File size: 9,725 Bytes
1067923 644dbbb 1067923 99098dc 43eec1c 99098dc 0e25155 1067923 99098dc 364b41d 99098dc 264e852 99098dc f227687 1067923 99098dc 1067923 99098dc 1067923 99098dc 1067923 99098dc 1067923 99098dc 1067923 99098dc 1067923 99098dc 1067923 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 |
"""A large crowd-sourced dataset for developing natural language interfaces for relational databases"""
import json
import os
import textwrap
import datasets
_CITATION = """\
@article{zhongSeq2SQL2017,
author = {Victor Zhong and
Caiming Xiong and
Richard Socher},
title = {Seq2SQL: Generating Structured Queries from Natural Language using
Reinforcement Learning},
journal = {CoRR},
volume = {abs/1709.00103},
year = {2017}
}
"""
_DESCRIPTION = """\
A large crowd-sourced dataset for developing natural language interfaces for relational databases
"""
_DATA_URL = "https://huggingface.co/datasets/SALT-NLP/wikisql_VALUE/resolve/main/data.zip"
_AGG_OPS = ["", "MAX", "MIN", "COUNT", "SUM", "AVG"]
_COND_OPS = ["=", ">", "<", "OP"]
class WikiSQLConfig(datasets.BuilderConfig):
"""BuilderConfig for WikiSQL."""
def __init__(
self,
name,
description,
train_path,
dev_path,
test_path,
**kwargs
):
super(WikiSQLConfig, self).__init__(version=datasets.Version("0.1.0", ""), **kwargs)
self.features = datasets.Features(
{
"phase": datasets.Value("int32"),
"question": datasets.Value("string"),
"table": {
"header": datasets.features.Sequence(datasets.Value("string")),
"page_title": datasets.Value("string"),
"page_id": datasets.Value("string"),
"types": datasets.features.Sequence(datasets.Value("string")),
"id": datasets.Value("string"),
"section_title": datasets.Value("string"),
"caption": datasets.Value("string"),
"rows": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
"name": datasets.Value("string"),
},
"sql": {
"human_readable": datasets.Value("string"),
"sel": datasets.Value("int32"),
"agg": datasets.Value("int32"),
"conds": datasets.features.Sequence(
{
"column_index": datasets.Value("int32"),
"operator_index": datasets.Value("int32"),
"condition": datasets.Value("string"),
}
),
},
}
)
self.name = name
self.description = description
self.train_path = train_path
self.dev_path = dev_path
self.test_path = test_path
class WikiSQL(datasets.GeneratorBasedBuilder):
"""WikiSQL: A large crowd-sourced dataset for developing natural language interfaces for relational databases"""
VERSION = datasets.Version("0.1.0")
BUILDER_CONFIGS = [
WikiSQLConfig(
name="AppE",
description=textwrap.dedent(
"""\
An Appalachian English variant of the large crowd-sourced dataset for developing natural language interfaces for relational databases"""
),
train_path="train_AppE.jsonl",
dev_path="dev_AppE.jsonl",
test_path="test_AppE.jsonl"
),
WikiSQLConfig(
name="ChcE",
description=textwrap.dedent(
"""\
A Chicano English variant of the large crowd-sourced dataset for developing natural language interfaces for relational databases"""
),
train_path="train_ChcE.jsonl",
dev_path="dev_ChcE.jsonl",
test_path="test_ChcE.jsonl"
),
WikiSQLConfig(
name="CollSgE",
description=textwrap.dedent(
"""\
A Singapore English (Singlish) variant of the large crowd-sourced dataset for developing natural language interfaces for relational databases"""
),
train_path="train_CollSgE.jsonl",
dev_path="dev_CollSgE.jsonl",
test_path="test_CollSgE.jsonl"
),
WikiSQLConfig(
name="IndE",
description=textwrap.dedent(
"""\
An Indian English variant of the large crowd-sourced dataset for developing natural language interfaces for relational databases"""
),
train_path="train_IndE.jsonl",
dev_path="dev_IndE.jsonl",
test_path="test_IndE.jsonl"
),
WikiSQLConfig(
name="UAAVE",
description=textwrap.dedent(
"""\
An Urban African American English variant of the large crowd-sourced dataset for developing natural language interfaces for relational databases"""
),
train_path="train_UAAVE.jsonl",
dev_path="dev_UAAVE.jsonl",
test_path="test_UAAVE.jsonl"
),
WikiSQLConfig(
name="MULTI",
description=textwrap.dedent(
"""\
A mixed-dialectal variant of the large crowd-sourced dataset for developing natural language interfaces for relational databases"""
),
train_path="train_MULTI.jsonl",
dev_path="dev_MULTI.jsonl",
test_path="test_MULTI.jsonl"
),
]
def _info(self):
return datasets.DatasetInfo(
description=self.config.description,
features=self.config.features,
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage="https://github.com/salesforce/WikiSQL",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_dir = dl_manager.download_and_extract(_DATA_URL)
dl_dir = os.path.join(dl_dir, "data")
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"main_filepath": os.path.join(dl_dir, self.config.test_path),
"tables_filepath": os.path.join(dl_dir, "test.tables.jsonl"),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"main_filepath": os.path.join(dl_dir, self.config.dev_path),
"tables_filepath": os.path.join(dl_dir, "dev.tables.jsonl"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"main_filepath": os.path.join(dl_dir, self.config.train_path),
"tables_filepath": os.path.join(dl_dir, "train.tables.jsonl"),
},
)
]
def _convert_to_human_readable(self, sel, agg, columns, conditions):
"""Make SQL query string. Based on https://github.com/salesforce/WikiSQL/blob/c2ed4f9b22db1cc2721805d53e6e76e07e2ccbdc/lib/query.py#L10"""
rep = f"SELECT {_AGG_OPS[agg]} {columns[sel] if columns is not None else f'col{sel}'} FROM table"
if conditions:
rep += " WHERE " + " AND ".join([f"{columns[i]} {_COND_OPS[o]} {v}" for i, o, v in conditions])
return " ".join(rep.split())
def _generate_examples(self, main_filepath, tables_filepath):
"""Yields examples."""
# Build dictionary to table_ids:tables
with open(tables_filepath, encoding="utf-8") as f:
tables = [json.loads(line) for line in f]
id_to_tables = {x["id"]: x for x in tables}
with open(main_filepath, encoding="utf-8") as f:
for idx, line in enumerate(f):
row = json.loads(line)
row["table"] = id_to_tables[row["table_id"]]
del row["table_id"]
# Handle missing data
row["table"]["page_title"] = row["table"].get("page_title", "")
row["table"]["section_title"] = row["table"].get("section_title", "")
row["table"]["caption"] = row["table"].get("caption", "")
row["table"]["name"] = row["table"].get("name", "")
row["table"]["page_id"] = str(row["table"].get("page_id", ""))
# Fix row types
row["table"]["rows"] = [[str(e) for e in r] for r in row["table"]["rows"]]
# Get human-readable version
row["sql"]["human_readable"] = self._convert_to_human_readable(
row["sql"]["sel"],
row["sql"]["agg"],
row["table"]["header"],
row["sql"]["conds"],
)
# Restructure sql->conds
# - wikiSQL provides a tuple [column_index, operator_index, condition]
# as 'condition' can have 2 types (float or str) we convert to dict
for i in range(len(row["sql"]["conds"])):
row["sql"]["conds"][i] = {
"column_index": row["sql"]["conds"][i][0],
"operator_index": row["sql"]["conds"][i][1],
"condition": str(row["sql"]["conds"][i][2]),
}
yield idx, row |