Datasets:
File size: 7,235 Bytes
f9b841a 49f9a08 f9b841a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 |
# 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
#
# 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 PersonalDialog dataset is a large-scale multi-turn Chinese dialogue dataset containing various traits from a large number of speakers.
We are releasing about 5M sessions of carefully filtered dialogues.
Each utterance in PersonalDialog is associated with a speaker marked with traits like Gender, Location, Interest Tags.
"""
import json
import datasets
_CITATION = """\
@article{zheng2019personalized,
title = {Personalized dialogue generation with diversified traits},
author = {Zheng, Yinhe and Chen, Guanyi and Huang, Minlie and Liu, Song and Zhu, Xuan},
journal = {arXiv preprint arXiv:1901.09672},
year = {2019}
}
@inproceedings{zheng2020pre,
title = {A pre-training based personalized dialogue generation model with persona-sparse data},
author = {Zheng, Yinhe and Zhang, Rongsheng and Huang, Minlie and Mao, Xiaoxi},
booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {34},
number = {05},
pages = {9693--9700},
year = {2020}
}
"""
_DESCRIPTION = """\
The PersonalDialog dataset is a large-scale multi-turn Chinese dialogue dataset containing various traits from a large number of speakers.
We are releasing about 5M sessions of carefully filtered dialogues.
Each utterance in PersonalDialog is associated with a speaker marked with traits like Gender, Location, Interest Tags.
"""
_HOMEPAGE = "https://github.com/silverriver/PersonalDilaog"
_LICENSE = "MIT"
_URLS = {
"valid": [
"https://huggingface.co/datasets/silver/personal_dialog/resolve/main/dev_biased.jsonl.gz",
"https://huggingface.co/datasets/silver/personal_dialog/resolve/main/dev_random.jsonl.gz",
],
"train": "https://huggingface.co/datasets/silver/personal_dialog/resolve/main/dialogues_train.jsonl.gz",
"test": [
"https://huggingface.co/datasets/silver/personal_dialog/resolve/main/test_biased.jsonl.gz",
"https://huggingface.co/datasets/silver/personal_dialog/resolve/main/test_random.jsonl.gz",
],
}
class PersonalDialog(datasets.GeneratorBasedBuilder):
"""Chinese Dialogues with Personal Traits."""
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"dialog": [datasets.Value("string")],
"profile": [
{
"tag": [datasets.Value("string")],
"loc": datasets.Value("string"),
"gender": datasets.Value("string"),
}
],
"uid": [datasets.Value("int32")],
"responder_profile": {
"tag": [datasets.Value("string")],
"loc": datasets.Value("string"),
"gender": datasets.Value("string"),
},
"golden_response": datasets.Value("string"),
"is_biased": datasets.Value("bool"),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# 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
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
urls = _URLS
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_files": [data_dir["train"]],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"data_files": [data_dir["valid"][0], data_dir["valid"][1]],
"split": "valid",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_files": [data_dir["test"][0], data_dir["test"][1]],
"split": "test",
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, data_files, split):
id = 0
for file_i, data_file in enumerate(data_files):
with open(data_file, encoding="utf-8") as f:
for line in f:
line = line.strip()
if len(line) == 0:
continue
line = json.loads(line)
profile = [
{"tag": i["tag"][0].split(";"), "loc": i["loc"], "gender": i["gender"]}
for i in line["profile"]
]
dialog = [i[0] for i in line["dialog"]]
if split == "train":
yield id, {
"dialog": dialog,
"profile": profile,
"uid": line["uid"],
"responder_profile": None,
"golden_response": None,
"is_biased": None,
}
else:
yield id, {
"dialog": dialog,
"profile": profile,
"uid": line["uid"],
"responder_profile": {
"tag": line["responder_profile"]["tag"][0].split(";"),
"loc": line["responder_profile"]["loc"],
"gender": line["responder_profile"]["gender"],
},
"golden_response": line["golden_response"][0],
"is_biased": True if file_i == 0 else False,
}
id += 1
|