File size: 18,056 Bytes
26499b9
eda3bbb
 
 
 
26499b9
 
eda3bbb
26499b9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
eda3bbb
26499b9
 
 
 
 
 
 
eda3bbb
 
 
 
 
26499b9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
eda3bbb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
26499b9
 
 
eda3bbb
 
 
 
 
26499b9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
eda3bbb
 
26499b9
 
 
 
 
 
 
 
 
 
 
 
 
eda3bbb
 
 
 
 
 
 
 
 
 
 
 
 
26499b9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import json
import random
import string
from collections import defaultdict
from typing import Dict, List, Optional, Union

import datasets as ds
import pandas as pd

_CITATION = """\
@inproceedings{kurihara-etal-2022-jglue,
    title = "{JGLUE}: {J}apanese General Language Understanding Evaluation",
    author = "Kurihara, Kentaro  and
      Kawahara, Daisuke  and
      Shibata, Tomohide",
    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2022.lrec-1.317",
    pages = "2957--2966",
    abstract = "To develop high-performance natural language understanding (NLU) models, it is necessary to have a benchmark to evaluate and analyze NLU ability from various perspectives. While the English NLU benchmark, GLUE, has been the forerunner, benchmarks are now being released for languages other than English, such as CLUE for Chinese and FLUE for French; but there is no such benchmark for Japanese. We build a Japanese NLU benchmark, JGLUE, from scratch without translation to measure the general NLU ability in Japanese. We hope that JGLUE will facilitate NLU research in Japanese.",
}

@InProceedings{Kurihara_nlp2022,
  author = 	"栗原健太郎 and 河原大輔 and 柴田知秀",
  title = 	"JGLUE: 日本語言語理解ベンチマーク",
  booktitle = 	"言語処理学会第28回年次大会",
  year =	"2022",
  url = "https://www.anlp.jp/proceedings/annual_meeting/2022/pdf_dir/E8-4.pdf"
  note= "in Japanese"
}
"""

_DESCRIPTION = """\
JGLUE, Japanese General Language Understanding Evaluation, is built to measure the general NLU ability in Japanese. JGLUE has been constructed from scratch without translation. We hope that JGLUE will facilitate NLU research in Japanese.
"""

_HOMEPAGE = "https://github.com/yahoojapan/JGLUE"

_LICENSE = """\
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
"""

_DESCRIPTION_CONFIGS = {
    "MARC-ja": "MARC-ja is a dataset of the text classification task. This dataset is based on the Japanese portion of Multilingual Amazon Reviews Corpus (MARC) (Keung+, 2020).",
    "JSTS": "JSTS is a Japanese version of the STS (Semantic Textual Similarity) dataset. STS is a task to estimate the semantic similarity of a sentence pair.",
    "JNLI": "JNLI is a Japanese version of the NLI (Natural Language Inference) dataset. NLI is a task to recognize the inference relation that a premise sentence has to a hypothesis sentence.",
    "JSQuAD": "JSQuAD is a Japanese version of SQuAD (Rajpurkar+, 2016), one of the datasets of reading comprehension.",
    "JCommonsenseQA": "JCommonsenseQA is a Japanese version of CommonsenseQA (Talmor+, 2019), which is a multiple-choice question answering dataset that requires commonsense reasoning ability.",
}

_URLS = {
    "MARC-ja": {
        "data": "https://s3.amazonaws.com/amazon-reviews-pds/tsv/amazon_reviews_multilingual_JP_v1_00.tsv.gz",
        "filter_review_id_list/valid.txt": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/preprocess/marc-ja/data/filter_review_id_list/valid.txt",
        "label_conv_review_id_list/valid.txt": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/preprocess/marc-ja/data/label_conv_review_id_list/valid.txt",
    },
    "JSTS": {
        "train": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jsts-v1.1/train-v1.1.json",
        "valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jsts-v1.1/valid-v1.1.json",
    },
    "JNLI": {
        "train": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jnli-v1.1/train-v1.1.json",
        "valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jnli-v1.1/valid-v1.1.json",
    },
    "JSQuAD": {
        "train": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jsquad-v1.1/train-v1.1.json",
        "valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jsquad-v1.1/valid-v1.1.json",
    },
    "JCommonsenseQA": {
        "train": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jcommonsenseqa-v1.1/train-v1.1.json",
        "valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jcommonsenseqa-v1.1/valid-v1.1.json",
    },
}


