Datasets:

Languages:
Bengali
Multilinguality:
monolingual
Size Categories:
100k<n<1M
Language Creators:
found
Annotations Creators:
found
Source Datasets:
original
ArXiv:
License:
BanglaParaphrase / BanglaParaphrase.py
Shukti's picture
correct data path
8963130
import json
import os
import datasets
_CITATION = """
to be added
"""
_DESCRIPTION = """\
We present a high quality bangla paraphrase dataset containing about 466k paraphrase pairs. The paraphrases ensures high quality by being semantically coherent and syntactically diverse.
"""
_HOMEPAGE = "https://github.com/csebuetnlp/banglaparaphrase"
_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)"
_URL = "https://huggingface.co/datasets/csebuetnlp/BanglaParaphrase/resolve/main/data/BanglaParaphrase.zip"
_LANGUAGES = [
"bn"
]
class IndicParaphrase(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="bn",
version=datasets.Version("1.0.0")
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"source": datasets.Value("string"),
"target": datasets.Value("string")
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE,
version=self.VERSION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
url = _URL
data_dir = dl_manager.download_and_extract(url)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, "BanglaParaphrase/train.jsonl"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(data_dir, "BanglaParaphrase/test.jsonl"),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(data_dir, "BanglaParaphrase/validation.jsonl"),
},
),
]
def _generate_examples(self, filepath):
"""Yields examples as (key, example) tuples."""
with open(filepath, encoding="utf-8") as f:
for idx_, row in enumerate(f):
data = json.loads(row)
yield idx_, {
"source": data["source"],
"target": data["target"],
}