Datasets:
Tasks:
Table to Text
Languages:
English
Multilinguality:
monolingual
Size Categories:
1M<n<10M
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
License:
File size: 6,960 Bytes
73a0727 8740f45 73a0727 7969364 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 |
# 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
|