Datasets:

silver
/

mmchat

	# 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.
	"""
	MMChat is a large-scale dialogue dataset that contains image-grounded dialogues in Chinese.
	Each dialogue in MMChat is associated with one or more images (maximum 9 images per dialogue).
	We design various strategies to ensure the quality of the dialogues in MMChat.
	"""

	import json

	import datasets


	_CITATION = """\
	@inproceedings{zheng2022MMChat,
	author = {Zheng, Yinhe and Chen, Guanyi and Liu, Xin and Sun, Jian},
	title = {MMChat: Multi-Modal Chat Dataset on Social Media},
	booktitle = {Proceedings of The 13th Language Resources and Evaluation Conference},
	year = {2022},
	publisher = {European Language Resources Association},
	}

	@inproceedings{wang2020chinese,
	title = {A Large-Scale Chinese Short-Text Conversation Dataset},
	author = {Wang, Yida and Ke, Pei and Zheng, Yinhe and Huang, Kaili and Jiang, Yong and Zhu, Xiaoyan and Huang, Minlie},
	booktitle = {NLPCC},
	year = {2020},
	url = {https://arxiv.org/abs/2008.03946}
	}
	"""

	_DESCRIPTION = """\
	MMChat is a large-scale dialogue dataset that contains image-grounded dialogues in Chinese.
	Each dialogue in MMChat is associated with one or more images (maximum 9 images per dialogue).
	We design various strategies to ensure the quality of the dialogues in MMChat.
	"""

	_HOMEPAGE = "https://github.com/silverriver/MMChat"

	_LICENSE = "MIT"

	_URLS = {
	"mmchat": {
	"train": [
	"https://huggingface.co/datasets/silver/mmchat/resolve/main/mmchat/dialog_train.jsonl.gz",
	"https://huggingface.co/datasets/silver/mmchat/resolve/main/mmchat/img_url_train.jsonl.gz",
	"https://huggingface.co/datasets/silver/mmchat/resolve/main/mmchat/weibo_train.jsonl.gz",
	],
	"dev": [
	"https://huggingface.co/datasets/silver/mmchat/resolve/main/mmchat/dialog_dev.jsonl.gz",
	"https://huggingface.co/datasets/silver/mmchat/resolve/main/mmchat/img_url_dev.jsonl.gz",
	"https://huggingface.co/datasets/silver/mmchat/resolve/main/mmchat/weibo_dev.jsonl.gz",
	],
	"test": [
	"https://huggingface.co/datasets/silver/mmchat/resolve/main/mmchat/dialog_test.jsonl.gz",
	"https://huggingface.co/datasets/silver/mmchat/resolve/main/mmchat/img_url_test.jsonl.gz",
	"https://huggingface.co/datasets/silver/mmchat/resolve/main/mmchat/weibo_test.jsonl.gz",
	],
	},
	"mmchat_hf": [
	"https://huggingface.co/datasets/silver/mmchat/resolve/main/mmchat_hf/dialog.jsonl.gz",
	"https://huggingface.co/datasets/silver/mmchat/resolve/main/mmchat_hf/weibo_img_expanded_url.jsonl.gz",
	"https://huggingface.co/datasets/silver/mmchat/resolve/main/mmchat_hf/weibo.jsonl.gz",
	"https://huggingface.co/datasets/silver/mmchat/resolve/main/mmchat_hf/human_annotation.jsonl.gz",
	],
	"mmchat_raw": [
	"https://huggingface.co/datasets/silver/mmchat/resolve/main/mmchat_raw/dialog_raw.jsonl.gz",
	"https://huggingface.co/datasets/silver/mmchat/resolve/main/mmchat_raw/weibo_img_expanded_url_raw.jsonl.gz",
	"https://huggingface.co/datasets/silver/mmchat/resolve/main/mmchat_raw/weibo_raw.jsonl.gz",
	],
	"mmchat_lccc_filtered": [
	"https://huggingface.co/datasets/silver/mmchat/resolve/main/mmchat_lccc_filtered/dialog_lccc_flt.jsonl.gz",
	"https://huggingface.co/datasets/silver/mmchat/resolve/main/mmchat_lccc_filtered/weibo_img_expanded_url_lccc_flt.jsonl.gz",
	"https://huggingface.co/datasets/silver/mmchat/resolve/main/mmchat_lccc_filtered/weibo_lccc_flt.jsonl.gz",
	],
	}


	class MMChat(datasets.GeneratorBasedBuilder):
	"""Multi-Modal Chat Dataset."""

