Yiran Guo
commited on
Commit
•
744397b
1
Parent(s):
1d16525
Add dataset files and load script
Browse files- .gitattributes +1 -0
- README.md +49 -3
- data.csv +3 -0
- privacy_detection.py +106 -0
.gitattributes
CHANGED
@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.jpg filter=lfs diff=lfs merge=lfs -text
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*.jpeg filter=lfs diff=lfs merge=lfs -text
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*.webp filter=lfs diff=lfs merge=lfs -text
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*.jpg filter=lfs diff=lfs merge=lfs -text
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*.jpeg filter=lfs diff=lfs merge=lfs -text
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*.webp filter=lfs diff=lfs merge=lfs -text
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data.csv filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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-
task_categories:
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-
- token-classification
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language:
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- zh
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-
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---
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language:
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- zh
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task_categories:
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- token-classification
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dataset_info:
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config_name: privacy_detection
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features:
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- name: id
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dtype: string
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- name: tokens
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sequence: string
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- name: ner_tags
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sequence:
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class_label:
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names:
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'0': O
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'1': B-position
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'2': I-position
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'3': B-name
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'4': I-name
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'5': B-movie
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'6': I-movie
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'7': B-organization
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'8': I-organization
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'9': B-company
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'10': I-company
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'11': B-book
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'12': I-book
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'13': B-address
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'14': I-address
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'15': B-scene
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'16': I-scene
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'17': B-mobile
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'18': I-mobile
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'19': B-email
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'20': I-email
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'21': B-game
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'22': I-game
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'23': B-government
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'24': I-government
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'25': B-QQ
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'26': I-QQ
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'27': B-vx
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'28': I-vx
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splits:
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- name: train
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num_bytes: 4899635
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num_examples: 2515
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download_size: 3290405
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dataset_size: 4899635
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---
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data.csv
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:690245ea213cab30e199ffd9076ac6c8762665573b19c37e9007bcdda8f24c36
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size 3290405
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privacy_detection.py
ADDED
@@ -0,0 +1,106 @@
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# coding=utf-8
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import datasets
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import pandas as pd
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logger = datasets.logging.get_logger(__name__)
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_DESCRIPTION = """\
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privacy detection dataset, which includes the following categories of privacy information: [position, name, movie, organization, company, book, address, scene, mobile, email, game, government, QQ, vx].
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The dataset consists of 3 columns. The first column is id, the second column is the list of text characters, and the third column is the list of privacy entity annotations. The entity annotation format is such that each entity's leading character is labeled as B-TYPE, the internal characters of the entity are labeled as I-TYPE, and the character is labeled O if it does not belong to any entity.
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For more details see: https://www.datafountain.cn/competitions/472.
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"""
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_URL = "data.csv"
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class PrivacyDetectionConfig(datasets.BuilderConfig):
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def __init__(self, **kwargs):
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super(PrivacyDetectionConfig, self).__init__(**kwargs)
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class PrivacyDectection(datasets.GeneratorBasedBuilder):
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"""PrivacyDectection dataset."""
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BUILDER_CONFIGS = [
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PrivacyDetectionConfig(
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name="privacy_detection",
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version=datasets.Version("1.0.0"),
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description="privacy detection dataset",
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),
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]
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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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"id": datasets.Value("string"),
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"tokens": datasets.Sequence(datasets.Value("string")),
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"ner_tags": datasets.Sequence(
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datasets.features.ClassLabel(
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names=[
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"O",
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"B-position",
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"I-position",
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"B-name",
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"I-name",
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"B-movie",
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"I-movie",
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"B-organization",
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"I-organization",
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"B-company",
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"I-company",
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"B-book",
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"I-book",
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"B-address",
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"I-address",
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"B-scene",
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"I-scene",
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"B-mobile",
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"I-mobile",
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"B-email",
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"I-email",
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"B-game",
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"I-game",
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"B-government",
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"I-government",
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"B-QQ",
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"I-QQ",
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"B-vx",
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"I-vx",
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]
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)
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),
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}
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),
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supervised_keys=None,
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homepage="https://www.datafountain.cn/competitions/472",
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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downloaded_file = dl_manager.download_and_extract(_URL)
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data_files = {
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"train": downloaded_file,
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}
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}
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)
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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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data = pd.read_csv(filepath)
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for _, row in data.iterrows():
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id_ = row["id"]
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tokens = eval(row["tokens"])
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ner_tags = eval(row["ner_tags"])
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yield id_, {
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"id": id_,
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"tokens": tokens,
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"ner_tags": ner_tags,
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}
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