Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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README.md

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+---
+annotations_creators:
+- found
+language_creators:
+- found
+languages:
+- zh
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
+---
+
+# Dataset Card for LiveQA
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [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)
+
+## Dataset Description
+
+- **Homepage:** [Github](https://github.com/PKU-TANGENT/LiveQA)
+- **Repository:** [Github](https://github.com/PKU-TANGENT/LiveQA)
+- **Paper:** [Liu et al., 2020](https://www.aclweb.org/anthology/2020.ccl-1.98.pdf)
+- **Leaderboard:** N/A
+- **Point of Contact:** Qianying Liu
+
+### Dataset Summary
+The LiveQA dataset is a Chinese question-answering resource constructed from playby-play live broadcasts. It contains 117k multiple-choice questions written by human commentators for over 1,670 NBA games, which are collected from the Chinese Hupu website.
+
+### Supported Tasks and Leaderboards
+Question Answering. 
+
+[More Information Needed]
+
+### Languages
+Chinese. 
+
+## Dataset Structure
+
+### Data Instances
+Each instance represents a timeline (i.e., a game) with an identifier. The passages field comprise an array of text or question segments. In the following truncated example, user comments about the game is followed by a question about which team will be the first to reach 60 points. 
+```python
+{
+  
+    'id': 1,
+    'passages': [
+      {
+        "is_question": False,
+        "text": "'我希望两位球员都能做到！！",
+        "candidate1": "",
+        "candidate2": "",
+        "answer": "",
+      },
+      {
+        "is_question": False,
+        "text": "新年给我们送上精彩比赛！",
+        "candidate1": "",
+        "candidate2": "",
+        "answer": "",
+      },
+      {
+        "is_question": True,
+        "text": "先达到60分？",
+        "candidate1": "火箭",
+        "candidate2": "勇士",
+        "answer": "勇士",
+      },
+      {
+        "is_question": False,
+        "text": "自己急停跳投！！！",
+        "candidate1": "",
+        "candidate2": "",
+        "answer": "",
+      }
+    ]
+}
+```
+
+### Data Fields
+- id: identifier for the game
+- passages: collection of text/question segments
+- text: real-time text comment or binary question related to the context
+- candidate1/2: one of the two answer options to the question
+- answer: correct answer to the question in text
+
+### Data Splits
+There is no predefined split in this dataset. 
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+This resource is developed by [Liu et al., 2020](https://www.aclweb.org/anthology/2020.ccl-1.98.pdf).
+```
+@inproceedings{qianying-etal-2020-liveqa,
+    title = "{L}ive{QA}: A Question Answering Dataset over Sports Live",
+    author = "Qianying, Liu  and
+      Sicong, Jiang  and
+      Yizhong, Wang  and
+      Sujian, Li",
+    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
+    month = oct,
+    year = "2020",
+    address = "Haikou, China",
+    publisher = "Chinese Information Processing Society of China",
+    url = "https://www.aclweb.org/anthology/2020.ccl-1.98",
+    pages = "1057--1067"
+}
+```

dataset_infos.json

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+{"default": {"description": "This is LiveQA, a Chinese dataset constructed from play-by-play live broadcast.\nIt contains 117k multiple-choice questions written by human commentators for over 1,670 NBA games,\nwhich are collected from the Chinese Hupu website.\n", "citation": "@inproceedings{qianying-etal-2020-liveqa,\n    title = \"{L}ive{QA}: A Question Answering Dataset over Sports Live\",\n    author = \"Qianying, Liu  and\n      Sicong, Jiang  and\n      Yizhong, Wang  and\n      Sujian, Li\",\n    booktitle = \"Proceedings of the 19th Chinese National Conference on Computational Linguistics\",\n    month = oct,\n    year = \"2020\",\n    address = \"Haikou, China\",\n    publisher = \"Chinese Information Processing Society of China\",\n    url = \"https://www.aclweb.org/anthology/2020.ccl-1.98\",\n    pages = \"1057--1067\"\n}\n", "homepage": "https://github.com/PKU-TANGENT/LiveQA", "license": "", "features": {"id": {"dtype": "int64", "id": null, "_type": "Value"}, "passages": {"feature": {"is_question": {"dtype": "bool", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "candidate1": {"dtype": "string", "id": null, "_type": "Value"}, "candidate2": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "live_qa", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 112187507, "num_examples": 1670, "dataset_name": "live_qa"}}, "download_checksums": {"https://raw.githubusercontent.com/PKU-TANGENT/LiveQA/master/LiveQA-1.json": {"num_bytes": 22754058, "checksum": "ab4861f63bbfc9b84bd9fee9d0a682cd77d9ac48aa57d3cbc25bd9e1433e53fd"}, "https://raw.githubusercontent.com/PKU-TANGENT/LiveQA/master/LiveQA-2.json": {"num_bytes": 22879003, "checksum": "e1094303ff34bf0bc105f9d336e0d6824625955bab02e52cae5115c51af67eb5"}, "https://raw.githubusercontent.com/PKU-TANGENT/LiveQA/master/LiveQA-3.json": {"num_bytes": 22771837, "checksum": "fcb46b890c6622508fa35ad62e96e8f5e072d5c4962ebbea95f9a1a12f7a641b"}, "https://raw.githubusercontent.com/PKU-TANGENT/LiveQA/master/LiveQA-4.json": {"num_bytes": 22966788, "checksum": "1d8a1ba7ba503b6c6f94fb301022e7cfe7d719dc03f89887e447617bc0f6bac3"}, "https://raw.githubusercontent.com/PKU-TANGENT/LiveQA/master/LiveQA-5.json": {"num_bytes": 23332883, "checksum": "85d8f8fe802aa5e80bc5e58edd6a990912a7b128f8279684fda4e5d39cd07bb9"}}, "download_size": 114704569, "post_processing_size": null, "dataset_size": 112187507, "size_in_bytes": 226892076}}

