Datasets:

silver
/

personal_dialog

	# 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