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import json |
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import random |
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import string |
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from typing import Dict, List, Optional, Union |
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import datasets as ds |
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import pandas as pd |
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_CITATION = """\ |
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@inproceedings{kurihara-etal-2022-jglue, |
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title = "{JGLUE}: {J}apanese General Language Understanding Evaluation", |
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author = "Kurihara, Kentaro and |
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Kawahara, Daisuke and |
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Shibata, Tomohide", |
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booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", |
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month = jun, |
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year = "2022", |
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address = "Marseille, France", |
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publisher = "European Language Resources Association", |
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url = "https://aclanthology.org/2022.lrec-1.317", |
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pages = "2957--2966", |
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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.", |
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} |
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@InProceedings{Kurihara_nlp2022, |
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author = "栗原健太郎 and 河原大輔 and 柴田知秀", |
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title = "JGLUE: 日本語言語理解ベンチマーク", |
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booktitle = "言語処理学会第28回年次大会", |
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year = "2022", |
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url = "https://www.anlp.jp/proceedings/annual_meeting/2022/pdf_dir/E8-4.pdf" |
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note= "in Japanese" |
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} |
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""" |
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_DESCRIPTION = """\ |
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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. |
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""" |
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_HOMEPAGE = "https://github.com/yahoojapan/JGLUE" |
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_LICENSE = """\ |
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. |
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""" |
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_DESCRIPTION_CONFIGS = { |
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"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).", |
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"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.", |
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"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.", |
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"JSQuAD": "JSQuAD is a Japanese version of SQuAD (Rajpurkar+, 2016), one of the datasets of reading comprehension.", |
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"JCommonsenseQA": "JCommonsenseQA is a Japanese version of CommonsenseQA (Talmor+, 2019), which is a multiple-choice question answering dataset that requires commonsense reasoning ability.", |
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} |
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_URLS = { |
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"MARC-ja": { |
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"data": "https://s3.amazonaws.com/amazon-reviews-pds/tsv/amazon_reviews_multilingual_JP_v1_00.tsv.gz", |
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"filter_review_id_list": { |
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"valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/preprocess/marc-ja/data/filter_review_id_list/valid.txt" |
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}, |
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"label_conv_review_id_list": { |
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"valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/preprocess/marc-ja/data/label_conv_review_id_list/valid.txt" |
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}, |
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}, |
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"JSTS": { |
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"train": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jsts-v1.1/train-v1.1.json", |
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"valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jsts-v1.1/valid-v1.1.json", |
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}, |
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"JNLI": { |
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"train": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jnli-v1.1/train-v1.1.json", |
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"valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jnli-v1.1/valid-v1.1.json", |
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}, |
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"JSQuAD": { |
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"train": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jsquad-v1.1/train-v1.1.json", |
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"valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jsquad-v1.1/valid-v1.1.json", |
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}, |
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"JCommonsenseQA": { |
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"train": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jcommonsenseqa-v1.1/train-v1.1.json", |
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"valid": "https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jcommonsenseqa-v1.1/valid-v1.1.json", |
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}, |
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} |
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def features_jsts() -> ds.Features: |
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features = ds.Features( |
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{ |
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"sentence_pair_id": ds.Value("string"), |
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"yjcaptions_id": ds.Value("string"), |
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"sentence1": ds.Value("string"), |
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"sentence2": ds.Value("string"), |
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"label": ds.Value("float"), |
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} |
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) |
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return features |
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def features_jnli() -> ds.Features: |
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features = ds.Features( |
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{ |
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"sentence_pair_id": ds.Value("string"), |
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"yjcaptions_id": ds.Value("string"), |
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"sentence1": ds.Value("string"), |
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"sentence2": ds.Value("string"), |
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"label": ds.ClassLabel( |
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num_classes=3, names=["entailment", "contradiction", "neutral"] |
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), |
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} |
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) |
