File size: 2,200 Bytes
7fd7df1 |
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 |
import csv
import os
import datasets
_DESCRIPTION = """\
This is the Dutch version of the original SNLI dataset. The translation was performed using Google Translate. Original SNLI available at https://nlp.stanford.edu/projects/snli/
"""
class DutchSnli(datasets.GeneratorBasedBuilder):
"""The Dutch-translated Stanford Natural Language Inference (SNLI) Corpus."""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="plain_text",
version=datasets.Version("1.0.0", ""),
description="Plain text import of SNLI",
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"premise": datasets.Value("string"),
"hypothesis": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
}
),
# No default supervised_keys (as we have to pass both premise
# and hypothesis as input).
supervised_keys=None,
homepage="",
citation="",
)
def _split_generators(self, dl_manager: datasets.DownloadManager):
data_dir = dl_manager._base_path
return [
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "test.tsv")}
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "train.tsv")}
),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
with open(filepath, encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for idx, row in enumerate(reader):
if row["label"] != '-':
yield idx, {
"premise": row["premise"],
"hypothesis": row["hypothesis"],
"label": row["label"],
} |