Datasets:
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"""HashSet dataset."""
import datasets
import pandas as pd
import json
_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 Manual: contains 1.9k manually annotated hashtags. Each row consists of the hashtag, segmented
hashtag ,named entity annotations, a list storing whether the hashtag contains mix of hindi and english
tokens and/or contains non-english tokens.
"""
_URL = "https://raw.githubusercontent.com/prashantkodali/HashSet/master/datasets/hashset/HashSet-Manual.csv"
class HashSetManual(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"),
"spans": datasets.Sequence(
{
"start": datasets.Value("int32"),
"end": datasets.Value("int32"),
"text": datasets.Value("string")
}
),
"source": datasets.Value("string"),
"gold_position": datasets.Value("int32"),
"mix": datasets.Value("bool"),
"other": datasets.Value("bool"),
"ner": datasets.Value("bool"),
"annotator_id": datasets.Value("int32"),
"annotation_id": datasets.Value("int32"),
"created_at": datasets.Value("timestamp[us]"),
"updated_at": datasets.Value("timestamp[us]"),
"lead_time": datasets.Value("float64"),
"rank": datasets.Sequence(
{
"position": datasets.Value("int32"),
"candidate": 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):
def read_language_labels(field):
mix_label = "Hashtag has a mix of english and hindi tokens"
other_label = "Hashtag has non english token "
ner_label = "Hashtag has named entities"
try:
record = json.loads(field)
except json.decoder.JSONDecodeError:
record = {"choices": [field]}
mix = False
other = False
ner = False
if mix_label in record["choices"]:
mix = True
if other_label in record["choices"]:
other = True
if ner_label in record["choices"]:
ner = True
return mix, other, ner
def read_entities(field):
try:
record = json.loads(field)
except json.decoder.JSONDecodeError:
return []
output = []
for row in record:
output.append({
"start": row.get("start", None),
"end": row.get("end", None),
"text": row.get("text", None)
})
return output
def read_rank(row):
output = []
for i in range(10):
output.append({
"position": str(i+1),
"candidate": row[str(i+1)]
})
return output
def get_gold_position(field):
output = field.strip("$")
try:
return int(output)
except ValueError:
return None
records = pd.read_csv(filepath).to_dict("records")
for idx, row in enumerate(records):
mix, other, ner = read_language_labels(row["mutlitoken"])
yield idx, {
"index": row["Unnamed: 0"],
"hashtag": row["Hashtag"],
"segmentation": row["Final Segmentation"],
"spans": read_entities(row["charner"]),
"source": row["Source"],
"gold_position": get_gold_position(row["topk"]),
"mix": mix,
"other": other,
"ner": ner,
"annotator_id": int(row["annotator"]),
"annotation_id": int(row["annotation_id"]),
"created_at": row["created_at"],
"updated_at": row["updated_at"],
"lead_time": row["lead_time"],
"rank": read_rank(row)
}
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