlqa": ["en", "de", "ar", "es", "hi", "vi", "zh"],
"nc": ["en", "de", "es", "fr", "ru"],
"xnli": ["en", "ar", "bg", "de", "el", "es", "fr", "hi", "ru", "sw", "th", "tr", "ur", "vi", "zh"],
"paws-x": ["en", "de", "es", "fr"],
"qadsm": ["en", "de", "fr"],
"wpr": ["en", "de", "es", "fr", "it", "pt", "zh"],
"qam": ["en", "de", "fr"],
"qg": ["en", "de", "es", "fr", "it", "pt"],
"ntg": ["en", "de", "es", "fr", "ru"],
}
_PATHS = {
"mlqa": {
"train": "squad1.1/train-v1.1.json",
"dev": "MLQA_V1/dev/dev-context-{0}-question-{0}.json",
"test": "MLQA_V1/test/test-context-{0}-question-{0}.json",
},
"xnli": {"train": "multinli.train.en.tsv", "dev": "{}.dev", "test": "{}.test"},
"paws-x": {
"train": "en/train.tsv",
"dev": "{}/dev_2k.tsv",
"test": "{}/test_2k.tsv",
},
}
for name in ["ner", "pos"]:
_PATHS[name] = {"train": "en.train", "dev": "{}.dev", "test": "{}.test"}
for name in ["nc", "qadsm", "wpr", "qam"]:
_PATHS[name] = {
"train": "xglue." + name + ".en.train",
"dev": "xglue." + name + ".{}.dev",
"test": "xglue." + name + ".{}.test",
}
for name in ["qg", "ntg"]:
_PATHS[name] = {"train": "xglue." + name + ".en", "dev": "xglue." + name + ".{}", "test": "xglue." + name + ".{}"}
class XGlueConfig(datasets.BuilderConfig):
"""BuilderConfig for XGLUE."""
def __init__(
self,
data_dir,
citation,
url,
**kwargs,
):
"""BuilderConfig for XGLUE.
Args:
data_dir: `string`, the path to the folder containing the files in the
downloaded .tar
citation: `string`, citation for the data set
url: `string`, url for information about the data set
**kwargs: keyword arguments forwarded to super.
"""
super(XGlueConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
self.data_dir = data_dir
self.citation = citation
self.url = url
class XGlue(datasets.GeneratorBasedBuilder):
"""The Cross-lingual Pre-training, Understanding and Generation (XGlue) Benchmark."""
BUILDER_CONFIGS = [
XGlueConfig(
name="ner",
description=textwrap.dedent(
"""\
The shared task of CoNLL-2003 concerns language-independent named entity recognition.
We will concentrate on four types of named entities:
persons, locations, organizations and names of miscellaneous entities
that do not belong to the previous three groups.
"""
),
data_dir="NER",
citation=textwrap.dedent(
"""\
@article{Sang2003IntroductionTT,
title={Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition},
author={Erik F. Tjong Kim Sang and Fien De Meulder},
journal={ArXiv},
year={2003},
volume={cs.CL/0306050}
},
@article{Sang2002IntroductionTT,
title={Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition},
author={Erik F. Tjong Kim Sang},
journal={ArXiv},
year={2002},
volume={cs.CL/0209010}
}"""
),
url="https://www.clips.uantwerpen.be/conll2003/ner/",
),
XGlueConfig(
name="pos",
description=textwrap.dedent(
"""\
Universal Dependencies (UD) is a project that is developing cross-linguistically consistent treebank
annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual
learning, and parsing research from a language typology perspective. The annotation scheme is based on an
evolution of (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal
part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets
(Zeman, 2008). The general philosophy is to provide a universal inventory of categories and guidelines
to facilitate consistent annotation of similar constructions across languages, while
allowing language-specific extensions when necessary.
"""
),
data_dir="POS",
citation=textwrap.dedent(
"""\
@misc{11234/1-3105,
title={Universal Dependencies 2.5},
author={Zeman, Daniel and Nivre, Joakim and Abrams, Mitchell and Aepli, et al.},
url={http://hdl.handle.net/11234/1-3105},
note={{LINDAT}/{CLARIAH}-{CZ} digital library at the Institute of Formal and Applied Linguistics ({{\'U}FAL}), Faculty of Mathematics and Physics, Charles University},
copyright={Licence Universal Dependencies v2.5},
year={2019}
}"""
),
url="https://universaldependencies.org/",
),
XGlueConfig(
name="mlqa",
description=textwrap.dedent(
"""\
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering
performance. MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages
- English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese.
MLQA is highly parallel, with QA instances parallel between 4 different languages on average.
