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:
+- expert-generated
+language_creators:
+- found
+languages:
+- en
+licenses:
+- cc-by-sa-3-0
+multilinguality:
+- monolingual
+size_categories:
+- 1M<n<5M
+source_datasets:
+- original
+task_categories:
+- conditional-text-generation
+task_ids:
+- table-to-text
+---
+
+# 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:** None
+- **Repository:** https://github.com/google-research-datasets/great
+- **Paper:** https://openreview.net/forum?id=B1lnbRNtwr
+- **Leaderboard:** [More Information Needed]
+- **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
+
+Here are some examples of questions and facts:
+
+
+### 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]

dummy/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:455dee21b3220e69db465c09bca16900da304dab2673f20dc4dd4ca942a3e4f4
+size 3650

great_code.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 = """\
+The dataset for the variable-misuse task, described in the ICLR 2020 paper 'Global Relational Models of Source Code' [https://openreview.net/forum?id=B1lnbRNtwr]
+
+This is the public version of the dataset used in that paper. The original, used to produce the graphs in the paper, could not be open-sourced due to licensing issues. See the public associated code repository [https://github.com/VHellendoorn/ICLR20-Great] for results produced from this dataset.
+
+This dataset was generated synthetically from the corpus of Python code in the ETH Py150 Open dataset [https://github.com/google-research-datasets/eth_py150_open].
+"""
+_HOMEPAGE_URL = ""
+_CITATION = """\
+@inproceedings{DBLP:conf/iclr/HellendoornSSMB20,
+  author    = {Vincent J. Hellendoorn and
+               Charles Sutton and
+               Rishabh Singh and
+               Petros Maniatis and
+               David Bieber},
+  title     = {Global Relational Models of Source Code},
+  booktitle = {8th International Conference on Learning Representations, {ICLR} 2020,
+               Addis Ababa, Ethiopia, April 26-30, 2020},
+  publisher = {OpenReview.net},
+  year      = {2020},
+  url       = {https://openreview.net/forum?id=B1lnbRNtwr},
+  timestamp = {Thu, 07 May 2020 17:11:47 +0200},
+  biburl    = {https://dblp.org/rec/conf/iclr/HellendoornSSMB20.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+_TRAIN_URLS = [
+    f"https://raw.githubusercontent.com/google-research-datasets/great/master/train/train__VARIABLE_MISUSE__SStuB.txt-{x:05d}-of-00300"
+    for x in range(300)
+]
+_TEST_URLS = [
+    f"https://raw.githubusercontent.com/google-research-datasets/great/master/eval/eval__VARIABLE_MISUSE__SStuB.txt-{x:05d}-of-00300"
+    for x in range(300)
+]
+_VALID_URLS = [
+    f"https://raw.githubusercontent.com/google-research-datasets/great/master/dev/dev__VARIABLE_MISUSE__SStuB.txt-{x:05d}-of-00300"
+    for x in range(300)
+]
+
+
+class GreatCode(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "source_tokens": datasets.Sequence(datasets.Value("string")),
+                    "has_bug": datasets.Value("bool"),
+                    "error_location": datasets.Value("int32"),
+                    "repair_candidates": datasets.Sequence(datasets.Value("string")),
+                    "bug_kind": datasets.Value("int32"),
+                    "bug_kind_name": datasets.Value("string"),
+                    "repair_targets": datasets.Sequence(datasets.Value("int32")),
+                    "edges": [
+                        [
+                            {
+                                "before_index": datasets.Value("int32"),
+                                "after_index": datasets.Value("int32"),
+                                "edge_type": datasets.Value("int32"),
+                                "edge_type_name": datasets.Value("string"),
+                            }
+                        ]
+                    ],
+                    "provenances": [
+                        {
+                            "datasetProvenance": {
+                                "datasetName": datasets.Value("string"),
+                                "filepath": datasets.Value("string"),
+                                "license": datasets.Value("string"),
+                                "note": 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_URLS)
+        valid_path = dl_manager.download_and_extract(_VALID_URLS)
+        test_path = dl_manager.download_and_extract(_TEST_URLS)
+        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):
+        for dp in datapath:
+            with open(dp, "r", encoding="utf-8") as json_file:
+                json_list = list(json_file)
+
+            for example_counter, json_str in enumerate(json_list):
+                result = json.loads(json_str)
+                response = {
+                    "id": example_counter,
+                    "source_tokens": result["source_tokens"],
+                    "has_bug": result["has_bug"],
+                    "error_location": result["error_location"],
+                    "repair_candidates": [str(x) for x in result["repair_candidates"]],
+                    "bug_kind": result["bug_kind"],
+                    "bug_kind_name": result["bug_kind_name"],
+                    "repair_targets": result["repair_targets"],
+                    "edges": [
+                        [
+                            {
+                                "before_index": result["edges"][x][0],
+                                "after_index": result["edges"][x][1],
+                                "edge_type": result["edges"][x][2],
+                                "edge_type_name": result["edges"][x][3],
+                            }
+                        ]
+                        for x in range(len(result["edges"]))
+                    ],
+                    "provenances": result["provenances"],
+                }
+                yield example_counter, response