Update files from the datasets library (from 1.2.0)

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

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README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- en
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- question-answering-other-multihop-tabular-text-qa
+---
+
+# Dataset Card Creation Guide
+
+## 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:** -
+- **Repository:** [GitHub](https://github.com/wenhuchen/HybridQA)
+- **Paper:** [HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data](https://arxiv.org/abs/1909.05358)
+- **Leaderboard:** [HybridQA Competition](https://competitions.codalab.org/competitions/24420)
+- **Point of Contact:** [Wenhu Chen](wenhuchen@cs.ucsb.edu)
+
+
+### Dataset Summary
+
+Existing question answering datasets focus on dealing with homogeneous information, based either only on text or
+KB/Table information alone. However, as human knowledge is distributed over heterogeneous forms,
+using homogeneous information alone might lead to severe coverage problems.
+To fill in the gap, we present HybridQA, a new large-scale question-answering dataset that
+requires reasoning on heterogeneous information. Each question is aligned with a Wikipedia table
+and multiple free-form corpora linked with the entities in the table. The questions are designed
+to aggregate both tabular information and text information, i.e.,
+lack of either form would render the question unanswerable.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The dataset is in English language.
+
+## Dataset Structure
+
+### Data Instances
+
+A typical example looks like this
+
+```
+{
+    "question_id": "00009b9649d0dd0a",
+    "question": "Who were the builders of the mosque in Herat with fire temples ?",
+    "table_id": "List_of_mosques_in_Afghanistan_0",
+    "answer_text": "Ghurids",
+    "question_postag": "WP VBD DT NNS IN DT NN IN NNP IN NN NNS .",
+    "table": {
+        "url": "https://en.wikipedia.org/wiki/List_of_mosques_in_Afghanistan",
+        "title": "List of mosques in Afghanistan",
+        "header": [
+            "Name",
+            "Province",
+            "City",
+            "Year",
+            "Remarks"
+        ],
+        "data": [
+            {
+                "value": "Kabul",
+                "urls": [
+                    {
+                        "summary": "Kabul ( Persian : کابل , romanized : Kābol , Pashto : کابل , romanized : Kābəl ) is the capital and largest city of Afghanistan...",
+                        "url": "/wiki/Kabul"
+                    }
+                ]
+            }
+        ]
+    },
+    "section_title": "",
+    "section_text": "",
+    "uid": "List_of_mosques_in_Afghanistan_0",
+    "intro": "The following is an incomplete list of large mosques in Afghanistan:"
+}
+```
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+The dataset is split into `train`, `dev` and `test` splits.
+
+|                 | Tain   | Valid | Test  |
+| --------------- | ------ | ----- | ----- |
+| N. Instances    | 62682  | 3466  | 3463  |
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### 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
+
+[More Information Needed]
+```
+@article{chen2020hybridqa,
+  title={HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data},
+  author={Chen, Wenhu and Zha, Hanwen and Chen, Zhiyu and Xiong, Wenhan and Wang, Hong and Wang, William},
+  journal={Findings of EMNLP 2020},
+  year={2020}
+}
+```

dataset_infos.json

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+{"hybrid_qa": {"description": "Existing question answering datasets focus on dealing with homogeneous information, based either only on text or KB/Table information alone. However, as human knowledge is distributed over heterogeneous forms, using homogeneous information alone might lead to severe coverage problems. To fill in the gap, we present HybridQA, a new large-scale question-answering dataset that requires reasoning on heterogeneous information. Each question is aligned with a Wikipedia table and multiple free-form corpora linked with the entities in the table. The questions are designed to aggregate both tabular information and text information, i.e., lack of either form would render the question unanswerable.\n", "citation": "@article{chen2020hybridqa,\n  title={HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data},\n  author={Chen, Wenhu and Zha, Hanwen and Chen, Zhiyu and Xiong, Wenhan and Wang, Hong and Wang, William},\n  journal={Findings of EMNLP 2020},\n  year={2020}\n}\n", "homepage": "https://github.com/wenhuchen/HybridQA", "license": "", "features": {"question_id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "table_id": {"dtype": "string", "id": null, "_type": "Value"}, "answer_text": {"dtype": "string", "id": null, "_type": "Value"}, "question_postag": {"dtype": "string", "id": null, "_type": "Value"}, "table": {"url": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "header": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "data": [{"value": {"dtype": "string", "id": null, "_type": "Value"}, "urls": [{"url": {"dtype": "string", "id": null, "_type": "Value"}, "summary": {"dtype": "string", "id": null, "_type": "Value"}}]}], "section_title": {"dtype": "string", "id": null, "_type": "Value"}, "section_text": {"dtype": "string", "id": null, "_type": "Value"}, "uid": {"dtype": "string", "id": null, "_type": "Value"}, "intro": {"dtype": "string", "id": null, "_type": "Value"}}}, "post_processed": null, "supervised_keys": null, "builder_name": "hybrid_qa", "config_name": "hybrid_qa", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2745712769, "num_examples": 62682, "dataset_name": "hybrid_qa"}, "validation": {"name": "validation", "num_bytes": 153512016, "num_examples": 3466, "dataset_name": "hybrid_qa"}, "test": {"name": "test", "num_bytes": 148795919, "num_examples": 3463, "dataset_name": "hybrid_qa"}}, "download_checksums": {"https://github.com/wenhuchen/WikiTables-WithLinks/archive/f4ed68e54e25c495f63d309de0b89c0f97b3c508.zip": {"num_bytes": 193533209, "checksum": "8f8f708f485e38a6114cf7d246e6ac00eb6f7705a9e5b740ab2ef499b864da43"}, "https://raw.githubusercontent.com/wenhuchen/HybridQA/master/released_data/train.json": {"num_bytes": 21638585, "checksum": "b33aa73638959a2383e1e1638fd6abe87818b7379c7a42eac1621475d2d959e2"}, "https://raw.githubusercontent.com/wenhuchen/HybridQA/master/released_data/dev.json": {"num_bytes": 1193503, "checksum": "424272b233735a70ed8ef5af4a615373d114f472168c686c4370d54c92d58ac1"}, "https://raw.githubusercontent.com/wenhuchen/HybridQA/master/released_data/test.json": {"num_bytes": 1071558, "checksum": "41845fec9cba21979e663a96626c2880adbf2d26b5667ea7d0bf61fab0cdc356"}}, "download_size": 217436855, "post_processing_size": null, "dataset_size": 3048020704, "size_in_bytes": 3265457559}}