"""
),
data_dir="MLQA",
citation=textwrap.dedent(
"""\
@article{Lewis2019MLQAEC,
title={MLQA: Evaluating Cross-lingual Extractive Question Answering},
author={Patrick Lewis and Barlas Oguz and Ruty Rinott and Sebastian Riedel and Holger Schwenk},
journal={ArXiv},
year={2019},
volume={abs/1910.07475}
}"""
),
url="https://github.com/facebookresearch/MLQA",
),
XGlueConfig(
name="nc",
description=textwrap.dedent(
"""\
This task aims to predict the category given a news article. It covers
5 languages, including English, Spanish, French,
German and Russian. Each labeled instance is a
3-tuple: <news title, news body, category>. The
category number is 10. We crawl this dataset from
a commercial news website. Accuracy (ACC) of
the multi-class classification is used as the metric.
"""
),
data_dir="NC",
citation="",
url="",
),
XGlueConfig(
name="xnli",
description=textwrap.dedent(
"""\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""
),
data_dir="XNLI",
citation=textwrap.dedent(
"""\
@inproceedings{Conneau2018XNLIEC,
title={XNLI: Evaluating Cross-lingual Sentence Representations},
author={Alexis Conneau and Guillaume Lample and Ruty Rinott and Adina Williams and Samuel R. Bowman and Holger Schwenk and Veselin Stoyanov},
booktitle={EMNLP},
year={2018}
}"""
),
url="https://github.com/facebookresearch/XNLI",
),
XGlueConfig(
name="paws-x",
description=textwrap.dedent(
"""\
PAWS-X contains 23,659 human translated PAWS (Paraphrase Adversaries from Word Scrambling) evaluation pairs and 296,406 machine translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. All translated pairs are sourced from examples in PAWS-Wiki.
"""
),
data_dir="PAWSX",
citation=textwrap.dedent(
"""\
@article{Yang2019PAWSXAC,
title={PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification},
author={Yinfei Yang and Yuan Zhang and Chris Tar and Jason Baldridge},
journal={ArXiv},
year={2019},
volume={abs/1908.11828}
}"""
),
url="https://github.com/google-research-datasets/paws/tree/master/pawsx",
),
XGlueConfig(
name="qadsm",
description=textwrap.dedent(
"""\
Query-Ad Matching (QADSM) task aims
to predict whether an advertisement (ad) is relevant to an input query. It covers 3 languages, including English, French and German. Each labeled instance is a 4-tuple: <query, ad title, ad description, label>. The label indicates whether the
ad is relevant to the query (Good), or not (Bad).
This dataset was constructed based on a commercial search engine. Accuracy (ACC) of the binary classification should be used as the metric.
"""
),
data_dir="QADSM",
citation="",
url="",
),
XGlueConfig(
name="wpr",
description=textwrap.dedent(
"""\
Tthe Web Page Ranking (WPR) task aims to
predict whether a web page is relevant to an input query. It covers 7 languages, including English, German, French, Spanish, Italian, Portuguese and Chinese. Each labeled instance is a
4-tuple: <query, web page title, web page snippet, label>. The relevance label contains 5 ratings: Perfect (4), Excellent (3), Good (2), Fair (1)
and Bad (0). The dataset is constructed based on a
commercial search engine. Normalize Discounted
Cumulative Gain (nDCG) should be used as the metric.
"""
),
data_dir="WPR",
citation="",
url="",
),
XGlueConfig(
name="qam",
description=textwrap.dedent(
"""\
The QA Matching (QAM) task aims to predict whether a <question, passage> pair is a QA pair.
It covers 3 languages, including English, French
and German. Each labeled instance is a 3-tuple:
<question, passage, label>. The label indicates
whether the passage is the answer of the question
(1), or not (0). This dataset is constructed based on
a commercial search engine. Accuracy (ACC) of
the binary classification should be used as the metric.
"""
),
data_dir="QAM",
citation="",
url="",
),
XGlueConfig(
name="qg",
description=textwrap.dedent(
"""\
The Question Generation (QG) task aims to
generate a question for a given passage. <passage, question> pairs were collected from a commercial search engine. It covers 6 languages, including English, French, German, Spanish, Italian and
Portuguese. BLEU-4 score should be used as the metric.
"""
),
data_dir="QG",
citation="",
url="",
),
XGlueConfig(
name="ntg",
description=textwrap.dedent(
"""\
News Title Generation (NTG) task aims
to generate a proper title for a given news body.
We collect <news body, news title> pairs from a
commercial news website. It covers 5 languages,
including German, English, French, Spanish and
Russian. BLEU-4 score should be used as the metric.
