File size: 9,105 Bytes
5cd0931 7d6410b 5cd0931 b55a988 5cd0931 027a870 5cd0931 71349cc 5cd0931 b55a988 5cd0931 b55a988 5cd0931 b55a988 5cd0931 b55a988 5cd0931 b55a988 5cd0931 b55a988 5cd0931 d3fedd5 f8c6b11 d3fedd5 5cd0931 b55a988 5cd0931 a062d30 2cdfb4c a062d30 70b637b e8de9d0 779f055 e8de9d0 e2e397d e8de9d0 5cd0931 b55a988 5cd0931 a062d30 2cdfb4c a062d30 70b637b e8de9d0 779f055 e8de9d0 e2e397d e8de9d0 5cd0931 f8c6b11 5cd0931 1bdf84f f8c6b11 1bdf84f 5cd0931 f8c6b11 5cd0931 f8c6b11 5cd0931 7d6410b bcc6f80 ac14605 7d6410b ac14605 5cd0931 ac14605 7d6410b 5cd0931 7d6410b a80999a 7d6410b a80999a 7d6410b e8de9d0 05e8420 7d6410b 0aa3afb 7d6410b 027a870 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 |
import datasets
import os
import pickle
class Dbp15kFrEnConfig(datasets.BuilderConfig):
def __init__(self, features, data_url, citation, url, label_classes=("False", "True"), **kwargs):
super(Dbp15kFrEnConfig, self).__init__(version=datasets.Version("0.0.1"), **kwargs)
self.features = features
self.label_classes = label_classes
self.data_url = data_url
self.citation = citation
self.url = url
class Dbp15kFrEn(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
Dbp15kFrEnConfig(
name="source",
features=["column1", "column2", "column3"],
citation="TODO",
url="TODO",
data_url="https://huggingface.co/datasets/matchbench/dbp15k-fr-en/resolve/main/dbp15k-fr-en-src.zip"
),
Dbp15kFrEnConfig(
name="target",
features=["column1", "column2", "column3"],
citation="TODO",
url="TODO",
data_url="https://huggingface.co/datasets/matchbench/dbp15k-fr-en/resolve/main/dbp15k-fr-en-tgt.zip"
),
Dbp15kFrEnConfig(
name="pairs",
features=["left_id", "right_id"],
citation="TODO",
url="TODO",
data_url="https://huggingface.co/datasets/matchbench/dbp15k-fr-en/resolve/main/dbp15k-fr-en-pairs.zip"
),
]
def _info(self):
if self.config.name=="source":
features = {feature: datasets.Value("string") for feature in self.config.features}
elif self.config.name=="target":
features = {feature: datasets.Value("string") for feature in self.config.features}
elif self.config.name=="pairs":
features = {feature: datasets.Value("string") for feature in self.config.features}
return datasets.DatasetInfo(
features=datasets.Features(features)
)
def _split_generators(self, dl_manager):
dl_dir = dl_manager.download_and_extract(self.config.data_url) or ""
#task_name = _get_task_name_from_data_url(self.config.data_url)
#dl_dir = os.path.join(dl_dir, task_name)
if self.config.name == "source":
return [
datasets.SplitGenerator(
name="ent_ids",
gen_kwargs={
"data_file": os.path.join(dl_dir, "ent_ids_1"),
"split": "ent_ids",
},
),
datasets.SplitGenerator(
name="rel_ids",
gen_kwargs={
"data_file": os.path.join(dl_dir, "rel_ids_1"),
"split": "rel_ids",
},
),
datasets.SplitGenerator(
name="attr_triples",
gen_kwargs={
"data_file": os.path.join(dl_dir, "att_triples_1"),
"split": "attr_triples",
},
),
datasets.SplitGenerator(
name="rel_triples",
gen_kwargs={
"data_file": os.path.join(dl_dir, "triples_1"),
"split": "rel_triples",
},
),
datasets.SplitGenerator(
name="description",
gen_kwargs={
