Update parquet files

.gitattributes

@@ -17,3 +17,11 @@ mmchat_raw/weibo_raw.jsonl.gz filter=lfs diff=lfs merge=lfs -text
 mmchat_lccc_filtered/dialog_lccc_flt.jsonl.gz filter=lfs diff=lfs merge=lfs -text
 mmchat_lccc_filtered/weibo_img_expanded_url_lccc_flt.jsonl.gz filter=lfs diff=lfs merge=lfs -text
 mmchat_lccc_filtered/weibo_lccc_flt.jsonl.gz filter=lfs diff=lfs merge=lfs -text
+mmchat/mmchat-train.parquet filter=lfs diff=lfs merge=lfs -text
+mmchat_hf/mmchat-train.parquet filter=lfs diff=lfs merge=lfs -text
+mmchat_lccc_filtered/mmchat-train.parquet filter=lfs diff=lfs merge=lfs -text
+mmchat_raw/mmchat-train-00001-of-00005.parquet filter=lfs diff=lfs merge=lfs -text
+mmchat_raw/mmchat-train-00003-of-00005.parquet filter=lfs diff=lfs merge=lfs -text
+mmchat_raw/mmchat-train-00004-of-00005.parquet filter=lfs diff=lfs merge=lfs -text
+mmchat_raw/mmchat-train-00002-of-00005.parquet filter=lfs diff=lfs merge=lfs -text
+mmchat_raw/mmchat-train-00000-of-00005.parquet filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,258 +0,0 @@
----
-annotations_creators:
-- no-annotation
-language_creators:
-- found
-language:
-- zh
-license:
-- other
-multilinguality:
-- monolingual
-paperswithcode_id: mmchat-multi-modal-chat-dataset-on-social
-pretty_name: "MMChat: Multi-Modal Chat Dataset on Social Media"
-size_categories:
-- 10M<n<100M
-source_datasets:
-- original
-task_categories:
-- conversational
-task_ids:
-- dialogue-generation
----
-
-# Dataset Card for MMChat
-
-## Table of Contents
-- [Dataset Card for MMChat](#dataset-card-for-mmchat)
-  - [Table of Contents](#table-of-contents)
-  - [Dataset Description](#dataset-description)
-    - [Dataset Summary](#dataset-summary)
-    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-    - [Languages](#languages)
-  - [Dataset Structure](#dataset-structure)
-    - [Data Instances](#data-instances)
-    - [Data Fields](#data-fields)
-    - [Data Splits](#data-splits)
-  - [Dataset Creation](#dataset-creation)
-    - [Curation Rationale](#curation-rationale)
-    - [Source Data](#source-data)
-      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
-      - [Who are the source language producers?](#who-are-the-source-language-producers)
-    - [Annotations](#annotations)
-      - [Annotation process](#annotation-process)
-      - [Who are the annotators?](#who-are-the-annotators)
-    - [Personal and Sensitive Information](#personal-and-sensitive-information)
-  - [Considerations for Using the Data](#considerations-for-using-the-data)
-    - [Social Impact of Dataset](#social-impact-of-dataset)
-    - [Discussion of Biases](#discussion-of-biases)
-    - [Other Known Limitations](#other-known-limitations)
-  - [Additional Information](#additional-information)
-    - [Dataset Curators](#dataset-curators)
-    - [Licensing Information](#licensing-information)
-    - [Citation Information](#citation-information)
-    - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** https://www.zhengyinhe.com/datasets/
-- **Repository:** https://github.com/silverriver/MMChat
-- **Paper:** https://arxiv.org/abs/2108.07154
-
-### Dataset Summary
-
-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.
-
-MMChat comes with 4 different versions: 
-
-- `mmchat`: The MMChat dataset used in our paper.
-- `mmchat_hf`: Contains human annotation on 100K sessions of dialogues. 
-- `mmchat_raw`: Raw dialogues used to construct MMChat.
-  `mmchat_lccc_filtered`: Raw dialogues filtered using the LCCC dataset.
-
-If you what to use high quality multi-modal dialogues that are closed related to the given images, I suggest you to use the `mmchat_hf` version.
-If you only care about the quality of dialogue texts, I suggest you to use the `mmchat_lccc_filtered` version.
-
-### Supported Tasks and Leaderboards
-
-- dialogue-generation: The dataset can be used to train a model for generating dialogue responses.
-- response-retrieval: The dataset can be used to train a reranker model that can be used to implement a retrieval-based dialogue model.
-
-### Languages
-
-MMChat is in Chinese
-
-MMChat中的对话是中文的
-
-## Dataset Structure
-
-### Data Instances
-
-Several versions of MMChat are available. For `mmchat`, `mmchat_raw`, `mmchat_lccc_filtered`, the following instance applies:
-
-```json
-{
-  "dialog": ["你只拍出了你十分之一的美", "你的头像竟然换了，奥"],
-  "weibo_content": "分享图片",
-  "imgs": ["https://wx4.sinaimg.cn/mw2048/d716a6e2ly1fmug2w2l9qj21o02yox6p.jpg"]
