| """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, |
| ) |
|
|
