Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
unknown
Language Creators:
machine-generated
Annotations Creators:
expert-generated
Source Datasets:
original
License:
File size: 1,876 Bytes
d421e49 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
"""SNAP dataset"""
import datasets
_CITATION = """
@inproceedings{celebi2016segmenting,
title={Segmenting hashtags using automatically created training data},
author={Celebi, Arda and {\"O}zg{\"u}r, Arzucan},
booktitle={Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)},
pages={2981--2985},
year={2016}
}
"""
_DESCRIPTION = """
Automatically segmented 803K SNAP Twitter Data Set hashtags with the heuristic described in the paper "Segmenting hashtags using automatically created training data".
"""
_URL = "https://raw.githubusercontent.com/ruanchaves/hashformers/master/datasets/SNAP.Hashtags.Segmented.w.Heuristics.txt"
class Snap(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/ardax/hashtag-segmentor",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download(_URL)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files}),
]
def _generate_examples(self, filepath):
with open(filepath, 'r') as f:
for idx, line in enumerate(f):
yield idx, {
"index": idx,
"hashtag": line.strip().replace(" ", ""),
"segmentation": line.strip()
} |