Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
json
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
100K - 1M
Tags:
pii-detection
License:
camille-vanhoffelen commited on
Commit ·
15bd4f4
1
Parent(s): e8eb2a7
fix: remove dataset script
Browse files
privy.py
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import csv
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import json
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import os
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import datasets
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_DESCRIPTION="This labelled PII dataset consists of protocol traces (JSON, SQL (PostgreSQL, MySQL), HTML, and XML) generated from OpenAPI specifications and includes 60+ PII types."
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_CITATION="""
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@online{WinNT,
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author = {Benjamin Kilimnik},
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title = {{Privy} Synthetic PII Protocol Trace Dataset},
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year = 2022,
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url = {https://huggingface.co/datasets/beki/privy},
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}
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"""
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_HOMEPAGE = "https://github.com/pixie-io/pixie/tree/main/src/datagen/pii/privy/privy"
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_LICENSE = "MIT"
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_URL = "https://huggingface.co/datasets/beki/privy/resolve/main/privy-dataset.zip"
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class Privy(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.0.0")
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# You will be able to load one or the other configurations in the following list with
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# data = datasets.load_dataset('my_dataset', 'first_domain')
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# data = datasets.load_dataset('my_dataset', 'second_domain')
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="small", version=VERSION, description="Privy small"),
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datasets.BuilderConfig(name="large", version=VERSION, description="Privy large"),
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]
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DEFAULT_CONFIG_NAME = "small"
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def _info(self):
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if self.config.name == "large":
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features = datasets.Features(
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{
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"full_text": datasets.Value("string"),
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"masked": datasets.Value("string"),
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"spans": datasets.Sequence(datasets.Value("string")),
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"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-O",
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"I-O",
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"L-O",
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"U-O",
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"B-PERSON",
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"I-PERSON",
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"L-PERSON",
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"U-PERSON",
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"B-LOCATION",
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"I-LOCATION",
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"L-LOCATION",
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"U-LOCATION",
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"B-ORGANIZATION",
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"I-ORGANIZATION",
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"L-ORGANIZATION",
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"U-ORGANIZATION",
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"B-NRP",
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"I-NRP",
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"L-NRP",
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"U-NRP",
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"B-DATE_TIME",
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"I-DATE_TIME",
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"L-DATE_TIME",
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"U-DATE_TIME",
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"B-CREDIT_CARD",
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"I-CREDIT_CARD",
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"L-CREDIT_CARD",
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"U-CREDIT_CARD",
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"B-URL",
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"I-URL",
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"L-URL",
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"U-URL",
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"B-IBAN_CODE",
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"I-IBAN_CODE",
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"L-IBAN_CODE",
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"U-IBAN_CODE",
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"B-US_BANK_NUMBER",
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"I-US_BANK_NUMBER",
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"L-US_BANK_NUMBER",
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"U-US_BANK_NUMBER",
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"B-PHONE_NUMBER",
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"I-PHONE_NUMBER",
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"L-PHONE_NUMBER",
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"U-PHONE_NUMBER",
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"B-US_SSN",
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"I-US_SSN",
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"L-US_SSN",
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"U-US_SSN",
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"B-US_PASSPORT",
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"I-US_PASSPORT",
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"L-US_PASSPORT",
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"U-US_PASSPORT",
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"B-US_DRIVER_LICENSE",
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"I-US_DRIVER_LICENSE",
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"L-US_DRIVER_LICENSE",
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"U-US_DRIVER_LICENSE",
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"B-US_LICENSE_PLATE",
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"I-US_LICENSE_PLATE",
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"L-US_LICENSE_PLATE",
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"U-US_LICENSE_PLATE",
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"B-IP_ADDRESS",
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"I-IP_ADDRESS",
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"L-IP_ADDRESS",
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"U-IP_ADDRESS",
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"B-US_ITIN",
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"I-US_ITIN",
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"L-US_ITIN",
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"U-US_ITIN",
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"B-EMAIL_ADDRESS",
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"I-EMAIL_ADDRESS",
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"L-EMAIL_ADDRESS",
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"U-EMAIL_ADDRESS",
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"B-TITLE",
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"I-TITLE",
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"L-TITLE",
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"U-TITLE",
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"B-COORDINATE",
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"I-COORDINATE",
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"L-COORDINATE",
