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# The majority of this file was taken and adapted from this file, on 6/3/21: https://github.com/huggingface/datasets/blob/master/datasets/snli/snli.py
# License reproduced from the original code:
# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# The changes to the file include:
# Changing all of the parameters to reflect childes data and information
# Changing the dl_manager to be essentially unused in split_generators and loading from text files instead
# Changing generate_examples to load directly from text files and clean the lines of text.
import datasets
import os
class Childes(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="childes_data",
version=datasets.Version("1.0.0", ""),
description="Childes language modeling dataset",
)
]
def _info(self):
citation_text = """\\
@article{sanchez2019childes,
title={childes-db: A flexible and reproducible interface to the child language data exchange system},
author={Sanchez, Alessandro and Meylan, Stephan C and Braginsky, Mika and MacDonald, Kyle E and Yurovsky, Daniel and Frank, Michael C},
journal={Behavior research methods},
volume={51},
number={4},
pages={1928--1941},
year={2019},
publisher={Springer}}
"""
return datasets.DatasetInfo(
description = "CHILDES data for language modeling",
citation = citation_text,
# 6/3 Citation info is directly taken from Google Scholar
features=datasets.Features(
{
"text": datasets.Value("string"),
}
),
# No default supervised_keys (as we have to pass both premise
# and hypothesis as input).
homepage="https://childes-db.stanford.edu/",
)
def _split_generators(self, download_helper):
paths = download_helper.download_and_extract({
'train' : 'https://www.dropbox.com/s/dl/i282barrzlari08/train.txt?dl=1',
'val' : 'https://www.dropbox.com/s/gx0rngo3v5mvlcf/validation.txt?dl=1',
})
list_datasets = []
phases = ['train', 'val']
dataset_names = [
datasets.Split.TRAIN,
datasets.Split.VALIDATION,
]
for phase, phase_name in zip(phases, dataset_names):
this_dataset = datasets.SplitGenerator(
name=phase_name, gen_kwargs={"file_path": paths[phase]}
)
list_datasets.append(this_dataset)
return list_datasets
def _generate_examples(self, file_path):
# 6/17: https://stackoverflow.com/questions/10406135/unicodedecodeerror-ascii-codec-cant-decode-byte-0xd1-in-position-2-ordinal
with open(file_path, 'r', encoding="utf-8") as f:
for idx, line in enumerate(f.readlines()):
yield idx, {"text" : line}
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