File size: 3,669 Bytes
75b8815 ff8a1cd 75b8815 ff8a1cd 75b8815 ff8a1cd 75b8815 347c94b ff8a1cd 75b8815 97824bf 75b8815 347c94b ff8a1cd 87f5157 a18279e ff8a1cd 87f5157 ff8a1cd 75b8815 |
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 |
import os
import datasets
import pandas as pd
import json
class semiRelConfig(datasets.BuilderConfig):
def __init__(self, features, data_url, **kwargs):
super(semiRelConfig, self).__init__(**kwargs)
self.features = features
self.data_url = data_url
class semiRel(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
semiRelConfig(
name="pairs",
features={
"ltable_id": datasets.Value("string"),
"rtable_id": datasets.Value("string"),
"label": datasets.Value("string"),
},
data_url="https://huggingface.co/datasets/matchbench/semi-Rel/resolve/main/",
),
semiRelConfig(
name="source",
features={
"id": datasets.Value("string"),
"title": datasets.Value("string"),
"director": datasets.Value("string"),
"actors": datasets.Value("string"),
"year": datasets.Value("string"),
"rating": datasets.Value("string"),
"information": datasets.Value("string"),
},
data_url="https://huggingface.co/datasets/matchbench/semi-Rel/resolve/main/left.csv",
),
semiRelConfig(
name="target",
features={
"content": datasets.Value("string"),
},
data_url="https://huggingface.co/datasets/matchbench/semi-Rel/resolve/main/right.json",
),
]
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features(self.config.features)
)
def _split_generators(self, dl_manager):
if self.config.name == "pairs":
return [
datasets.SplitGenerator(
name=split,
gen_kwargs={
"path_file": dl_manager.download_and_extract(
os.path.join(self.config.data_url, f"{split}.csv")),
"split": split,
}
)
for split in ["train", "valid", "test"]
]
if self.config.name == "source":
return [datasets.SplitGenerator(name="source", gen_kwargs={
"path_file": dl_manager.download_and_extract(self.config.data_url), "split": "source", })]
if self.config.name == "target":
return [datasets.SplitGenerator(name="target", gen_kwargs={
"path_file": dl_manager.download_and_extract(self.config.data_url), "split": "target", })]
def _generate_examples(self, path_file, split):
if split in ['target']:
with open(path_file, "r") as f:
file = json.load(f)
for i in range(len(file)):
yield i, {
"content": json.dumps(file[i])
}
elif split in ['source']:
file = pd.read_csv(path_file)
for i, row in file.iterrows():
yield i, {
"id": row["id"],
"title": row["title"],
"director": row["director"],
"actors": row["actors"],
"year": row["year"],
"rating": row["rating"],
"information": row["information"],
}
else:
file = pd.read_csv(path_file)
for i, row in file.iterrows():
yield i, {
"ltable_id": row["ltable_id"],
"rtable_id": row["rtable_id"],
"label": row["label"],
}
|