Datasets:
Languages:
Russian
Multilinguality:
monolingual
Size Categories:
unknown
Language Creators:
machine-generated
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
License:
File size: 2,058 Bytes
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"""HSE Russian dataset by Glushkova et al.."""
import datasets
import pandas as pd
from functools import reduce
_CITATION = """
@article{glushkova2019char,
title={Char-RNN and Active Learning for Hashtag Segmentation},
author={Glushkova, Taisiya and Artemova, Ekaterina},
journal={arXiv preprint arXiv:1911.03270},
year={2019}
}
"""
_DESCRIPTION = """
2000 real hashtags collected from several pages about civil services on vk.com (a Russian social network)
and then segmented manually.
"""
_URL = "https://raw.githubusercontent.com/glushkovato/hashtag_segmentation/master/data/test_rus.csv"
class HSE(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"index": datasets.Value("int32"),
"hashtag": datasets.Value("string"),
"segmentation": datasets.Value("string")
}
),
supervised_keys=None,
homepage="https://github.com/glushkovato/hashtag_segmentation",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download(_URL)
return [
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files }),
]
def _generate_examples(self, filepath):
df = pd.read_csv(filepath)
records = df.to_dict("records")
def get_segmentation(a, b):
return "".join(reduce(lambda x,y: x + y, list(zip(a,b)))).replace("0","").replace("1"," ").strip()
for idx, row in enumerate(records):
yield idx, {
"index": idx,
"hashtag": row["hashtag"],
"segmentation": get_segmentation(
row["hashtag"],
row["true_segmentation"]
)} |