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return features |
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def features_jsquad() -> ds.Features: |
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title = ds.Value("string") |
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answers = ds.Sequence( |
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{"text": ds.Value("string"), "answer_start": ds.Value("int64")} |
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) |
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qas = ds.Sequence( |
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{ |
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"question": ds.Value("string"), |
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"id": ds.Value("string"), |
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"answers": answers, |
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"is_impossible": ds.Value("bool"), |
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} |
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) |
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paragraphs = ds.Sequence({"qas": qas, "context": ds.Value("string")}) |
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features = ds.Features( |
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{"data": ds.Sequence({"title": title, "paragraphs": paragraphs})} |
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) |
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return features |
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def features_jcommonsenseqa() -> ds.Features: |
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features = ds.Features( |
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{ |
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"q_id": ds.Value("int64"), |
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"question": ds.Value("string"), |
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"choice0": ds.Value("string"), |
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"choice1": ds.Value("string"), |
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"choice2": ds.Value("string"), |
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"choice3": ds.Value("string"), |
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"choice4": ds.Value("string"), |
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"label": ds.Value("int8"), |
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} |
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) |
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return features |
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def features_marc_ja() -> ds.Features: |
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features = ds.Features( |
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{ |
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"sentence": ds.Value("string"), |
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"label": ds.ClassLabel( |
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num_classes=3, names=["positive", "negative", "neutral"] |
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), |
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"review_id": ds.Value("string"), |
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} |
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) |
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return features |
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class MarcJaConfig(ds.BuilderConfig): |
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def __init__( |
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self, |
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name: str = "MARC-ja", |
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is_han_to_zen: bool = False, |
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max_instance_num: Optional[int] = None, |
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max_char_length: int = 500, |
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is_pos_neg: bool = True, |
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train_ratio: float = 0.94, |
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val_ratio: float = 0.03, |
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test_ratio: float = 0.03, |
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output_testset: bool = False, |
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filter_review_id_list_valid: bool = True, |
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label_conv_review_id_list_valid: bool = True, |
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version: Optional[Union[ds.utils.Version, str]] = ds.utils.Version("0.0.0"), |
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data_dir: Optional[str] = None, |
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data_files: Optional[ds.data_files.DataFilesDict] = None, |
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description: Optional[str] = None, |
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) -> None: |
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super().__init__( |
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name=name, |
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version=version, |
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data_dir=data_dir, |
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data_files=data_files, |
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description=description, |
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) |
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assert train_ratio + val_ratio + test_ratio == 1.0 |
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self.train_ratio = train_ratio |
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self.val_ratio = val_ratio |
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self.test_ratio = test_ratio |
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self.is_han_to_zen = is_han_to_zen |
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self.max_instance_num = max_instance_num |
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self.max_char_length = max_char_length |
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self.is_pos_neg = is_pos_neg |
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self.output_testset = output_testset |
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self.filter_review_id_list_valid = filter_review_id_list_valid |
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self.label_conv_review_id_list_valid = label_conv_review_id_list_valid |
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def get_label(rating: int, is_pos_neg: bool = False) -> Optional[str]: |
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if rating >= 4: |
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return "positive" |
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elif rating <= 2: |
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return "negative" |
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else: |
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if is_pos_neg: |
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return None |
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else: |
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return "neutral" |
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def is_filtered_by_ascii_rate(text: str, threshold: float = 0.9) -> bool: |
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ascii_letters = set(string.printable) |
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rate = sum(c in ascii_letters for c in text) / len(text) |
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return rate >= threshold |
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def shuffle_dataframe(df: pd.DataFrame) -> pd.DataFrame: |
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instances = df.to_dict(orient="records") |
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random.seed(1) |
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random.shuffle(instances) |
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return pd.DataFrame(instances) |
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def get_filter_review_id_list( |
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filter_review_id_list_paths: Dict[str, str], |