-}
-```
-
-For `mmchat_hf`, the following instance applies:
-
-```json
-{
-  "dialog": ["白百合", "啊？", "有点像", "还好吧哈哈哈牙像", "有男盆友没呢", "还没", "和你说话呢。没回我"],
-  "weibo_content": "补一张昨天礼仪的照片",
-  "imgs": ["https://ww2.sinaimg.cn/mw2048/005Co9wdjw1eyoz7ib9n5j307w0bu3z5.jpg"],
-  "labels": {
-    "image_qualified": true, 
-    "dialog_qualified": true, 
-    "dialog_image_related": true
-  }
-}
-```
-
-### Data Fields
-
-- `dialog` (list of strings): List of utterances consisting of a dialogue.
-- `weibo_content` (string): Weibo content of the dialogue.
-- `imgs` (list of strings): List of URLs of images.
-- `labels` (dict): Human-annotated labels of the dialogue.
-- `image_qualified` (bool): Whether the image is of high quality.
-- `dialog_qualified` (bool): Whether the dialogue is of high quality.
-- `dialog_image_related` (bool): Whether the dialogue is related to the image.
-
-### Data Splits
-
-For `mmchat`, we provide the following splits:
-
-|train|valid|test|
-|---:|---:|---:|
-|115,842 | 4,000 | 1,000 |
-
-For other versions, we do not provide the offical split.
-More stastics are listed here:
-
-| `mmchat`                   | Count   |
-|--------------------------------------|--------:|
-| Sessions                             | 120.84 K |
-| Sessions with more than 4 utterances |  17.32 K |
-| Utterances                           | 314.13 K |
-| Images                               |  198.82 K |
-| Avg. utterance per session           |  2.599 |
-| Avg. image per session               |  2.791 |
-| Avg. character per utterance         |  8.521 |
-
-| `mmchat_hf`                     | Count   |
-|--------------------------------------|--------:|
-| Sessions                             | 19.90 K |
-| Sessions with more than 4 utterances | 8.91 K |
-| Totally annotated sessions           | 100.01 K |
-| Utterances                           | 81.06 K |
-| Images                               | 52.66K |
-| Avg. utterance per session           | 4.07 |
-| Avg. image per session               | 2.70 |
-| Avg. character per utterance         | 11.93 |
-
-| `mmchat_raw`                     | Count    |
-|--------------------------------------|---------:|
-| Sessions                             | 4.257 M  |
-| Sessions with more than 4 utterances | 2.304 M  |
-| Utterances                           | 18.590 M |
-| Images                               | 4.874 M  |
-| Avg. utterance per session           | 4.367    |
-| Avg. image per session               | 1.670    |
-| Avg. character per utterance         | 14.104   |
-
-| `mmchat_lccc_filtered`                     | Count   |
-|--------------------------------------|--------:|
-| Sessions                             | 492.6 K |
-| Sessions with more than 4 utterances | 208.8 K |
-| Utterances                           | 1.986 M |
-| Images                               | 1.066 M |
-| Avg. utterance per session           | 4.031   |
-| Avg. image per session               | 2.514   |
-| Avg. character per utterance         | 11.336  |
-
-## Dataset Creation
-
-### Curation Rationale
-
-[Needs More Information]
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[Needs More Information]
-
-#### Who are the source language producers?
-
-[Needs More Information]
-
-### Annotations
-
-#### Annotation process
-
-[Needs More Information]
-
-#### Who are the annotators?
-
-[Needs More Information]
-
-### Personal and Sensitive Information
-
-[Needs More Information]
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[Needs More Information]
-
-### Discussion of Biases
-
-[Needs More Information]
-
-### Other Known Limitations
-
-[Needs More Information]
-
-## Additional Information
-
-### Dataset Curators
-
-[Needs More Information]
-
-### Licensing Information
-
-other-weibo
-
-This dataset is collected from Weibo.
-You can refer to the [detailed policy](https://weibo.com/signup/v5/privacy) required to use this dataset.
-Please restrict the usage of this dataset to non-commerical purposes.
-
-### Citation Information
-
-```
-@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}
-}
-```
-
-### Contributions
-
-Thanks to [Yinhe Zheng](https://github.com/silverriver) for adding this dataset.

dataset_infos.json

@@ -1 +0,0 @@
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mmchat.py

@@ -1,231 +0,0 @@
-# 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()

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