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"""we're testin'""" |
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import datasets |
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import json |
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class TestDatasetConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Test Dataset for testing HF parsing""" |
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def __init__( |
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self, |
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text_features, |
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foo="foo", |
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process_label=lambda x: x, |
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**kwargs, |
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): |
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"""BuilderConfig for TestDatset. |
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Args: |
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text_features: `dict[string, string]`, map from the name of the feature |
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dict for each text field to the name of the column in the tsv file |
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label_column: `string`, name of the column in the tsv file corresponding |
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to the label |
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data_dir: `string`, the path to the folder containing the tsv files in the |
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downloaded zip |
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label_classes: `list[string]`, the list of classes if the label is |
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categorical. If not provided, then the label will be of type |
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`datasets.Value('float32')`. |
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process_label: `Function[string, any]`, function taking in the raw value |
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of the label and processing it to the form required by the label feature |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(TestDatasetConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) |
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self.text_features = text_features |
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self.foo = foo |
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self.process_label = process_label |
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class TestDatasetEvals(datasets.GeneratorBasedBuilder): |
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"""The General Language Understanding Evaluation (GLUE) benchmark.""" |
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BUILDER_CONFIGS = [ |
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TestDatasetConfig( |
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name="juggernaut", |
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description= "this is a test dataset for our unit test intergrating HF datasets" , |
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text_features={"context": "context", "continuation": "continuation"}, |
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data_dir="heroes", |
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), |
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TestDatasetConfig( |
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name="invoker", |
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description= "this is a test dataset for our unit test intergrating HF datasets" , |
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text_features={"quas": "quas", "wex": "wex", "exort": "exort", "spell": "spell"}, |
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data_dir="heroes", |
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), |
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] |
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def _info(self): |
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features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()} |
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features["idx"] = datasets.Value("int32") |
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return datasets.DatasetInfo( |
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description=self.config.description, |
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features=datasets.Features(features), |
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) |
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def _split_generators(self, dl_manager): |
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constructed_filepath = self.construct_filepath() |
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data_file = dl_manager.download(constructed_filepath) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"data_file": data_file, |
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}, |
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), |
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] |
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def construct_filepath(self): |
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return self.config.name + '/data.jsonl' |
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def _generate_examples(self, data_file): |
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with open(data_file, encoding="utf8") as f: |
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for n, row in enumerate(f): |
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data = json.loads(row) |
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example = {feat: data[col] for feat, col in self.config.text_features.items()} |
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example["idx"] = n |
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yield example["idx"], example |
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