"""
),
data_dir="NTG",
citation="",
url="",
),
]
def _info(self):
if self.config.name == "ner":
features = {
"words": datasets.Sequence(datasets.Value("string")),
"ner": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
"B-MISC",
"I-MISC",
]
)
),
}
elif self.config.name == "pos":
features = {
"words": datasets.Sequence(datasets.Value("string")),
"pos": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
]
)
),
}
elif self.config.name == "mlqa":
features = {
"context": datasets.Value("string"),
"question": datasets.Value("string"),
"answers": datasets.features.Sequence(
{"answer_start": datasets.Value("int32"), "text": datasets.Value("string")}
),
# These are the features of your dataset like images, labels ...
}
elif self.config.name == "nc":
features = {
"news_title": datasets.Value("string"),
"news_body": datasets.Value("string"),
"news_category": datasets.ClassLabel(
names=[
"foodanddrink",
"sports",
"travel",
"finance",
"lifestyle",
"news",
"entertainment",
"health",
"video",
"autos",
]
),
}
elif self.config.name == "xnli":
features = {
"premise": datasets.Value("string"),
"hypothesis": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
}
elif self.config.name == "paws-x":
features = {
"sentence1": datasets.Value("string"),
"sentence2": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["different", "same"]),
}
elif self.config.name == "qadsm":
features = {
"query": datasets.Value("string"),
"ad_title": datasets.Value("string"),
"ad_description": datasets.Value("string"),
"relevance_label": datasets.features.ClassLabel(names=["Bad", "Good"]),
}
elif self.config.name == "wpr":
features = {
"query": datasets.Value("string"),
"web_page_title": datasets.Value("string"),
"web_page_snippet": datasets.Value("string"),
"relavance_label": datasets.features.ClassLabel(names=["Bad", "Fair", "Good", "Excellent", "Perfect"]),
}
elif self.config.name == "qam":
features = {
"question": datasets.Value("string"),
"answer": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["False", "True"]),
}
elif self.config.name == "qg":
features = {
"answer_passage": datasets.Value("string"),
"question": datasets.Value("string"),
}
elif self.config.name == "ntg":
features = {
"news_body": datasets.Value("string"),
"news_title": datasets.Value("string"),
}
return datasets.DatasetInfo(
description=_XGLUE_DESCRIPTION,
features=datasets.Features(features),
homepage=self.config.url,
citation=self.config.citation + "\n" + _XGLUE_CITATION,
)
def _split_generators(self, dl_manager):
archive = dl_manager.download(_XGLUE_ALL_DATA)
data_folder = f"xglue_full_dataset/{self.config.data_dir}"
name = self.config.name
languages = _LANGUAGES[name]
return (
[
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"archive": dl_manager.iter_archive(archive),
"data_path": f"{data_folder}/{_PATHS[name]['train']}",
"split": "train",
},
),
]
+ [
datasets.SplitGenerator(
name=datasets.Split(f"validation.{lang}"),
gen_kwargs={
"archive": dl_manager.iter_archive(archive),
"data_path": f"{data_folder}/{_PATHS[name]['dev'].format(lang)}",
"split": "dev",
},
)
for lang in languages
]
+ [
datasets.SplitGenerator(
name=datasets.Split(f"test.{lang}"),
gen_kwargs={
"archive": dl_manager.iter_archive(archive),
"data_path": f"{data_folder}/{_PATHS[name]['test'].format(lang)}",
"split": "test",
},
)
for lang in languages
]
)
def _generate_examples(self, archive, data_path, split=None):
keys = list(self._info().features.keys())
src_f = tgt_f = None
for path, file in archive:
if self.config.name == "mlqa":
if path == data_path:
data = json.load(file)
for examples in data["data"]:
for example in examples["paragraphs"]:
context = example["context"]
for qa in example["qas"]:
question = qa["question"]
id_ = qa["id"]
answers = qa["answers"]
answers_start = [answer["answer_start"] for answer in answers]
answers_text = [answer["text"] for answer in answers]
yield id_, {
"context": context,
"question": question,
"answers": {"answer_start": answers_start, "text": answers_text},
}
elif self.config.name in ["ner", "pos"]:
if path == data_path:
words = []
result = []
idx = -1
for line in file:
line = line.decode("utf-8")
if line.strip() == "":
if len(words) > 0:
out_dict = {keys[0]: words, keys[1]: result}
words = []
result = []
idx += 1
yield idx, out_dict
else:
splits = line.strip().split(" ")
words.append(splits[0])
result.append(splits[1])
elif self.config.name in ["ntg", "qg"]:
if path == data_path + ".src." + split:
src_f = [line.decode("utf-8") for line in file]
elif path == data_path + ".tgt." + split:
tgt_f = [line.decode("utf-8") for line in file]
if src_f and tgt_f:
for idx, (src_line, tgt_line) in enumerate(zip(src_f, tgt_f)):
yield idx, {keys[0]: src_line.strip(), keys[1]: tgt_line.strip()}
else:
_process_dict = {
"paws-x": {"0": "different", "1": "same"},
"xnli": {"contradictory": "contradiction"},
"qam": {"0": "False", "1": "True"},
"wpr": {"0": "Bad", "1": "Fair", "2": "Good", "3": "Excellent", "4": "Perfect"},
}
def _process(value):
if self.config.name in _process_dict and value in _process_dict[self.config.name]:
return _process_dict[self.config.name][value]
return value
if path == data_path:
for idx, line in enumerate(file):
line = line.decode("utf-8")
if data_path.split(".")[-1] == "tsv" and idx == 0:
continue
items = line.strip().split("\t")
yield idx, {
key: _process(value)
for key, value in zip(keys, items[1:] if self.config.name == "paws-x" else items)
}
|