def features_jsts() -> ds.Features:
    features = ds.Features(
        {
            "sentence_pair_id": ds.Value("string"),
            "yjcaptions_id": ds.Value("string"),
            "sentence1": ds.Value("string"),
            "sentence2": ds.Value("string"),
            "label": ds.Value("float"),
        }
    )
    return features


def features_jnli() -> ds.Features:
    features = ds.Features(
        {
            "sentence_pair_id": ds.Value("string"),
            "yjcaptions_id": ds.Value("string"),
            "sentence1": ds.Value("string"),
            "sentence2": ds.Value("string"),
            "label": ds.ClassLabel(
                num_classes=3, names=["entailment", "contradiction", "neutral"]
            ),
        }
    )
    return features


def features_jsquad() -> ds.Features:
    title = ds.Value("string")
    answers = ds.Sequence(
        {"text": ds.Value("string"), "answer_start": ds.Value("int64")}
    )
    qas = ds.Sequence(
        {
            "question": ds.Value("string"),
            "id": ds.Value("string"),
            "answers": answers,
            "is_impossible": ds.Value("bool"),
        }
    )
    paragraphs = ds.Sequence({"qas": qas, "context": ds.Value("string")})
    features = ds.Features(
        {"data": ds.Sequence({"title": title, "paragraphs": paragraphs})}
    )
    return features


def features_jcommonsenseqa() -> ds.Features:
    features = ds.Features(
        {
            "q_id": ds.Value("int64"),
            "question": ds.Value("string"),
            "choice0": ds.Value("string"),
            "choice1": ds.Value("string"),
            "choice2": ds.Value("string"),
            "choice3": ds.Value("string"),
            "choice4": ds.Value("string"),
            "label": ds.Value("int8"),
        }
    )
    return features


def features_marc_ja() -> ds.Features:
    features = ds.Features()
    return features


class MarcJaConfig(ds.BuilderConfig):
    def __init__(
        self,
        name: str = "MARC-ja",
        is_han_to_zen: bool = False,
        max_instance_num: Optional[int] = None,
        max_char_length: Optional[int] = None,
        is_pos_neg: bool = False,
        train_ratio: float = 0.94,
        val_ratio: float = 0.03,
        test_ratio: float = 0.03,
        output_testset: bool = False,
        filter_review_id_list_valid: Optional[str] = None,
        filter_review_id_list_test: Optional[str] = None,
        label_conv_review_id_list_valid: Optional[str] = None,
        label_conv_review_id_list_test: Optional[str] = None,
        version: Optional[Union[ds.utils.Version, str]] = ds.utils.Version("0.0.0"),
        data_dir: Optional[str] = None,
        data_files: Optional[ds.data_files.DataFilesDict] = None,
        description: Optional[str] = None,
    ) -> None:
        super().__init__(
            name=name,
            version=version,
            data_dir=data_dir,
            data_files=data_files,
            description=description,
        )
        assert train_ratio + val_ratio + test_ratio == 1.0

        self.train_ratio = train_ratio
        self.val_ratio = val_ratio
        self.test_ratio = test_ratio

        self.is_han_to_zen = is_han_to_zen
        self.max_instance_num = max_instance_num
        self.max_char_length = max_char_length
        self.is_pos_neg = is_pos_neg
        self.output_testset = output_testset
        self.filter_review_id_list_valid = filter_review_id_list_valid
        self.filter_review_id_list_test = filter_review_id_list_test
        self.label_conv_review_id_list_valid = label_conv_review_id_list_valid
        self.label_conv_review_id_list_test = label_conv_review_id_list_test


def preprocess_for_marc_ja(
    config: MarcJaConfig,
    data_file_path: str,
    filter_review_id_list_path: str,
    label_conv_review_id_list_path: str,
) -> Dict[str, str]:
    import mojimoji
    from bs4 import BeautifulSoup

    df = pd.read_csv(data_file_path, delimiter="\t")
    df = df[["review_body", "star_rating", "review_id"]]