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"U-COORDINATE",
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"B-IMEI",
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"I-IMEI",
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"L-IMEI",
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"U-IMEI",
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-
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"B-PASSWORD",
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"I-PASSWORD",
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"L-PASSWORD",
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"U-PASSWORD",
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"B-LICENSE_PLATE",
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"I-LICENSE_PLATE",
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"L-LICENSE_PLATE",
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"U-LICENSE_PLATE",
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"B-CURRENCY",
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"I-CURRENCY",
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"L-CURRENCY",
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"U-CURRENCY",
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"B-FINANCIAL",
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"I-FINANCIAL",
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"L-FINANCIAL",
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"U-FINANCIAL",
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"B-ROUTING_NUMBER",
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"I-ROUTING_NUMBER",
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"L-ROUTING_NUMBER",
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"U-ROUTING_NUMBER",
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"B-SWIFT_CODE",
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"I-SWIFT_CODE",
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"L-SWIFT_CODE",
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"U-SWIFT_CODE",
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"B-MAC_ADDRESS",
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"I-MAC_ADDRESS",
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"L-MAC_ADDRESS",
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"U-MAC_ADDRESS",
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"B-AGE",
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"I-AGE",
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"L-AGE",
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"U-AGE",
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]
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)
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),
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"tokens": datasets.Sequence(datasets.Value("string")),
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"template_id": datasets.Value("int32"),
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"metadata": datasets.Value("int32"),
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}
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)
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if self.config.name == "small":
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features = datasets.Features(
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{
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"full_text": datasets.Value("string"),
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"masked": datasets.Value("string"),
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"spans": datasets.Sequence(datasets.Value("string")),
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"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-O",
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"I-O",
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"L-O",
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"U-O",
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"B-PER",
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"I-PER",
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"L-PER",
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"U-PER",
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"B-LOC",
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"I-LOC",
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"L-LOC",
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"U-LOC",
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"B-ORG",
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"I-ORG",
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"L-ORG",
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"U-ORG",
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"B-NRP",
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"I-NRP",
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"L-NRP",
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"U-NRP",
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"B-DATE_TIME",
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"I-DATE_TIME",
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"L-DATE_TIME",
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"U-DATE_TIME",
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-
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"B-CREDIT_CARD",
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"I-CREDIT_CARD",
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"L-CREDIT_CARD",
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"U-CREDIT_CARD",
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"B-URL",
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"I-URL",
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"L-URL",
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"U-URL",
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"B-IBAN_CODE",
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"I-IBAN_CODE",
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"L-IBAN_CODE",
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"U-IBAN_CODE",
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"B-US_BANK_NUMBER",
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"I-US_BANK_NUMBER",
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"L-US_BANK_NUMBER",
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"U-US_BANK_NUMBER",
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"B-PHONE_NUMBER",
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"I-PHONE_NUMBER",
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"L-PHONE_NUMBER",
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"U-PHONE_NUMBER",
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"B-US_SSN",
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"I-US_SSN",
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"L-US_SSN",
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"U-US_SSN",
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"B-US_PASSPORT",
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"I-US_PASSPORT",
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"L-US_PASSPORT",
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"U-US_PASSPORT",
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-
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"B-US_DRIVER_LICENSE",
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"I-US_DRIVER_LICENSE",
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"L-US_DRIVER_LICENSE",
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"U-US_DRIVER_LICENSE",
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"B-US_LICENSE_PLATE",
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| 292 |
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"I-US_LICENSE_PLATE",
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"L-US_LICENSE_PLATE",
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"U-US_LICENSE_PLATE",
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-
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"B-IP_ADDRESS",
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"I-IP_ADDRESS",
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"L-IP_ADDRESS",
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"U-IP_ADDRESS",
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"B-US_ITIN",
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"I-US_ITIN",
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"L-US_ITIN",
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"U-US_ITIN",
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"B-EMAIL_ADDRESS",
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"I-EMAIL_ADDRESS",