	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="mmchat", version=VERSION, description="The MMChat dataset"),
	datasets.BuilderConfig(name="mmchat_hf", version=VERSION, description="Human filtered version of MMChat"),
	datasets.BuilderConfig(name="mmchat_raw", version=VERSION, description="Raw dialogues in MMChat"),
	datasets.BuilderConfig(name="mmchat_lccc_filtered", version=VERSION, description="LCCC filtered MMChat"),
	]

	DEFAULT_CONFIG_NAME = "mmchat"

	def _info(self):
	if self.config.name in ["mmchat", "mmchat_raw", "mmchat_lccc_filtered"]:
	features = datasets.Features(
	{
	"dialog": [datasets.Value("string")],
	"weibo_content": datasets.Value("string"),
	"imgs": [datasets.Value("string")],
	}
	)
	else:
	features = datasets.Features(
	{
	"dialog": [datasets.Value("string")],
	"weibo_content": datasets.Value("string"),
	"imgs": [datasets.Value("string")],
	"labels": {
	"image_qualified": datasets.Value("bool"),
	"dialog_qualified": datasets.Value("bool"),
	"dialog_image_related": 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[self.config.name]
	data_dir = dl_manager.download_and_extract(urls)
	if self.config.name == "mmchat":
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"dialog_file": data_dir["train"][0],
	"weibo_file": data_dir["train"][2],
	"img_file": data_dir["train"][1],
	"label_file": None,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"dialog_file": data_dir["test"][0],
	"weibo_file": data_dir["test"][2],
	"img_file": data_dir["test"][1],
	"label_file": None,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"dialog_file": data_dir["dev"][0],
	"weibo_file": data_dir["dev"][2],
	"img_file": data_dir["dev"][1],
	"label_file": None,
	},
	),
	]
	else:
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"dialog_file": data_dir[0],
	"weibo_file": data_dir[2],
	"img_file": data_dir[1],
	"label_file": data_dir[3] if len(data_dir) == 4 else None,
	},
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, dialog_file, weibo_file, img_file, label_file):
	id = 0
	if label_file is not None:
	label_f = open(label_file, encoding="utf-8")

	with open(dialog_file, encoding="utf-8") as dialog_f, open(weibo_file, encoding="utf-8") as weibo_f, open(
	img_file, encoding="utf-8"
	) as img_f:
	while True:
	try:
	dialog_line = dialog_f.readline().strip()
	if len(dialog_line) == 0:
	break
	dialog = json.loads(dialog_line) # dialog_f.readline())
	weibo = json.loads(weibo_f.readline())
	if self.config.name == "mmchat":
	imgs = img_f.readline().strip().split(";")
	else:
	imgs = json.loads(img_f.readline())["weibo_img"].split(";")

	if self.config.name == "mmchat_hf":
	label = json.loads(label_f.readline())
	# Yields examples as (key, example) tuples
	yield id, {
	"dialog": dialog,
	"weibo_content": weibo,
	"imgs": imgs,
	"labels": {
	"image_qualified": True if label["image_quality"] == "1" else False,
	"dialog_qualified": True if label["dialog_quality"] == "1" else False,
	"dialog_image_related": True if label["dialog_image_relativeness"] == "1" else False,
	},
	}
	else:
	yield id, {
	"dialog": dialog,
	"weibo_content": weibo,
	"imgs": imgs,
	}
	id += 1
	except EOFError:
	break
	if label_file is not None:
	label_f.close()