155 lines
6.8 KiB

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""
import csv
import json
import os
import datasets
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
title = {A great new dataset},
author={huggingface, Inc.
# TODO: Add description of the dataset here
# You can copy an official description
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
# TODO: Add a link to an official homepage for the dataset here
# TODO: Add the licence for the dataset here if you can find it
# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
"train": "",
"predict": "",
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class Kyk(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("1.1.0")
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
# If you need to make complex sub-parts in the datasets with configurable options
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('my_dataset', 'first_domain')
# data = datasets.load_dataset('my_dataset', 'second_domain')
datasets.BuilderConfig(name="part_1", version=VERSION,
description="part 1"),
datasets.BuilderConfig(name="part_2", version=VERSION,
description="part 2"),
# It's not mandatory to have a default configuration. Just use one if it make sense.
def _info(self):
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
if == "part_1": # This is the name of the configuration selected in BUILDER_CONFIGS above
features = datasets.Features(
"text": datasets.Value("string")
# These are the features of your dataset like images, labels ...
else: # This is an example to show how to have different features for "first_domain" and "second_domain"
features = datasets.Features(
"text": datasets.Value("string"),
# These are the features of your dataset like images, labels ...
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# Homepage of the dataset for documentation
# License for the dataset if available
# Citation for the dataset
def _split_generators(self, dl_manager: datasets.DownloadManager):
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in
# dl_manager is a that can be used to download and extract URLS
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
data_dir = dl_manager.download_and_extract(_URLS)
return [
# These kwargs will be passed to _generate_examples
"filepath": data_dir["train"],
"split": "train",
# name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
"filepath": data_dir["predict"],
"split": "predict",
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
with open(filepath, encoding="utf-8") as f:
for key, row in enumerate(f):
yield key, {
"text": row.strip(), # strip末尾换行符