dummy/1.0.0/dummy_data.zip

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+oid sha256:1bc66ac3ede6d276010f4b7a5c88aa4d8f3ee70e5887ef6748737bb572626a3a
+size 2391

liveqa.py

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+# coding=utf-8
+# 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.
+"""LiveQA dataset."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{qianying-etal-2020-liveqa,
+    title = "{L}ive{QA}: A Question Answering Dataset over Sports Live",
+    author = "Qianying, Liu  and
+      Sicong, Jiang  and
+      Yizhong, Wang  and
+      Sujian, Li",
+    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
+    month = oct,
+    year = "2020",
+    address = "Haikou, China",
+    publisher = "Chinese Information Processing Society of China",
+    url = "https://www.aclweb.org/anthology/2020.ccl-1.98",
+    pages = "1057--1067"
+}
+"""
+
+_DESCRIPTION = """\
+This is LiveQA, a Chinese dataset constructed from play-by-play live broadcast.
+It contains 117k multiple-choice questions written by human commentators for over 1,670 NBA games,
+which are collected from the Chinese Hupu website.
+"""
+
+_HOMEPAGE = "https://github.com/PKU-TANGENT/LiveQA"
+
+_REPO = "https://raw.githubusercontent.com/PKU-TANGENT/LiveQA/master/"
+_URLs = [f"{_REPO}LiveQA-{i}.json" for i in range(1, 6)]
+
+
+class LiveQA(datasets.GeneratorBasedBuilder):
+    """LiveQA dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "id": datasets.Value("int64"),
+                "passages": datasets.Sequence(
+                    {
+                        "is_question": datasets.Value("bool"),
+                        "text": datasets.Value("string"),
+                        "candidate1": datasets.Value("string"),
+                        "candidate2": datasets.Value("string"),
+                        "answer": datasets.Value("string"),
+                    }
+                ),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # No default split.
+        # Data is separated into 5 files due to size restrictions,
+        # but they must be concatenated to create a well-formed json.
+
+        data_dir = dl_manager.download_and_extract(_URLs)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepaths": data_dir, "split": "train"},
+            )
+        ]
+
+    def _generate_examples(self, filepaths, split):
+        """ Yields examples. """
+
+        data_raw = ""
+        for filepath in filepaths:
+            with open(filepath, "r", encoding="utf-8") as f:
+                data_raw += f.read()
+
+        data = json.loads(data_raw)
+        games = data["passages"]
+
+        game_id = -1  # "id" field is always 1 in the original dataset regardless of game
+        for game in games:
+            game_id += 1
+            passages = []
+            for passage in game["passage"]:
+                is_question = "question" in passage
+                text = passage["question"] if is_question else passage["text"]
+                candidate_1 = passage["candidate1"] if is_question else ""
+                candidate_2 = passage["candidate2"] if is_question else ""
+                answer = passage["answer"] if is_question else ""
+
+                passages.append(
+                    {
+                        "is_question": is_question,
+                        "text": text,
+                        "candidate1": candidate_1,
+                        "candidate2": candidate_2,
+                        "answer": answer,
+                    }
+                )
+
+            yield game_id, {"id": game_id, "passages": passages}