# 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()