|
"""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"]
|
|
}
|
|
|
