| """HashSet dataset."""
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|
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| import datasets
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| import pandas as pd
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| import json
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|
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| _CITATION = """
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| @article{kodali2022hashset,
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| title={HashSet--A Dataset For Hashtag Segmentation},
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| author={Kodali, Prashant and Bhatnagar, Akshala and Ahuja, Naman and Shrivastava, Manish and Kumaraguru, Ponnurangam},
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| journal={arXiv preprint arXiv:2201.06741},
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| year={2022}
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| }
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| """
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|
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| _DESCRIPTION = """
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| Hashset is a new dataset consisiting on 1.9k manually annotated and 3.3M loosely supervised tweets for testing the
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| efficiency of hashtag segmentation models. We compare State of The Art Hashtag Segmentation models on Hashset and other
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| baseline datasets (STAN and BOUN). We compare and analyse the results across the datasets to argue that HashSet can act
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| as a good benchmark for hashtag segmentation tasks.
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|
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| HashSet Manual: contains 1.9k manually annotated hashtags. Each row consists of the hashtag, segmented
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| hashtag ,named entity annotations, a list storing whether the hashtag contains mix of hindi and english
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| tokens and/or contains non-english tokens.
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| """
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| _URL = "https://raw.githubusercontent.com/prashantkodali/HashSet/master/datasets/hashset/HashSet-Manual.csv"
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|
|
| class HashSetManual(datasets.GeneratorBasedBuilder):
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|
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| VERSION = datasets.Version("1.0.0")
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|
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| def _info(self):
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| return datasets.DatasetInfo(
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| description=_DESCRIPTION,
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| features=datasets.Features(
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| {
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| "index": datasets.Value("int32"),
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| "hashtag": datasets.Value("string"),
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| "segmentation": datasets.Value("string"),
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| "spans": datasets.Sequence(
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| {
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| "start": datasets.Value("int32"),
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| "end": datasets.Value("int32"),
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| "text": datasets.Value("string")
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| }
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| ),
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| "source": datasets.Value("string"),
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| "gold_position": datasets.Value("int32"),
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| "mix": datasets.Value("bool"),
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| "other": datasets.Value("bool"),
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| "ner": datasets.Value("bool"),
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| "annotator_id": datasets.Value("int32"),
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| "annotation_id": datasets.Value("int32"),
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| "created_at": datasets.Value("timestamp[us]"),
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| "updated_at": datasets.Value("timestamp[us]"),
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| "lead_time": datasets.Value("float64"),
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| "rank": datasets.Sequence(
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| {
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| "position": datasets.Value("int32"),
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| "candidate": datasets.Value("string")
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| }
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| )
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| }
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| ),
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| supervised_keys=None,
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| homepage="https://github.com/prashantkodali/HashSet/",
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| citation=_CITATION,
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| )
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|
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| def _split_generators(self, dl_manager):
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| downloaded_files = dl_manager.download(_URL)
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| return [
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| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files }),
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| ]
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|
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| def _generate_examples(self, filepath):
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|
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| def read_language_labels(field):
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| mix_label = "Hashtag has a mix of english and hindi tokens"
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| other_label = "Hashtag has non english token "
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| ner_label = "Hashtag has named entities"
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| try:
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| record = json.loads(field)
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| except json.decoder.JSONDecodeError:
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| record = {"choices": [field]}
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|
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| mix = False
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| other = False
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| ner = False
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| if mix_label in record["choices"]:
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| mix = True
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| if other_label in record["choices"]:
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| other = True
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| if ner_label in record["choices"]:
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| ner = True
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| return mix, other, ner
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|
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| def read_entities(field):
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| try:
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| record = json.loads(field)
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| except json.decoder.JSONDecodeError:
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| return []
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| output = []
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| for row in record:
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| output.append({
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| "start": row.get("start", None),
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| "end": row.get("end", None),
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| "text": row.get("text", None)
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| })
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| return output
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|
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| def read_rank(row):
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| output = []
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| for i in range(10):
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| output.append({
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| "position": str(i+1),
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| "candidate": row[str(i+1)]
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| })
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| return output
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|
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| def get_gold_position(field):
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| output = field.strip("$")
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| try:
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| return int(output)
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| except ValueError:
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| return None
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|
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| records = pd.read_csv(filepath).to_dict("records")
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| for idx, row in enumerate(records):
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| mix, other, ner = read_language_labels(row["mutlitoken"])
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| yield idx, {
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| "index": row["Unnamed: 0"],
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| "hashtag": row["Hashtag"],
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| "segmentation": row["Final Segmentation"],
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| "spans": read_entities(row["charner"]),
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| "source": row["Source"],
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| "gold_position": get_gold_position(row["topk"]),
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| "mix": mix,
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| "other": other,
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| "ner": ner,
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| "annotator_id": int(row["annotator"]),
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| "annotation_id": int(row["annotation_id"]),
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| "created_at": row["created_at"],
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| "updated_at": row["updated_at"],
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| "lead_time": row["lead_time"],
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| "rank": read_rank(row)
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| }
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|
|
