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"""HashSet dataset."""
import datasets
import pandas as pd
_CITATION = """
@article{kodali2022hashset,
title={HashSet--A Dataset For Hashtag Segmentation},
author={Kodali, Prashant and Bhatnagar, Akshala and Ahuja, Naman and Shrivastava, Manish and Kumaraguru, Ponnurangam},
journal={arXiv preprint arXiv:2201.06741},
year={2022}
}
"""
_DESCRIPTION = """
Hashset is a new dataset consisiting on 1.9k manually annotated and 3.3M loosely supervised tweets for testing the
efficiency of hashtag segmentation models. We compare State of The Art Hashtag Segmentation models on Hashset and other
baseline datasets (STAN and BOUN). We compare and analyse the results across the datasets to argue that HashSet can act
as a good benchmark for hashtag segmentation tasks.
HashSet Distant: 3.3M loosely collected camel cased hashtags containing hashtag and their segmentation.
"""
_URL = "https://raw.githubusercontent.com/prashantkodali/HashSet/master/datasets/hashset/HashSet-Distant.csv"
class HashSetDistant(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/prashantkodali/HashSet/",
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):
records = pd.read_csv(filepath).to_dict("records")
for idx, row in enumerate(records):
yield idx, {
"index": row["Unnamed: 0.1"],
"hashtag": row["Unsegmented_hashtag"],
"segmentation": row["Segmented_hashtag"]
}
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