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"""Multi domain document classification dataset used in [https://arxiv.org/pdf/2004.10964.pdf](https://arxiv.org/pdf/2004.10964.pdf)"""
import json
from itertools import chain
import datasets
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """Multi domain document classification dataset used in [https://arxiv.org/pdf/2004.10964.pdf](https://arxiv.org/pdf/2004.10964.pdf)"""
_NAME = "multi_domain_document_classification"
_VERSION = "0.2.3"
_CITATION = """
@inproceedings{dontstoppretraining2020,
author = {Suchin Gururangan and Ana Marasović and Swabha Swayamdipta and Kyle Lo and Iz Beltagy and Doug Downey and Noah A. Smith},
title = {Don't Stop Pretraining: Adapt Language Models to Domains and Tasks},
year = {2020},
booktitle = {Proceedings of ACL},
}
"""
_HOME_PAGE = "https://github.com/asahi417/m3"
_URL = f'https://huggingface.co/datasets/asahi417/{_NAME}/raw/main/dataset'
_DATA_TYPE = ["chemprot", "citation_intent", "hyperpartisan_news", "rct_sample", "sciie", "amcd",
"yelp_review", "tweet_eval_irony", "tweet_eval_hate", "tweet_eval_emotion"]
_URLS = {
k:
{
str(datasets.Split.TEST): [f'{_URL}/{k}/test.jsonl'],
str(datasets.Split.TRAIN): [f'{_URL}/{k}/train.jsonl'],
str(datasets.Split.VALIDATION): [f'{_URL}/{k}/dev.jsonl']
}
for k in _DATA_TYPE
}
_LABELS = {
"chemprot": {"ACTIVATOR": 0, "AGONIST": 1, "AGONIST-ACTIVATOR": 2, "AGONIST-INHIBITOR": 3, "ANTAGONIST": 4, "DOWNREGULATOR": 5, "INDIRECT-DOWNREGULATOR": 6, "INDIRECT-UPREGULATOR": 7, "INHIBITOR": 8, "PRODUCT-OF": 9, "SUBSTRATE": 10, "SUBSTRATE_PRODUCT-OF": 11, "UPREGULATOR": 12},
"citation_intent": {"Background": 0, "CompareOrContrast": 1, "Extends": 2, "Future": 3, "Motivation": 4, "Uses": 5},
"hyperpartisan_news": {"false": 0, "true": 1},
"rct_sample": {"BACKGROUND": 0, "CONCLUSIONS": 1, "METHODS": 2, "OBJECTIVE": 3, "RESULTS": 4},
"sciie": {"COMPARE": 0, "CONJUNCTION": 1, "EVALUATE-FOR": 2, "FEATURE-OF": 3, "HYPONYM-OF": 4, "PART-OF": 5, "USED-FOR": 6},
"amcd": {"false": 0, "true": 1},
"yelp_review": {"5 star": 4, "4 star": 3, "3 star": 2, "2 star": 1, "1 star": 0},
"tweet_eval_irony": {"non_irony":0, "irony": 1},
"tweet_eval_hate": {"non_hate": 0, "hate": 1},
"tweet_eval_emotion": {"anger": 0, "joy": 1, "optimism": 2, "sadness": 3}
}
class MultiDomainDocumentClassificationConfig(datasets.BuilderConfig):
"""BuilderConfig"""
def __init__(self, **kwargs):
"""BuilderConfig.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(MultiDomainDocumentClassificationConfig, self).__init__(**kwargs)
class MultiDomainDocumentClassification(datasets.GeneratorBasedBuilder):
"""Dataset."""
BUILDER_CONFIGS = [
MultiDomainDocumentClassificationConfig(
name=k, version=datasets.Version(_VERSION), description=_DESCRIPTION
) for k in _DATA_TYPE
]
def _split_generators(self, dl_manager):
downloaded_file = dl_manager.download_and_extract(_URLS[self.config.name])
return [
datasets.SplitGenerator(name=i, gen_kwargs={"filepaths": downloaded_file[str(i)]})
for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]
]
def _generate_examples(self, filepaths):
_key = 0
for filepath in filepaths:
logger.info(f"generating examples from = {filepath}")
with open(filepath, encoding="utf-8") as f:
_list = [i for i in f.read().split('\n') if len(i) > 0]
for i in _list:
data = json.loads(i)
yield _key, data
_key += 1
def _info(self):
label2id = sorted(_LABELS[self.config.name].items(), key=lambda x: x[1])
label = [i[0] for i in label2id]
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=label),
}
),
supervised_keys=None,
homepage=_HOME_PAGE,
citation=_CITATION,
)
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