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:
+- found
+languages:
+- en
+licenses:
+- ms-pl
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
+---
+
+# Dataset Card for Microsoft Research Sequential Question Answering
+
+## Table of Contents
+
+- [Dataset Card for Microsoft Research Sequential Question Answering](#dataset-card-for-microsoft-research-sequential-question-answering)
+  - [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)
+
+## Dataset Description
+
+- **Homepage:[Microsoft Research Sequential Question Answering (SQA) Dataset](https://msropendata.com/datasets/b25190ed-0f59-47b1-9211-5962858142c2)**
+- **Repository:**
+- **Paper:[https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/acl17-dynsp.pdf](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/acl17-dynsp.pdf)**
+- **Leaderboard:**
+- **Point of Contact:**
+  - Scott Wen-tau Yih        [email protected]
+  - Mohit Iyyer              [email protected]
+  - Ming-Wei Chang           [email protected]
+
+### Dataset Summary
+
+Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions.
+
+We created SQA by asking crowdsourced workers to decompose 2,022 questions from WikiTableQuestions (WTQ)*, which contains highly-compositional questions about tables from Wikipedia. We had three workers decompose each WTQ question, resulting in a dataset of 6,066 sequences that contain 17,553 questions in total. Each question is also associated with answers in the form of cell locations in the tables.
+
+- Panupong Pasupat, Percy Liang. "Compositional Semantic Parsing on Semi-Structured Tables" ACL-2015.
+  [http://www-nlp.stanford.edu/software/sempre/wikitable/](http://www-nlp.stanford.edu/software/sempre/wikitable/)
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+- `id` (`str`): question sequence id (the id is consistent with those in WTQ)
+- `annotator` (`int`): `0`, `1`, `2` (the 3 annotators who annotated the question intent)
+- `position` (`int`): the position of the question in the sequence
+- `question` (`str`): the question given by the annotator
+- `table_file` (`str`): the associated table
+- `table_header` (`List[str]`): a list of headers in the table
+- `table_data` (`List[List[str]]`): 2d array of data in the table
+- `answer_coordinates` (`List[Dict]`): the table cell coordinates of the answers (0-based, where 0 is the first row after the table header)
+  - `row_index`
+  - `column_index`
+- `answer_text` (`List[str]`): the content of the answer cells
+
+Note that some text fields may contain Tab or LF characters and thus start with quotes.
+It is recommended to use a CSV parser like the Python CSV package to process the data.
+
+### Data Splits
+
+[More Information Needed]
+
+## 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
+
+[More Information Needed]

dataset_infos.json

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+{"default": {"description": "Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We created SQA by asking crowdsourced workers to decompose 2,022 questions from WikiTableQuestions (WTQ), which contains highly-compositional questions about tables from Wikipedia. We had three workers decompose each WTQ question, resulting in a dataset of 6,066 sequences that contain 17,553 questions in total. Each question is also associated with answers in the form of cell locations in the tables.\n", "citation": "@inproceedings{iyyer2017search,\n  title={Search-based neural structured learning for sequential question answering},\n  author={Iyyer, Mohit and Yih, Wen-tau and Chang, Ming-Wei},\n  booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},\n  pages={1821--1831},\n  year={2017}\n}\n", "homepage": "https://msropendata.com/datasets/b25190ed-0f59-47b1-9211-5962858142c2", "license": "Microsoft Research Data License Agreement", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "annotator": {"dtype": "int32", "id": null, "_type": "Value"}, "position": {"dtype": "int32", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "table_file": {"dtype": "string", "id": null, "_type": "Value"}, "table_header": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "table_data": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "answer_coordinates": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "answer_text": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "msr_sqa", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 22605449, "num_examples": 14541, "dataset_name": "msr_sqa"}, "test": {"name": "test", "num_bytes": 4924516, "num_examples": 3012, "dataset_name": "msr_sqa"}}, "download_checksums": {"https://download.microsoft.com/download/1/D/C/1DC270D2-1B53-4A61-A2E3-88AB3E4E6E1F/SQA%20Release%201.0.zip": {"num_bytes": 4796932, "checksum": "791a07ef90d6e736c186b25009d3c10cb38624b879bb668033445a3ab8892f64"}}, "download_size": 4796932, "post_processing_size": null, "dataset_size": 27529965, "size_in_bytes": 32326897}}

dummy/0.0.0/dummy_data.zip

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+oid sha256:7e46d5f939a1049a45c605ba21355084b0043e84b5dc6c7dec2717e0aa326510
+size 2732

