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import json |
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import os |
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import datasets |
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from datasets.tasks import TextClassification |
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_CITATION = None |
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_DESCRIPTION = """ |
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Patent Classification Dataset: a classification of Patents (9 classes). |
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It contains 9 unbalanced classes, 35k Patents and summaries divided into 3 splits: train (25k), val (5k) and test (5k). |
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Data are sampled from "BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization." by Eva Sharma, Chen Li and Lu Wang |
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See: https://aclanthology.org/P19-1212.pdf |
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See: https://evasharma.github.io/bigpatent/ |
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""" |
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_LABELS = [ |
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"Human Necessities", |
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"Performing Operations; Transporting", |
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"Chemistry; Metallurgy", |
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"Textiles; Paper", |
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"Fixed Constructions", |
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"Mechanical Engineering; Lightning; Heating; Weapons; Blasting", |
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"Physics", |
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"Electricity", |
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"General tagging of new or cross-sectional technology", |
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] |
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class PatentClassificationConfig(datasets.BuilderConfig): |
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"""BuilderConfig for PatentClassification.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for PatentClassification. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(PatentClassificationConfig, self).__init__(**kwargs) |
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class PatentClassificationDataset(datasets.GeneratorBasedBuilder): |
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"""PatentClassification Dataset: classification of Patents (9 classes).""" |
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_DOWNLOAD_URL = "https://huggingface.co/datasets/ccdv/patent-classification/resolve/main/" |
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_TRAIN_FILE = "train_data.txt" |
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_VAL_FILE = "val_data.txt" |
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_TEST_FILE = "test_data.txt" |
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_LABELS_DICT = {label: i for i, label in enumerate(_LABELS)} |
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BUILDER_CONFIGS = [ |
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PatentClassificationConfig( |
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name="patent", |
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version=datasets.Version("1.0.0"), |
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description="Patent Classification Dataset: A classification task of Patents (9 classes)", |
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), |
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PatentClassificationConfig( |
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name="abstract", |
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version=datasets.Version("1.0.0"), |
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description="Patent Classification Dataset: A classification task of Patents with abstracts (9 classes)", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "patent" |
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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.features.ClassLabel(names=_LABELS), |
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} |
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), |
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supervised_keys=None, |
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citation=_CITATION, |
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task_templates=[TextClassification( |
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text_column="text", label_column="label")], |
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) |
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def _split_generators(self, dl_manager): |
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train_path = dl_manager.download_and_extract(self._TRAIN_FILE) |
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val_path = dl_manager.download_and_extract(self._VAL_FILE) |
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test_path = dl_manager.download_and_extract(self._TEST_FILE) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_path} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={"filepath": test_path} |
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), |
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] |
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def _generate_examples(self, filepath): |
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"""Generate PatentClassification examples.""" |
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with open(filepath, encoding="utf-8") as f: |
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for id_, row in enumerate(f): |
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data = json.loads(row) |
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label = self._LABELS_DICT[data["label"]] |
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if self.config.name == "abstract": |
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text = data["abstract"] |
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else: |
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text = data["description"] |
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yield id_, {"text": text, "label": label} |
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