dummy/hybrid_qa/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:93e1ee4d803dbbf2c9302915d50b8fe1b691f7b4fce3727bcc1c270342abfcc8
+size 44090

hybrid_qa.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors.
+#
+# 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.
+"""HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data"""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@article{chen2020hybridqa,
+  title={HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data},
+  author={Chen, Wenhu and Zha, Hanwen and Chen, Zhiyu and Xiong, Wenhan and Wang, Hong and Wang, William},
+  journal={Findings of EMNLP 2020},
+  year={2020}
+}
+"""
+
+_DESCRIPTION = """\
+Existing question answering datasets focus on dealing with homogeneous information, based either only on text or \
+KB/Table information alone. However, as human knowledge is distributed over heterogeneous forms, \
+using homogeneous information alone might lead to severe coverage problems. \
+To fill in the gap, we present HybridQA, a new large-scale question-answering dataset that \
+requires reasoning on heterogeneous information. Each question is aligned with a Wikipedia table \
+and multiple free-form corpora linked with the entities in the table. The questions are designed \
+to aggregate both tabular information and text information, i.e., \
+lack of either form would render the question unanswerable.
+"""
+
+_HOMEPAGE = "https://github.com/wenhuchen/HybridQA"
+
+_WIKI_TABLES_GIT_ARCHIVE_URL = (
+    "https://github.com/wenhuchen/WikiTables-WithLinks/archive/f4ed68e54e25c495f63d309de0b89c0f97b3c508.zip"
+)
+
+_QA_DATA_BASE_URL = "https://raw.githubusercontent.com/wenhuchen/HybridQA/master/released_data"
+_URLS = {
+    "train": f"{_QA_DATA_BASE_URL}/train.json",
+    "dev": f"{_QA_DATA_BASE_URL}/dev.json",
+    "test": f"{_QA_DATA_BASE_URL}/test.json",
+}
+
+
+class HybridQa(datasets.GeneratorBasedBuilder):
+    """HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data"""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="hybrid_qa",
+            version=datasets.Version("1.0.0"),
+        ),
+    ]
+
+    def _info(self):
+        features = {
+            "question_id": datasets.Value("string"),
+            "question": datasets.Value("string"),
+            "table_id": datasets.Value("string"),
+            "answer_text": datasets.Value("string"),
+            "question_postag": datasets.Value("string"),
+            "table": {
+                "url": datasets.Value("string"),
+                "title": datasets.Value("string"),
+                "header": datasets.Sequence(datasets.Value("string")),
+                "data": [
+                    {
+                        "value": datasets.Value("string"),
+                        "urls": [{"url": datasets.Value("string"), "summary": datasets.Value("string")}],
+                    }
+                ],
+                "section_title": datasets.Value("string"),
+                "section_text": datasets.Value("string"),
+                "uid": datasets.Value("string"),
+                "intro": datasets.Value("string"),
+            },
+        }
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(features),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        extracted_path = dl_manager.download_and_extract(_WIKI_TABLES_GIT_ARCHIVE_URL)
+        downloaded_files = dl_manager.download(_URLS)
+
+        repo_path = os.path.join(extracted_path, "WikiTables-WithLinks-f4ed68e54e25c495f63d309de0b89c0f97b3c508")
+        tables_path = os.path.join(repo_path, "tables_tok")
+        requests_path = os.path.join(repo_path, "request_tok")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "qa_filepath": downloaded_files["train"],
+                    "tables_path": tables_path,
+                    "requests_path": requests_path,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "qa_filepath": downloaded_files["dev"],
+                    "tables_path": tables_path,
+                    "requests_path": requests_path,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "qa_filepath": downloaded_files["test"],
+                    "tables_path": tables_path,
+                    "requests_path": requests_path,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, qa_filepath, tables_path, requests_path):
+        with open(qa_filepath, encoding="utf-8") as f:
+            examples = json.load(f)
+
+        for example in examples:
+            table_id = example["table_id"]
+            table_file_path = os.path.join(tables_path, f"{table_id}.json")
+            url_data_path = os.path.join(requests_path, f"{table_id}.json")
+
+            with open(table_file_path, encoding="utf-8") as f:
+                table = json.load(f)
+            with open(url_data_path, encoding="utf-8") as f:
+                url_data = json.load(f)
+
+            table["header"] = [header[0] for header in table["header"]]
+
+            # here each row is a list with two elemets, the row value and list of urls for that row
+            # convert it to list of dict with keys value and urls
+            rows = []
+            for row in table["data"]:
+                for col in row:
+                    new_row = {"value": col[0]}
+                    urls = col[1]
+                    new_row["urls"] = [{"url": url, "summary": url_data[url]} for url in urls]
+                    rows.append(new_row)
+            table["data"] = rows
+
+            example["answer_text"] = example.pop("answer-text") if "answer-text" in example else ""
+            example["table"] = table
+
+            yield example["question_id"], example