init

multi_domain_document_classification.py

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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 = "0.0.0"
+_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"]
+_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}/valid.jsonl']
+        }
+    for k in _DATA_TYPE
+}
+
+
+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):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "tags": datasets.Sequence(datasets.Value("int32")),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOME_PAGE,
+            citation=_CITATION,
+        )