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"L-EMAIL_ADDRESS",
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"U-EMAIL_ADDRESS",
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-
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"B-TITLE",
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"I-TITLE",
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"L-TITLE",
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"U-TITLE",
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"B-COORDINATE",
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"I-COORDINATE",
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"L-COORDINATE",
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"U-COORDINATE",
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-
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"B-IMEI",
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"I-IMEI",
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"L-IMEI",
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"U-IMEI",
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-
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"B-PASSWORD",
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"I-PASSWORD",
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"L-PASSWORD",
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| 329 |
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"U-PASSWORD",
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| 330 |
-
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"B-LICENSE_PLATE",
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"I-LICENSE_PLATE",
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"L-LICENSE_PLATE",
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"U-LICENSE_PLATE",
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-
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"B-CURRENCY",
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| 337 |
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"I-CURRENCY",
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| 338 |
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"L-CURRENCY",
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| 339 |
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"U-CURRENCY",
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| 340 |
-
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"B-FINANCIAL",
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| 342 |
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"I-FINANCIAL",
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| 343 |
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"L-FINANCIAL",
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"U-FINANCIAL",
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-
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"B-ROUTING_NUMBER",
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| 347 |
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"I-ROUTING_NUMBER",
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| 348 |
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"L-ROUTING_NUMBER",
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| 349 |
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"U-ROUTING_NUMBER",
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| 350 |
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| 351 |
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"B-SWIFT_CODE",
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| 352 |
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"I-SWIFT_CODE",
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| 353 |
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"L-SWIFT_CODE",
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"U-SWIFT_CODE",
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-
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"B-MAC_ADDRESS",
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| 357 |
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"I-MAC_ADDRESS",
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| 358 |
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"L-MAC_ADDRESS",
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| 359 |
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"U-MAC_ADDRESS",
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| 360 |
-
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| 361 |
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"B-AGE",
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| 362 |
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"I-AGE",
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| 363 |
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"L-AGE",
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| 364 |
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"U-AGE",
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]
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)
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),
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"tokens": datasets.Sequence(datasets.Value("string")),
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"template_id": datasets.Value("int32"),
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"metadata": datasets.Value("int32"),
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}
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)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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# supervised_keys=("sentence", "label"),
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# Homepage of the dataset for documentation
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homepage=_HOMEPAGE,
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# License for the dataset if available
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license=_LICENSE,
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# Citation for the dataset
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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data_dir = dl_manager.download_and_extract(_URL)
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size = "small"
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if self.config.name == "large": # This is the name of the configuration selected in BUILDER_CONFIGS above
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size = "large"
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": os.path.join(data_dir, f"train-{size}.json"),
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"split": "train",
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": os.path.join(data_dir, f"dev-{size}.json"),
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"split": "dev",
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},
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),
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| 408 |
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datasets.SplitGenerator(
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| 409 |
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name=datasets.Split.TEST,
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| 410 |
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# These kwargs will be passed to _generate_examples
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| 411 |
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gen_kwargs={
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| 412 |
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"filepath": os.path.join(data_dir, f"test-{size}.json"),
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"split": "test"
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},
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),
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]
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-
|
| 418 |
-
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
|
| 419 |
-
def _generate_examples(self, filepath, split):
|
| 420 |
-
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
|
| 421 |
-
with open(filepath, encoding="utf-8") as f:
|
| 422 |
-
dataset = json.load(f)
|
| 423 |
-
for key, row in enumerate(dataset):
|
| 424 |
-
# Yields examples as (key, example) tuples
|
| 425 |
-
yield key, {
|
| 426 |
-
"tokens": row["tokens"],
|
| 427 |
-
"tags": row["tags"],
|
| 428 |
-
"full_text": row["full_text"],
|
| 429 |
-
"spans": row["spans"],
|
| 430 |
-
"masked": row["masked"],
|
| 431 |
-
"template_id": row["template_id"],
|
| 432 |
-
"metadata": row["metadata"],
|
| 433 |
-
}
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