Datasets:
Sub-tasks:
dialogue-generation
Languages:
Chinese
Multilinguality:
monolingual
Size Categories:
10M<n<100M
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
ArXiv:
Tags:
License:
File size: 7,235 Bytes
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# 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
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