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) -> Dict[str, List[str]]: |
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filter_review_id_list_valid = filter_review_id_list_paths.get("valid") |
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filter_review_id_list_test = filter_review_id_list_paths.get("test") |
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filter_review_id_list = {} |
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if filter_review_id_list_valid is not None: |
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with open(filter_review_id_list_valid, "r") as rf: |
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filter_review_id_list["valid"] = [line.rstrip() for line in rf] |
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if filter_review_id_list_test is not None: |
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with open(filter_review_id_list_test, "r") as rf: |
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filter_review_id_list["test"] = [line.rstrip() for line in rf] |
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return filter_review_id_list |
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def get_label_conv_review_id_list( |
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label_conv_review_id_list_paths: Dict[str, str], |
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) -> Dict[str, Dict[str, str]]: |
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import csv |
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label_conv_review_id_list_valid = label_conv_review_id_list_paths.get("valid") |
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label_conv_review_id_list_test = label_conv_review_id_list_paths.get("test") |
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label_conv_review_id_list: Dict[str, Dict[str, str]] = {} |
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if label_conv_review_id_list_valid is not None: |
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with open(label_conv_review_id_list_valid, "r") as rf: |
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label_conv_review_id_list["valid"] = { |
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row[0]: row[1] for row in csv.reader(rf) |
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} |
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if label_conv_review_id_list_test is not None: |
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with open(label_conv_review_id_list_test, "r") as rf: |
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label_conv_review_id_list["test"] = { |
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row[0]: row[1] for row in csv.reader(rf) |
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} |
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return label_conv_review_id_list |
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def output_data( |
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df: pd.DataFrame, |
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train_ratio: float, |
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val_ratio: float, |
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test_ratio: float, |
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output_testset: bool, |
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filter_review_id_list_paths: Dict[str, str], |
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label_conv_review_id_list_paths: Dict[str, str], |
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) -> Dict[str, pd.DataFrame]: |
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instance_num = len(df) |
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split_dfs: Dict[str, pd.DataFrame] = {} |
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length1 = int(instance_num * train_ratio) |
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split_dfs["train"] = df.iloc[:length1] |
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length2 = int(instance_num * (train_ratio + val_ratio)) |
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split_dfs["valid"] = df.iloc[length1:length2] |
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split_dfs["test"] = df.iloc[length2:] |
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|
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filter_review_id_list = get_filter_review_id_list( |
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filter_review_id_list_paths=filter_review_id_list_paths, |
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) |
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label_conv_review_id_list = get_label_conv_review_id_list( |
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label_conv_review_id_list_paths=label_conv_review_id_list_paths, |
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) |
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|
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for eval_type in ("valid", "test"): |
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if filter_review_id_list.get(eval_type): |
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df = split_dfs[eval_type] |
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df = df[~df["review_id"].isin(filter_review_id_list[eval_type])] |
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split_dfs[eval_type] = df |
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|
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for eval_type in ("valid", "test"): |
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if label_conv_review_id_list.get(eval_type): |
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df = split_dfs[eval_type] |
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df = df.assign( |
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converted_label=df["review_id"].map(label_conv_review_id_list["valid"]) |
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) |
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df = df.assign( |
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label=df[["label", "converted_label"]].apply( |
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lambda xs: xs["label"] |
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if pd.isnull(xs["converted_label"]) |
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else xs["converted_label"], |
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axis=1, |
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) |
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) |
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df = df.drop(columns=["converted_label"]) |
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split_dfs[eval_type] = df |
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|
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return { |
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"train": split_dfs["train"], |
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"valid": split_dfs["valid"], |
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} |
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def preprocess_for_marc_ja( |
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config: MarcJaConfig, |
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data_file_path: str, |
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filter_review_id_list_paths: Dict[str, str], |
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label_conv_review_id_list_paths: Dict[str, str], |
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) -> Dict[str, pd.DataFrame]: |
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import mojimoji |
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from bs4 import BeautifulSoup |
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from tqdm import tqdm |
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|
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df = pd.read_csv(data_file_path, delimiter="\t") |
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df = df[["review_body", "star_rating", "review_id"]] |
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df = df.rename(columns={"review_body": "text", "star_rating": "rating"}) |
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|
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tqdm.pandas(dynamic_ncols=True, desc="Convert the rating to the label") |
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df = df.assign( |
