patent-classification.py

import json
import os

import datasets
from datasets.tasks import TextClassification

_CITATION = None


_DESCRIPTION = """
 Patent Classification Dataset: a classification of Patents (9 classes).
 It contains 11 slightly unbalanced classes, 25k Patents and summaries divided into 3 splits: train (25k), val (5k) and test (5k).
 Copied from "Long Document Classification From Local Word Glimpses via Recurrent Attention Learning" by JUN HE LIQUN WANG LIU LIU, JIAO FENG AND HAO WU
 See: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8675939
 See: https://github.com/LiqunW/Long-document-dataset
"""

_LABELS = [
    "Human Necessities",
    "Performing Operations; Transporting",
    "Chemistry; Metallurgy",
    "Textiles; Paper",
    "Fixed Constructions",
    "Mechanical Engineering; Lightning; Heating; Weapons; Blasting",
    "Physics",
    "Electricity",
    "General tagging of new or cross-sectional technology",
]

class PatentClassificationConfig(datasets.BuilderConfig):
    """BuilderConfig for PatentClassification."""

    def __init__(self, **kwargs):
        """BuilderConfig for PatentClassification.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(PatentClassificationConfig, self).__init__(**kwargs)


class PatentClassificationDataset(datasets.GeneratorBasedBuilder):
    """PatentClassification Dataset: classification of Patents (9 classes)."""
    
    _DOWNLOAD_URL = "https://huggingface.co/datasets/ccdv/patent-classification/resolve/main/"
    _TRAIN_FILE = "train_data.txt"
    _VAL_FILE = "val_data.txt"
    _TEST_FILE = "test_data.txt"
    _LABELS_DICT = {label: i for i, label in enumerate(_LABELS)}

    BUILDER_CONFIGS = [
        PatentClassificationConfig(
            name="patent",
            version=datasets.Version("1.0.0"),
            description="Patent Classification Dataset: A classification task of Patents (9 classes)",
        ),

        PatentClassificationConfig(
            name="abstract",
            version=datasets.Version("1.0.0"),
            description="Patent Classification Dataset: A classification task of Patents with abstracts (9 classes)",
        ),
    ]

    DEFAULT_CONFIG_NAME = "patent"

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "label": datasets.features.ClassLabel(names=_LABELS),
                }
            ),
            supervised_keys=None,
            citation=_CITATION,
            task_templates=[TextClassification(
                text_column="text", label_column="label")],
        )

    def _split_generators(self, dl_manager):
        train_path = dl_manager.download_and_extract(self._DOWNLOAD_URL + self._TRAIN_FILE)
        val_path = dl_manager.download_and_extract(self._DOWNLOAD_URL + self._VAL_FILE)
        test_path = dl_manager.download_and_extract(self._DOWNLOAD_URL + self._TEST_FILE)
        
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_path}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}
            ),
        ]
    
    def _generate_examples(self, filepath):
        """Generate PatentClassification examples."""
        with open(filepath, encoding="utf-8") as f:
            for id_, row in enumerate(f):
                data = json.loads(row)
                label = self._LABELS_DICT[data["label"]]

                if self.config.name == "abstract":
                    text = data["abstract"]
                else:
                    text = data["description"]
                yield id_, {"text": text, "label": label}