msr_sqa.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors, The Google AI Language Team 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.
+"""Microsoft Research Sequential Question Answering (SQA) Dataset"""
+
+from __future__ import absolute_import, division, print_function
+
+import ast
+import csv
+import os
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@inproceedings{iyyer2017search,
+  title={Search-based neural structured learning for sequential question answering},
+  author={Iyyer, Mohit and Yih, Wen-tau and Chang, Ming-Wei},
+  booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
+  pages={1821--1831},
+  year={2017}
+}
+"""
+
+_DESCRIPTION = """\
+Recent work in semantic parsing for question answering has focused on long and complicated questions, \
+many of which would seem unnatural if asked in a normal conversation between two humans. \
+In an effort to explore a conversational QA setting, we present a more realistic task: \
+answering sequences of simple but inter-related questions. We created SQA by asking crowdsourced workers \
+to decompose 2,022 questions from WikiTableQuestions (WTQ), which contains highly-compositional questions about \
+tables from Wikipedia. We had three workers decompose each WTQ question, resulting in a dataset of 6,066 sequences \
+that contain 17,553 questions in total. Each question is also associated with answers in the form of cell \
+locations in the tables.
+"""
+
+_HOMEPAGE = "https://msropendata.com/datasets/b25190ed-0f59-47b1-9211-5962858142c2"
+
+_LICENSE = "Microsoft Research Data License Agreement"
+
+_URL = "https://download.microsoft.com/download/1/D/C/1DC270D2-1B53-4A61-A2E3-88AB3E4E6E1F/SQA%20Release%201.0.zip"
+
+
+def _load_table_data(table_file):
+    """Load additional data from a csv table file.
+
+    Args:
+        table_file: Path to the csv file.
+
+    Returns:
+        header: a list of headers in the table.
+        data: 2d array of data in the table.
+    """
+    with open(table_file, encoding="utf-8") as f:
+        lines = f.readlines()
+    header = lines[0].strip().split(",")
+    data = [line.strip().split(",") for line in lines[1:]]
+    return header, data
+
+
+def _parse_answer_coordinates(answer_coordinate_str):
+    """Parsing answer_coordinates field to a list of answer coordinates.
+    The original code is from https://github.com/google-research/tapas.
+
+    Args:
+      answer_coordinate_str: A string representation of a Python list of tuple
+        strings.
+        For example: "['(1, 4)','(1, 3)', ...]"
+
+    Returns:
+      answer_coordinates: A list of answer cordinates.
+    """
+    try:
+        answer_coordinates = []
+        coords = ast.literal_eval(answer_coordinate_str)
+        for row_index, column_index in sorted(ast.literal_eval(coord) for coord in coords):
+            answer_coordinates.append({"row_index": row_index, "column_index": column_index})
+        return answer_coordinates
+    except SyntaxError:
+        raise ValueError("Unable to evaluate %s" % answer_coordinate_str)
+
+
+def _parse_answer_text(answer_text_str):
+    """Parsing `answer_text` field to list of answers.
+    The original code is from https://github.com/google-research/tapas.
+    Args:
+      answer_text_str: A string representation of a Python list of strings.
+        For example: "[u'test', u'hello', ...]"
+
+    Returns:
+      answer_texts: A list of answers.
+    """
+    try:
+        answer_texts = []
+        for value in ast.literal_eval(answer_text_str):
+            answer_texts.append(value)
+        return answer_texts
+    except SyntaxError:
+        raise ValueError("Unable to evaluate %s" % answer_text_str)
+
+
+class MsrSQA(datasets.GeneratorBasedBuilder):
+    """Microsoft Research Sequential Question Answering (SQA) Dataset"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "annotator": datasets.Value("int32"),
+                    "position": datasets.Value("int32"),
+                    "question": datasets.Value("string"),
+                    "table_file": datasets.Value("string"),
+                    "table_header": datasets.features.Sequence(datasets.Value("string")),
+                    "table_data": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
+                    "answer_coordinates": datasets.features.Sequence(
+                        {"row_index": datasets.Value("int32"), "column_index": datasets.Value("int32")}
+                    ),
+                    "answer_text": datasets.features.Sequence(datasets.Value("string")),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = os.path.join(dl_manager.download_and_extract(_URL), "SQA Release 1.0")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": os.path.join(data_dir, "train.tsv"), "data_dir": data_dir},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": os.path.join(data_dir, "test.tsv"), "data_dir": data_dir},
+            ),
+        ]
+
+    def _generate_examples(self, filepath, data_dir):
+        """ Yields examples. """
+
+        with open(filepath, encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter="\t")
+            for row in reader:
+                item = dict(row)
+                item["answer_text"] = _parse_answer_text(item["answer_text"])
+                item["answer_coordinates"] = _parse_answer_coordinates(item["answer_coordinates"])
+                header, table_data = _load_table_data(os.path.join(data_dir, item["table_file"]))
+                item["table_header"] = header
+                item["table_data"] = table_data
+                yield item["id"], item