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:
+- crowdsourced
+language_creators:
+- crowdsourced
+- expert-generated
+languages:
+- en
+licenses:
+- cc-by-sa-3-0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
+- question-answering-other-conversational-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:** [ShARC](https://sharc-data.github.io/index.html)
+- **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]()
+- **Paper:** [Interpretation of Natural Language Rules in Conversational Machine Reading](https://arxiv.org/abs/1809.01494)
+- **Leaderboard:** [leaderboard](https://sharc-data.github.io/leaderboard.html)
+- **Point of Contact:** [Marzieh Saeidi](marzieh.saeidi@gmail.com), [Max Bartolo](maxbartolo@gmail.com), [Patrick Lewis](patrick.s.h.lewis@gmail.com), [Sebastian Riedel](s.riedel@cs.ucl.ac.uk)
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+## 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]

dataset_infos.json

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+{"sharc": {"description": "ShARC is a Conversational Question Answering dataset focussing on question answering from texts containing rules. The goal is to answer questions by possibly asking follow-up questions first. It is assumed assume that the question is often underspecified, in the sense that the question does not provide enough information to be answered directly. However, an agent can use the supporting rule text to infer what needs to be asked in order to determine the final answer.\n", "citation": "@misc{saeidi2018interpretation,\n      title={Interpretation of Natural Language Rules in Conversational Machine Reading},\n      author={Marzieh Saeidi and Max Bartolo and Patrick Lewis and Sameer Singh and Tim Rockt\u00e4schel and Mike Sheldon and Guillaume Bouchard and Sebastian Riedel},\n      year={2018},\n      eprint={1809.01494},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n", "homepage": "https://sharc-data.github.io/index.html", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "utterance_id": {"dtype": "string", "id": null, "_type": "Value"}, "source_url": {"dtype": "string", "id": null, "_type": "Value"}, "snippet": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "scenario": {"dtype": "string", "id": null, "_type": "Value"}, "history": [{"follow_up_question": {"dtype": "string", "id": null, "_type": "Value"}, "follow_up_answer": {"dtype": "string", "id": null, "_type": "Value"}}], "evidence": [{"follow_up_question": {"dtype": "string", "id": null, "_type": "Value"}, "follow_up_answer": {"dtype": "string", "id": null, "_type": "Value"}}], "answer": {"dtype": "string", "id": null, "_type": "Value"}, "negative_question": {"dtype": "bool_", "id": null, "_type": "Value"}, "negative_scenario": {"dtype": "bool_", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "sharc", "config_name": "sharc", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 15088577, "num_examples": 21890, "dataset_name": "sharc"}, "validation": {"name": "validation", "num_bytes": 1469172, "num_examples": 2270, "dataset_name": "sharc"}}, "download_checksums": {"https://sharc-data.github.io/data/sharc1-official.zip": {"num_bytes": 5230207, "checksum": "0c185809b807c00df05eeca2c504dc5d6d0ecbf98cf60ea51f7d1e690493b69a"}}, "download_size": 5230207, "post_processing_size": null, "dataset_size": 16557749, "size_in_bytes": 21787956}}

dummy/sharc/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8d50f53579b4052305b2bd807c6228d5bfec90b9494213f9a6d340f09e3a6c32
+size 5683

sharc.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.
+"""ShARC: A Conversational Question Answering dataset focussing on question answering from texts containing rules."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@misc{saeidi2018interpretation,
+      title={Interpretation of Natural Language Rules in Conversational Machine Reading},
+      author={Marzieh Saeidi and Max Bartolo and Patrick Lewis and Sameer Singh and Tim Rocktäschel and Mike Sheldon and Guillaume Bouchard and Sebastian Riedel},
+      year={2018},
+      eprint={1809.01494},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """\
+ShARC is a Conversational Question Answering dataset focussing on question answering from texts containing rules. \
+The goal is to answer questions by possibly asking follow-up questions first. It is assumed assume that the question is often underspecified, \
+in the sense that the question does not provide enough information to be answered directly. However, an agent can use the supporting rule text to \
+infer what needs to be asked in order to determine the final answer.
+"""
+
+_URL = "https://sharc-data.github.io/data/sharc1-official.zip"
+
+
+class Sharc(datasets.GeneratorBasedBuilder):
+    """ShARC: A Conversational Question Answering dataset focussing on question answering from texts containing rules."""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="sharc", version=datasets.Version("1.0.0")),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "utterance_id": datasets.Value("string"),
+                    "source_url": datasets.Value("string"),
+                    "snippet": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "scenario": datasets.Value("string"),
+                    "history": [
+                        {"follow_up_question": datasets.Value("string"), "follow_up_answer": datasets.Value("string")}
+                    ],
+                    "evidence": [
+                        {"follow_up_question": datasets.Value("string"), "follow_up_answer": datasets.Value("string")}
+                    ],
+                    "answer": datasets.Value("string"),
+                    "negative_question": datasets.Value("bool_"),
+                    "negative_scenario": datasets.Value("bool_"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://sharc-data.github.io/index.html",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        extracted_path = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data_dir": os.path.join(extracted_path, "sharc1-official"), "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"data_dir": os.path.join(extracted_path, "sharc1-official"), "split": "dev"},
+            ),
+        ]
+
+    def _generate_examples(self, data_dir, split):
+        with open(
+            os.path.join(data_dir, "negative_sample_utterance_ids", "sharc_negative_scenario_utterance_ids.txt"),
+            encoding="utf-8",
+        ) as f:
+            negative_scenario_ids = f.readlines()
+            negative_scenario_ids = [id_.strip() for id_ in negative_scenario_ids]
+        with open(
+            os.path.join(data_dir, "negative_sample_utterance_ids", "sharc_negative_question_utterance_ids.txt"),
+            encoding="utf-8",
+        ) as f:
+            negative_question_ids = f.readlines()
+            negative_question_ids = [id_.strip() for id_ in negative_question_ids]
+
+        data_file = os.path.join(data_dir, "json", f"sharc_{split}.json")
+        with open(data_file, encoding="utf-8") as f:
+            examples = json.load(f)
+            for i, example in enumerate(examples):
+                example.pop("tree_id")
+
+                example["negative_question"] = example["utterance_id"] in negative_question_ids
+                example["negative_scenario"] = example["utterance_id"] in negative_scenario_ids
+
+                example["id"] = example["utterance_id"]
+
+                # the keys are misspelled for one of the example in dev set
+                # fix it here
+                for evidence in example["evidence"]:
+                    if evidence.get("followup_answer") is not None:
+                        evidence["follow_up_answer"] = evidence.pop("followup_answer")
+                    if evidence.get("followup_question") is not None:
+                        evidence["follow_up_question"] = evidence.pop("followup_question")
+                yield example["id"], example