    # rename columns
    df = df.rename(columns={"review_body": "text", "star_rating": "rating"})

    def get_label(rating: int, is_pos_neg: bool = False) -> Optional[str]:
        if rating >= 4:
            return "positive"
        elif rating <= 2:
            return "negative"
        else:
            if is_pos_neg:
                return None
            else:
                return "neutral"

    # convert the rating to label
    df = df.assign(
        label=df["rating"].apply(lambda rating: get_label(rating, config.is_pos_neg))
    )

    # remove rows where the label is None
    df = df[df["label"].isnull()]

    # remove html tags from the text
    df = df.assign(
        text=df["text"].apply(
            lambda text: BeautifulSoup(text, "html.parser").get_text()
        )
    )

    def is_filtered_by_ascii_rate(text: str, threshold: float = 0.9) -> bool:
        ascii_letters = set(string.printable)
        rate = sum(c in ascii_letters for c in text) / len(text)
        return rate >= threshold

    # filter by ascii rate
    df = df[~df["text"].apply(is_filtered_by_ascii_rate)]

    if config.max_char_length is not None:
        df = df[df["text"].str.len() <= config.max_char_length]

    if config.is_han_to_zen:
        df = df.assign(text=df["text"].apply(mojimoji.han_to_zen))

    df = df[["text", "label", "review_id"]]
    df = df.rename(columns={"text": "sentence"})

    # shuffle dataset
    instances = df.to_dict(orient="records")
    random.seed(1)
    random.shuffle(instances)

    def get_filter_review_id_list(
        filter_review_id_list_valid: Optional[str] = None,
        filter_review_id_list_test: Optional[str] = None,
    ) -> Dict[str, List[str]]:
        filter_review_id_list = defaultdict(list)

        if filter_review_id_list_valid is not None:
            with open(filter_review_id_list_valid, "r") as rf:
                filter_review_id_list["valid"] = [line.rstrip() for line in rf]

        if filter_review_id_list_test is not None:
            with open(filter_review_id_list_test, "r") as rf:
                filter_review_id_list["test"] = [line.rstrip() for line in rf]

        return filter_review_id_list

    def get_label_conv_review_id_list(
        label_conv_review_id_list_valid: Optional[str] = None,
        label_conv_review_id_list_test: Optional[str] = None,
    ) -> Dict[str, str]:
        label_conv_review_id_list = defaultdict(list)

        if label_conv_review_id_list_valid is not None:
            breakpoint()
            with open(label_conv_review_id_list_valid, "r") as f:
                label_conv_review_id_list["valid"] = {
                    row[0]: row[1] for row in csv.reader(f)
                }

        if label_conv_review_id_list_test is not None:
            breakpoint()
            with open(label_conv_review_id_list_test, "r") as f:
                label_conv_review_id_list["test"] = {
                    row[0]: row[1] for row in csv.reader(f)
                }

        return label_conv_review_id_list

    def output_data(
        instances: List[Dict[str, str]],
        train_ratio: float,
        val_ratio: float,
        test_ratio: float,
        output_testset: bool = False,
    ) -> Dict[str, str]:
        instance_num = len(instances)

        split_instances = {}
        length1 = int(instance_num * train_ratio)
        split_instances["train"] = instances[:length1]

        length2 = int(instance_num * (train_ratio + val_ratio))
        split_instances["valid"] = instances[length1:length2]
        split_instances["test"] = instances[length2:]

        filter_review_id_list = get_filter_review_id_list(
            filter_review_id_list_valid=config.filter_review_id_list_valid,
            filter_review_id_list_test=config.filter_review_id_list_test,
        )
        label_conv_review_id_list = get_label_conv_review_id_list(
            label_conv_review_id_list_valid=config.label_conv_review_id_list_valid,
            label_conv_review_id_list_test=config.label_conv_review_id_list_test,
        )

        for eval_type in ("train", "valid", "test"):
            if not output_testset and eval_type == "test":
                continue

