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README.md

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----
-annotations_creators:
-- expert-generated
-language_creators:
-- found
-language:
-- en
-license:
-- cc-by-sa-3.0
-multilinguality:
-- monolingual
-size_categories:
-- 1M<n<10M
-source_datasets:
-- original
-task_categories:
-- table-to-text
-task_ids: []
-paperswithcode_id: null
-pretty_name: GREAT
-dataset_info:
-  features:
-  - name: id
-    dtype: int32
-  - name: source_tokens
-    sequence: string
-  - name: has_bug
-    dtype: bool
-  - name: error_location
-    dtype: int32
-  - name: repair_candidates
-    sequence: string
-  - name: bug_kind
-    dtype: int32
-  - name: bug_kind_name
-    dtype: string
-  - name: repair_targets
-    sequence: int32
-  - name: edges
-    list:
-      list:
-      - name: before_index
-        dtype: int32
-      - name: after_index
-        dtype: int32
-      - name: edge_type
-        dtype: int32
-      - name: edge_type_name
-        dtype: string
-  - name: provenances
-    list:
-    - name: datasetProvenance
-      struct:
-      - name: datasetName
-        dtype: string
-      - name: filepath
-        dtype: string
-      - name: license
-        dtype: string
-      - name: note
-        dtype: string
-  splits:
-  - name: train
-    num_bytes: 14705534822
-    num_examples: 1798742
-  - name: validation
-    num_bytes: 1502956919
-    num_examples: 185656
-  - name: test
-    num_bytes: 7880762248
-    num_examples: 968592
-  download_size: 23310374002
-  dataset_size: 24089253989
----
-
-# Dataset Card for GREAT
-
-## 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)
-  - [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)
-  - [Contributions](#contributions)
-
-## 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]
-### Contributions
-
-Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

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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 file_idx, dp in enumerate(datapath):
-            with open(dp, "r", encoding="utf-8") as json_file:
-                for example_counter, json_str in enumerate(json_file):
-                    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 f"{file_idx}_{example_counter}", response