Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
Tags:
long context
File size: 3,865 Bytes
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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 9 unbalanced classes, 35k Patents and summaries divided into 3 splits: train (25k), val (5k) and test (5k).
Data are sampled from "BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization." by Eva Sharma, Chen Li and Lu Wang
See: https://aclanthology.org/P19-1212.pdf
See: https://evasharma.github.io/bigpatent/
"""
_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._TRAIN_FILE)
val_path = dl_manager.download_and_extract(self._VAL_FILE)
test_path = dl_manager.download_and_extract(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}
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