update dataset script

README.md

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+---
+annotations_creators:
+- other
+language_creators:
+- other
+languages:
+- zh
+licenses:
+- mit
+multilinguality:
+- monolingual
+paperswithcode_id: personaldialog
+pretty_name: "PersonalDialog"
+size_categories:
+- 10M<n<100M
+source_datasets:
+- original
+task_categories:
+- conversational
+task_ids:
+- dialogue-generation
+---
+
+# Dataset Card for PersonalDialog
+
+## Table of Contents
+- [Dataset Card for PersonalDialog](#dataset-card-for-personaldialog)
+  - [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/PersonalDilaog
+- **Paper:** https://arxiv.org/abs/1901.09672
+
+### Dataset Summary
+
+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.
+
+### 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
+
+PersonalDialog is in Chinese
+
+PersonalDialog中的对话是中文的
+
+## Dataset Structure
+
+### Data Instances
+
+`train` split:
+
+```json
+{
+  "dialog": ["那么 晚", "加班 了 刚 到 家 呀 ！", "吃饭 了 么", "吃 过 了 ！"], 
+  "profile": [
+    { 
+      "tag": ["间歇性神经病", "爱笑的疯子", "他们说我犀利", "爱做梦", "自由", "旅游", "学生", "双子座", "好性格"], 
+      "loc": "福建 厦门", "gender": "male"
+    }, {
+      "tag": ["设计师", "健康养生", "热爱生活", "善良", "宅", "音樂", "时尚"], 
+      "loc": "山东 济南", "gender": "male"
+      }
+  ], 
+  "uid": [0, 1, 0, 1],
+}
+```
+
+`dev` and `test` split:
+
+```json
+{
+  "dialog": ["没 人性 啊 ！", "可以 来 组织 啊", "来 上海 陪姐 打 ？"], 
+  "profile": [
+    {"tag": [""], "loc": "上海 浦东新区", "gender": "female"}, 
+    {"tag": ["嘉庚", "keele", "leicester", "UK", "泉州五中"], "loc": "福建 泉州", "gender": "male"},
+  ], 
+  "uid": [0, 1, 0],
+  "responder_profile": {"tag": ["嘉庚", "keele", "leicester", "UK", "泉州五中"], "loc": "福建 泉州", "gender": "male"}, 
+  "golden_response": "吴经理 派车来 小 泉州 接 么 ？", 
+  "is_biased": true,
+}
+```
+
+### Data Fields
+
+- `dialog` (list of strings): List of utterances consisting of a dialogue.
+- `profile` (list of dicts): List of profiles associated with each speaker.
+- `tag` (list of strings): List of tags associated with each speaker.
+- `loc` (string): Location of each speaker.
+- `gender` (string): Gender of each speaker.
+- `uid` (list of int): Speaker id for each utterance in the dialogue.
+- `responder_profile` (dict): Profile of the responder. (Only available in `dev` and `test` split)
+- `golden_response` (str): Response of the responder. (Only available in `dev` and `test` split)
+- `id_biased` (bool): Whether the dialogue is guranteed to be persona related or not. (Only available in `dev` and `test` split)
+
+### Data Splits
+
+
+|train|valid|test|
+|---:|---:|---:|
+|5,438,165 | 10,521 | 10,523 |
+
+
+## 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
+
+MIT License
+
+Copyright (c) 2019 silver
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+### Citation Information
+
+```bibtex
+@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}
+}
+```
+
+### Contributions
+
+Thanks to [Yinhe Zheng](https://github.com/silverriver) for adding this dataset.

dataset_infos.json

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+{"default": {"description": "The PersonalDialog dataset is a large-scale multi-turn Chinese dialogue dataset containing various traits from a large number of speakers. \nWe are releasing about 5M sessions of carefully filtered dialogues.\nEach utterance in PersonalDialog is associated with a speaker marked with traits like Gender, Location, Interest Tags. \n", "citation": "@article{zheng2019personalized,\n  title   = {Personalized dialogue generation with diversified traits},\n  author  = {Zheng, Yinhe and Chen, Guanyi and Huang, Minlie and Liu, Song and Zhu, Xuan},\n  journal = {arXiv preprint arXiv:1901.09672},\n  year    = {2019}\n}\n\n@inproceedings{zheng2020pre,\n  title     = {A pre-training based personalized dialogue generation model with persona-sparse data},\n  author    = {Zheng, Yinhe and Zhang, Rongsheng and Huang, Minlie and Mao, Xiaoxi},\n  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},\n  volume    = {34},\n  number    = {05},\n  pages     = {9693--9700},\n  year      = {2020}\n}\n", "homepage": "https://github.com/silverriver/PersonalDilaog", "license": "MIT", "features": {"dialog": [{"dtype": "string", "id": null, "_type": "Value"}], "profile": [{"tag": [{"dtype": "string", "id": null, "_type": "Value"}], "loc": {"dtype": "string", "id": null, "_type": "Value"}, "gender": {"dtype": "string", "id": null, "_type": "Value"}}], "uid": [{"dtype": "int32", "id": null, "_type": "Value"}], "responder_profile": {"tag": [{"dtype": "string", "id": null, "_type": "Value"}], "loc": {"dtype": "string", "id": null, "_type": "Value"}, "gender": {"dtype": "string", "id": null, "_type": "Value"}}, "golden_response": {"dtype": "string", "id": null, "_type": "Value"}, "is_biased": {"dtype": "bool", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "personal_dialog", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1659592284, "num_examples": 5438165, "dataset_name": "personal_dialog"}, "validation": {"name": "validation", "num_bytes": 5395032, "num_examples": 10521, "dataset_name": "personal_dialog"}, "test": {"name": "test", "num_bytes": 5412543, "num_examples": 10523, "dataset_name": "personal_dialog"}}, "download_checksums": {"https://huggingface.co/datasets/silver/personal_dialog/resolve/main/dialogues_train.jsonl.gz": {"num_bytes": 558585860, "checksum": "9af400265fda0e7adc9c11a04d343a1b6214a95f07b2911c61cb41f37740195e"}, "https://huggingface.co/datasets/silver/personal_dialog/resolve/main/dev_biased.jsonl.gz": {"num_bytes": 53463, "checksum": "f911aa17eaf8fabc4a093b77949779498d0008480c18320e559eb4df9b97e43f"}, "https://huggingface.co/datasets/silver/personal_dialog/resolve/main/dev_random.jsonl.gz": {"num_bytes": 1634800, "checksum": "0bcbb157125a522c68a0a73d7cfe0e518c5a12c2870bee470543a726cdf48d7f"}, "https://huggingface.co/datasets/silver/personal_dialog/resolve/main/test_biased.jsonl.gz": {"num_bytes": 52719, "checksum": "8da28b2aad57b226390410c48485b9c58faddde80aaa0f68a845f7d4797eac84"}, "https://huggingface.co/datasets/silver/personal_dialog/resolve/main/test_random.jsonl.gz": {"num_bytes": 1639580, "checksum": "b57746e599c888a025c81a870e49971f34d983de424861b6c07586f0c0eec330"}}, "download_size": 561966422, "post_processing_size": null, "dataset_size": 1670399859, "size_in_bytes": 2232366281}}

dummy/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:39b603fbd7ef383f422739b8c1260bb3e945c3a1083ea4c2afcfafceb5dbfe60
+size 5974

personal_dialog.py

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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 = {
+    "train": "https://huggingface.co/datasets/silver/personal_dialog/resolve/main/dialogues_train.jsonl.gz",
+    "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",
+    ],
+    "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