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label=df["rating"].progress_apply( |
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lambda rating: get_label(rating, config.is_pos_neg) |
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) |
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) |
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|
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df = df[~df["label"].isnull()] |
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|
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tqdm.pandas(dynamic_ncols=True, desc="Remove html tags from the text") |
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df = df.assign( |
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text=df["text"].progress_apply( |
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lambda text: BeautifulSoup(text, "html.parser").get_text() |
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) |
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) |
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|
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tqdm.pandas(dynamic_ncols=True, desc="Filter by ascii rate") |
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df = df[~df["text"].progress_apply(is_filtered_by_ascii_rate)] |
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if config.max_char_length is not None: |
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df = df[df["text"].str.len() <= config.max_char_length] |
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|
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if config.is_han_to_zen: |
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df = df.assign(text=df["text"].apply(mojimoji.han_to_zen)) |
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df = df[["text", "label", "review_id"]] |
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df = df.rename(columns={"text": "sentence"}) |
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df = shuffle_dataframe(df) |
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split_dfs = output_data( |
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df=df, |
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train_ratio=config.train_ratio, |
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val_ratio=config.val_ratio, |
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test_ratio=config.test_ratio, |
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output_testset=config.output_testset, |
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filter_review_id_list_paths=filter_review_id_list_paths, |
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label_conv_review_id_list_paths=label_conv_review_id_list_paths, |
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) |
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return split_dfs |
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|
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class JGLUE(ds.GeneratorBasedBuilder): |
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VERSION = ds.Version("1.1.0") |
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BUILDER_CONFIGS = [ |
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MarcJaConfig( |
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name="MARC-ja", |
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version=VERSION, |
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description=_DESCRIPTION_CONFIGS["MARC-ja"], |
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), |
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ds.BuilderConfig( |
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name="JSTS", |
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version=VERSION, |
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description=_DESCRIPTION_CONFIGS["JSTS"], |
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), |
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ds.BuilderConfig( |
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name="JNLI", |
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version=VERSION, |
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description=_DESCRIPTION_CONFIGS["JNLI"], |
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), |
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ds.BuilderConfig( |
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name="JSQuAD", |
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version=VERSION, |
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description=_DESCRIPTION_CONFIGS["JSQuAD"], |
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), |
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ds.BuilderConfig( |
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name="JCommonsenseQA", |
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version=VERSION, |
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description=_DESCRIPTION_CONFIGS["JCommonsenseQA"], |
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), |
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] |
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|
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def _info(self) -> ds.DatasetInfo: |
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if self.config.name == "JSTS": |
|
features = features_jsts() |
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elif self.config.name == "JNLI": |
|
features = features_jnli() |
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elif self.config.name == "JSQuAD": |
|
features = features_jsquad() |
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elif self.config.name == "JCommonsenseQA": |
|
features = features_jcommonsenseqa() |
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elif self.config.name == "MARC-ja": |
|
features = features_marc_ja() |
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else: |
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raise ValueError(f"Invalid config name: {self.config.name}") |
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|
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return ds.DatasetInfo( |
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description=_DESCRIPTION, |
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citation=_CITATION, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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features=features, |
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) |
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|
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def _split_generators(self, dl_manager: ds.DownloadManager): |
|
file_paths = dl_manager.download_and_extract(_URLS[self.config.name]) |
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|
|
if self.config.name == "MARC-ja": |
|
filter_review_id_list = file_paths["filter_review_id_list"] |
|
label_conv_review_id_list = file_paths["label_conv_review_id_list"] |
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|
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split_dfs = preprocess_for_marc_ja( |
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config=self.config, |
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data_file_path=file_paths["data"], |
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filter_review_id_list_paths=filter_review_id_list, |
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label_conv_review_id_list_paths=label_conv_review_id_list, |
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) |
|
return [ |
|
ds.SplitGenerator( |
|
name=ds.Split.TRAIN, |
|
gen_kwargs={"split_df": split_dfs["train"]}, |
|
), |
|
ds.SplitGenerator( |
|
name=ds.Split.VALIDATION, |
|
gen_kwargs={"split_df": split_dfs["valid"]}, |
|
), |
|
] |
|
else: |
|
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: Optional[str] = None, |
|
split_df: Optional[pd.DataFrame] = None, |
|
): |
|
if self.config.name == "MARC-ja": |
|
if split_df is None: |
|
raise ValueError(f"Invalid preprocessing for {self.config.name}") |
|
|
|
instances = split_df.to_dict(orient="records") |
|
for i, data_dict in enumerate(instances): |
|
yield i, data_dict |
|
|
|
else: |
|
if file_path is None: |
|
raise ValueError(f"Invalid argument for {self.config.name}") |
|
|
|
with open(file_path, "r") as rf: |
|
for i, line in enumerate(rf): |
|
json_dict = json.loads(line) |
|
yield i, json_dict |
|
|
