Datasets:

ruanchaves
/

hashset_manual

	"""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)
	}