Datasets:
Languages:
Bengali
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
machine-generated
Source Datasets:
extended
ArXiv:
Tags:
License:
File size: 3,992 Bytes
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"""DailyDialogue Bengali Dataset"""
import os
import json
import datasets
_CITATION = """\
@inproceedings{bhattacharjee-etal-2023-banglanlg,
title = "{B}angla{NLG} and {B}angla{T}5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in {B}angla",
author = "Bhattacharjee, Abhik and
Hasan, Tahmid and
Ahmad, Wasi Uddin and
Shahriyar, Rifat",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.54",
pages = "726--735",
abstract = "This work presents {`}BanglaNLG,{'} a comprehensive benchmark for evaluating natural language generation (NLG) models in Bangla, a widely spoken yet low-resource language. We aggregate six challenging conditional text generation tasks under the BanglaNLG benchmark, introducing a new dataset on dialogue generation in the process. Furthermore, using a clean corpus of 27.5 GB of Bangla data, we pretrain {`}BanglaT5{'}, a sequence-to-sequence Transformer language model for Bangla. BanglaT5 achieves state-of-the-art performance in all of these tasks, outperforming several multilingual models by up to 9{\%} absolute gain and 32{\%} relative gain. We are making the new dialogue dataset and the BanglaT5 model publicly available at https://github.com/csebuetnlp/BanglaNLG in the hope of advancing future research on Bangla NLG.",
}
"""
_DESCRIPTION = """\
DailyDialogue (bengali) has been derived from the original English dataset.
"""
_HOMEPAGE = "https://github.com/csebuetnlp/BanglaNLG"
_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)"
_URL = "https://huggingface.co/datasets/csebuetnlp/dailydialogue_bn/resolve/main/data/dailydialogue_bn.tar.bz2"
_VERSION = datasets.Version("0.0.1")
class DailydialogueBn(datasets.GeneratorBasedBuilder):
"""DailyDialogue Bengali Dataset"""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="dailydialogue_bn",
version=_VERSION,
description=_DESCRIPTION,
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"dialogue": datasets.features.Sequence(
datasets.Value("string")
),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = os.path.join(dl_manager.download_and_extract(_URL), "dailydialogue_bn")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, "train.jsonl"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(data_dir, "test.jsonl"),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(data_dir, "validation.jsonl"),
},
),
]
def _generate_examples(self, filepath):
"""Yields examples as (key, example) tuples."""
with open(filepath, encoding="utf-8") as f:
for i, line in enumerate(f):
data = json.loads(line.strip())['source']
yield i, {
"id": str(i),
"dialogue": data
} |