            for instance in split_instances[eval_type]:
                # filter
                if len(filter_review_id_list) != 0:
                    filter_flag = False
                    for filter_eval_type in ("valid", "test"):
                        if (
                            eval_type == filter_eval_type
                            and instance["review_id"]
                            in filter_review_id_list[filter_eval_type]
                        ):
                            filter_flag = True
                        if eval_type != filter_eval_type:
                            if filter_eval_type in filter_review_id_list:
                                assert (
                                    instance["review_id"]
                                    not in filter_review_id_list[filter_eval_type]
                                )

                    if filter_flag is True:
                        continue

                # convert labels
                if len(label_conv_review_id_list) != 0:
                    for conv_eval_type in ("valid", "test"):
                        if (
                            eval_type == conv_eval_type
                            and instance["review_id"]
                            in label_conv_review_id_list[conv_eval_type]
                        ):
                            assert (
                                instance["label"]
                                != label_conv_review_id_list[conv_eval_type][
                                    instance["review_id"]
                                ]
                            )
                            # update
                            instance["label"] = label_conv_review_id_list[
                                conv_eval_type
                            ][instance["review_id"]]

                        if eval_type != conv_eval_type:
                            if conv_eval_type in label_conv_review_id_list:
                                assert (
                                    instance["review_id"]
                                    not in label_conv_review_id_list[conv_eval_type]
                                )

                if eval_type == "test":
                    del instance["label"]

                breakpoint()

        breakpoint()

    file_paths = output_data(
        df,
        train_ratio=config.train_ratio,
        val_ratio=config.val_ratio,
        test_ratio=config.test_ratio,
        output_testset=config.output_testset,
    )
    return file_paths


class JGLUE(ds.GeneratorBasedBuilder):
    VERSION = ds.Version("1.1.0")
    BUILDER_CONFIGS = [
        MarcJaConfig(
            name="MARC-ja",
            version=VERSION,
            description=_DESCRIPTION_CONFIGS["MARC-ja"],
        ),
        ds.BuilderConfig(
            name="JSTS",
            version=VERSION,
            description=_DESCRIPTION_CONFIGS["JSTS"],
        ),
        ds.BuilderConfig(
            name="JNLI",
            version=VERSION,
            description=_DESCRIPTION_CONFIGS["JNLI"],
        ),
        ds.BuilderConfig(
            name="JSQuAD",
            version=VERSION,
            description=_DESCRIPTION_CONFIGS["JSQuAD"],
        ),
        ds.BuilderConfig(
            name="JCommonsenseQA",
            version=VERSION,
            description=_DESCRIPTION_CONFIGS["JCommonsenseQA"],
        ),
    ]

    def _info(self) -> ds.DatasetInfo:
        if self.config.name == "JSTS":
            features = features_jsts()
        elif self.config.name == "JNLI":
            features = features_jnli()
        elif self.config.name == "JSQuAD":
            features = features_jsquad()
        elif self.config.name == "JCommonsenseQA":
            features = features_jcommonsenseqa()
        elif self.config.name == "MARC-ja":
            features = features_marc_ja()
        else:
            raise ValueError(f"Invalid config name: {self.config.name}")

        return ds.DatasetInfo(
            description=_DESCRIPTION,
            citation=_CITATION,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            features=features,
        )

    def _split_generators(self, dl_manager: ds.DownloadManager):
        file_paths = dl_manager.download_and_extract(_URLS[self.config.name])

        if self.config.name == "MARC-ja":
            file_paths = preprocess_for_marc_ja(
                config=self.config,
                data_file_path=file_paths["data"],
                filter_review_id_list_path=file_paths[
                    "filter_review_id_list/valid.txt"
                ],
                label_conv_review_id_list_path=file_paths[
                    "label_conv_review_id_list/valid.txt"
                ],
            )

        return [
            ds.SplitGenerator(
                name=ds.Split.TRAIN,
                gen_kwargs={
                    "file_path": file_paths["train"],
                },
            ),
            ds.SplitGenerator(
                name=ds.Split.VALIDATION,
                gen_kwargs={
                    "file_path": file_paths["valid"],
                },
            ),
        ]

    def _generate_examples(self, file_path: str):
        with open(file_path, "r") as rf:
            for i, line in enumerate(rf):
                json_dict = json.loads(line)
                yield i, json_dict