"data_file": os.path.join(dl_dir, "description1.pkl"),
"split": "description",
},
),
datasets.SplitGenerator(
name="rel_triples_whole",
gen_kwargs={
"data_file": os.path.join(dl_dir, "rel_triples_whole_1"),
"split": "rel_triples_whole",
},
),
datasets.SplitGenerator(
name="attr_triples_whole",
gen_kwargs={
"data_file": os.path.join(dl_dir, "att_triples_whole_1"),
"split": "attr_triples_whole",
},
),
]
elif self.config.name == "target":
return [
datasets.SplitGenerator(
name="ent_ids",
gen_kwargs={
"data_file": os.path.join(dl_dir, "ent_ids_2"),
"split": "ent_ids",
},
),
datasets.SplitGenerator(
name="rel_ids",
gen_kwargs={
"data_file": os.path.join(dl_dir, "rel_ids_2"),
"split": "rel_ids",
},
),
datasets.SplitGenerator(
name="attr_triples",
gen_kwargs={
"data_file": os.path.join(dl_dir, "att_triples_2"),
"split": "attr_triples",
},
),
datasets.SplitGenerator(
name="rel_triples",
gen_kwargs={
"data_file": os.path.join(dl_dir, "triples_2"),
"split": "rel_triples",
},
),
datasets.SplitGenerator(
name="description",
gen_kwargs={
"data_file": os.path.join(dl_dir, "description2.pkl"),
"split": "description",
},
),
datasets.SplitGenerator(
name="rel_triples_whole",
gen_kwargs={
"data_file": os.path.join(dl_dir, "rel_triples_whole_2"),
"split": "rel_triples_whole",
},
),
datasets.SplitGenerator(
name="attr_triples_whole",
gen_kwargs={
"data_file": os.path.join(dl_dir, "att_triples_whole_2"),
"split": "attr_triples_whole",
},
),
]
elif self.config.name == "pairs":
return [
datasets.SplitGenerator(
name="train",
gen_kwargs={
"data_file": os.path.join(dl_dir, "train_links"),
"split": "train",
},
),
datasets.SplitGenerator(
name="valid",
gen_kwargs={
"data_file": os.path.join(dl_dir, "valid_links"),
"split": "valid",
},
),
datasets.SplitGenerator(
name="test",
gen_kwargs={
"data_file": os.path.join(dl_dir, "test_links"),
"split": "test",
},
),
datasets.SplitGenerator(
name="sup",
gen_kwargs={
"data_file": os.path.join(dl_dir, "sup_pairs"),
"split": "sup",
},
),
datasets.SplitGenerator(
name="ref",
gen_kwargs={
"data_file": os.path.join(dl_dir, "ref_pairs"),
"split": "ref",
},
),
]
def _generate_examples(self, data_file, split):
if split in ["description"]:
des = pickle.load(open(data_file,"rb"))
i = -1
for ent,ori_des in des.items():
i += 1
yield i, {
"column1": ent,
"column2": ori_des,
"column3": None
}
else:
f = open(data_file,"r",encoding='utf-8')
data = f.readlines()
for i in range(len(data)):
#print(row)
if self.config.name in ["source", "target"]:
if split in ["ent_ids","rel_ids"]:
row = data[i].strip('\n').split('\t')
yield i, {
"column1": row[0],
"column2": row[1],
"column3": None
}
elif split in ["rel_triples","rel_triples_whole"]:
row = data[i].strip('\n').split('\t')
yield i, {
"column1": row[0],
"column2": row[1],
"column3": row[2]
}
elif split in ["attr_triples","attr_triples_whole"]:
row = data[i].rstrip('\n').split(' ',2)
yield i, {
"column1": row[0],
"column2": row[1],
"column3": row[2]
}
if self.config.name == "pairs":
row = data[i].strip('\n').split('\t')
yield i, {
"left_id": row[0],
"right_id": row[1]
} |