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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+*.arrow filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+- found
+languages:
+- en
+licenses:
+- cc-by-sa-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-retrieval
+task_ids:
+- fact-checking-retrieval
+---
+
+# 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:** https://hover-nlp.github.io/
+- **Repository:** https://github.com/hover-nlp/hover
+- **Paper:** https://arxiv.org/abs/2011.03088
+- **Leaderboard:** https://hover-nlp.github.io/
+- **Point of Contact:** [More Information Needed]
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+A sample training set is provided below
+
+```
+{'id': 14856, 'uid': 'a0cf45ea-b5cd-4c4e-9ffa-73b39ebd78ce', 'claim': 'The park at which Tivolis Koncertsal is located opened on 15 August 1843.', 'supporting_facts': [{'key': 'Tivolis Koncertsal', 'value': 0}, {'key': 'Tivoli Gardens', 'value': 1}], 'label': 'SUPPORTED', 'num_hops': 2, 'hpqa_id': '5abca1a55542993a06baf937'}
+```
+
+Please note that in test set sentence only id, uid and claim are available. Labels are not available in test set and are represented by -1.
+
+
+### 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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+{"default": {"description": "HoVer is an open-domain, many-hop fact extraction and claim verification dataset built upon the Wikipedia corpus. The original 2-hop claims are adapted from question-answer pairs from HotpotQA. It is collected by a team of NLP researchers at UNC Chapel Hill and Verisk Analytics.\n", "citation": "@inproceedings{jiang2020hover,\n  title={{HoVer}: A Dataset for Many-Hop Fact Extraction And Claim Verification},\n  author={Yichen Jiang and Shikha Bordia and Zheng Zhong and Charles Dognin and Maneesh Singh and Mohit Bansal.},\n  booktitle={Findings of the Conference on Empirical Methods in Natural Language Processing ({EMNLP})},\n  year={2020}\n}\n", "homepage": "https://hover-nlp.github.io/", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "uid": {"dtype": "string", "id": null, "_type": "Value"}, "claim": {"dtype": "string", "id": null, "_type": "Value"}, "supporting_facts": [{"key": {"dtype": "string", "id": null, "_type": "Value"}, "value": {"dtype": "int32", "id": null, "_type": "Value"}}], "label": {"num_classes": 2, "names": ["NOT_SUPPORTED", "SUPPORTED"], "names_file": null, "id": null, "_type": "ClassLabel"}, "num_hops": {"dtype": "int32", "id": null, "_type": "Value"}, "hpqa_id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "hover", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5532178, "num_examples": 18171, "dataset_name": "hover"}, "validation": {"name": "validation", "num_bytes": 1299252, "num_examples": 4000, "dataset_name": "hover"}, "test": {"name": "test", "num_bytes": 927513, "num_examples": 4000, "dataset_name": "hover"}}, "download_checksums": {"https://raw.githubusercontent.com/hover-nlp/hover/main/data/hover/hover_train_release_v1.1.json": {"num_bytes": 9205582, "checksum": "1f1cd57abd616fa00c70bdc575ce77c16fc6cf1a6cffd5ff87c208030a336bb6"}, "https://raw.githubusercontent.com/hover-nlp/hover/main/data/hover/hover_dev_release_v1.1.json": {"num_bytes": 2153439, "checksum": "67c14858f2d7fcdb96b6fe3d538ffcd6f76e3ba594aa2c0cd4359f601101e89d"}, "https://raw.githubusercontent.com/hover-nlp/hover/main/data/hover/hover_test_release_v1.1.json": {"num_bytes": 898814, "checksum": "c58e7fc59b4962213a5a6d41d746384ee88a7645e36cb3a439969cf762c8ec24"}}, "download_size": 12257835, "post_processing_size": null, "dataset_size": 7758943, "size_in_bytes": 20016778}}

dummy/1.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e4209fce73e0e85faa36cd2b5cfdb6065c57a1e314a9a31b196e7d620b866c70
+size 2277

hover.py

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+# coding=utf-8
+# Copyright 2020 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.
+
+# Lint as: python3
+import json
+
+import datasets
+
+
+_DESCRIPTION = """\
+HoVer is an open-domain, many-hop fact extraction and claim verification dataset built upon the Wikipedia corpus. The original 2-hop claims are adapted from question-answer pairs from HotpotQA. It is collected by a team of NLP researchers at UNC Chapel Hill and Verisk Analytics.
+"""
+_HOMEPAGE_URL = "https://hover-nlp.github.io/"
+_CITATION = """\
+@inproceedings{jiang2020hover,
+  title={{HoVer}: A Dataset for Many-Hop Fact Extraction And Claim Verification},
+  author={Yichen Jiang and Shikha Bordia and Zheng Zhong and Charles Dognin and Maneesh Singh and Mohit Bansal.},
+  booktitle={Findings of the Conference on Empirical Methods in Natural Language Processing ({EMNLP})},
+  year={2020}
+}
+"""
+
+_TRAIN_URL = "https://raw.githubusercontent.com/hover-nlp/hover/main/data/hover/hover_train_release_v1.1.json"
+_VALID_URL = "https://raw.githubusercontent.com/hover-nlp/hover/main/data/hover/hover_dev_release_v1.1.json"
+_TEST_URL = "https://raw.githubusercontent.com/hover-nlp/hover/main/data/hover/hover_test_release_v1.1.json"
+
+
+class Hover(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "uid": datasets.Value("string"),
+                    "claim": datasets.Value("string"),
+                    "supporting_facts": [
+                        {
+                            "key": datasets.Value("string"),
+                            "value": datasets.Value("int32"),
+                        }
+                    ],
+                    "label": datasets.ClassLabel(names=["NOT_SUPPORTED", "SUPPORTED"]),
+                    "num_hops": datasets.Value("int32"),
+                    "hpqa_id": datasets.Value("string"),
+                },
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        train_path = dl_manager.download_and_extract(_TRAIN_URL)
+        valid_path = dl_manager.download_and_extract(_VALID_URL)
+        test_path = dl_manager.download_and_extract(_TEST_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"datapath": train_path, "datatype": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"datapath": valid_path, "datatype": "valid"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"datapath": test_path, "datatype": "test"},
+            ),
+        ]
+
+    def _generate_examples(self, datapath, datatype):
+        with open(datapath, encoding="utf-8") as f:
+            data = json.load(f)
+
+        for sentence_counter, d in enumerate(data):
+            if datatype != "test":
+                resp = {
+                    "id": sentence_counter,
+                    "uid": d["uid"],
+                    "claim": d["claim"],
+                    "supporting_facts": [{"key": x[0], "value": x[1]} for x in d["supporting_facts"]],
+                    "label": d["label"],
+                    "num_hops": d["num_hops"],
+                    "hpqa_id": d["hpqa_id"],
+                }
+            else:
+                resp = {
+                    "id": sentence_counter,
+                    "uid": d["uid"],
+                    "claim": d["claim"],
+                    "supporting_facts": [],
+                    "label": -1,
+                    "num_hops": -1,
+                    "hpqa_id": "None",
+                }
+            yield sentence_counter, resp