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import json |
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import os |
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import datasets |
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from datasets.download.download_manager import DownloadManager |
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from datasets.tasks import TextClassification |
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logger = datasets.logging.get_logger(__name__) |
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_DESCRIPTION = "`azaheadhealth`" |
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_VARIANTS = { |
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"micro": { |
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"version": "1.0.0", |
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"splits": { |
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"train": "data/micro_train.json", |
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"test": "data/micro_test.json" |
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} |
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}, |
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"small": { |
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"version": "1.0.0", |
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"splits": { |
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"train": "data/small_train.json", |
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"test": "data/small_test.json" |
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} |
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}, |
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} |
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class AZAheadHealthConfig(datasets.BuilderConfig): |
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"""BuildConfig for AZAheadHealth""" |
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def __init__(self, **kwargs): |
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super(AZAheadHealthConfig, self).__init__(**kwargs) |
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class AZAheadHealth(datasets.GeneratorBasedBuilder): |
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"""AZAheadHealth: A custom dataset in the health domain for the AZAhead project.""" |
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use_auth_token = True |
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BUILDER_CONFIGS = [ |
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AZAheadHealthConfig(name=name, version=config["version"]) |
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for name, config in _VARIANTS.items() |
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] |
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DEFAULT_CONFIG_NAME = "small" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"text": datasets.Value("string"), |
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"label": datasets.ClassLabel(num_classes=2, names=["NEGATIVE", "POSITIVE"]), |
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} |
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), |
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supervised_keys=None, |
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task_templates=[ |
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TextClassification( |
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text_column="text", label_column="label" |
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) |
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] |
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) |
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def _split_generators(self, dl_manager: DownloadManager): |
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downloaded_files = dl_manager.download(_VARIANTS[self.config.name]["splits"]) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}) |
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] |
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def _generate_examples(self, filepath): |
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logger.info("generating examples from = %s", filepath) |
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with open(filepath) as fin: |
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content = json.load(fin) |
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key = 0 |
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for sample in content: |
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yield key, { |
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"text": sample["text"], |
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"label": sample["label"] |
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} |
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key+=1 |
