Datasets:
Tasks:
Text Generation
Sub-tasks:
dialogue-modeling
Languages:
Russian
Size:
1M<n<10M
Tags:
conversations
License:
File size: 1,934 Bytes
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import datasets
import os
from datasets import Dataset, DatasetDict
import gzip
import json
_CITATION = """\
@misc{Conversations,
author = {Ilya Koziev},
title = {Russian-Language Dialogues Dataset},
year = {2025},
publisher = {Hugging Face},
howpublished = {\\url{https://huggingface.co/datasets/inkoziev/Conversations}},
}
"""
_DESCRIPTION = """\
Russian-Language Dialogues Dataset
"""
class DatasetConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(DatasetConfig, self).__init__(**kwargs)
class Conversations(datasets.GeneratorBasedBuilder):
BUILDER_CONFIG_CLASS = DatasetConfig
BUILDER_CONFIGS = [
DatasetConfig(name="Conversations",
version=datasets.Version("27.02.2025"),
description=_DESCRIPTION,
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"domain": datasets.Value("string"),
"conversation": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
download_url = "https://huggingface.co/datasets/inkoziev/Conversations/resolve/main/conversations.jsonl.gz"
#download_url = "/home/inkoziev/github/Conversations/conversations.jsonl.gz"
path = dl_manager.download(download_url)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"datapath": path},
)
]
def _generate_examples(self, datapath):
with gzip.open(datapath, "rt", encoding="utf-8") as f:
for iline, line in enumerate(f):
yield iline, json.loads(line)
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