| """BT11"""
|
|
|
| import datasets
|
| import pandas as pd
|
| from collections import deque
|
|
|
| _CITATION = """
|
| @inproceedings{li2018helpful,
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| title={Helpful or Not? An investigation on the feasibility of identifier splitting via CNN-BiLSTM-CRF.},
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| author={Li, Jiechu and Du, Qingfeng and Shi, Kun and He, Yu and Wang, Xin and Xu, Jincheng},
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| booktitle={SEKE},
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| pages={175--174},
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| year={2018}
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| }
|
| """
|
|
|
| _DESCRIPTION = """
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| In programming languages, identifiers are tokens (also called symbols) which name language entities.
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| Some of the kinds of entities an identifier might denote include variables, types, labels, subroutines, and packages.
|
|
|
| BT11 is a dataset for identifier segmentation,
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| i.e. the task of adding spaces between the words on a identifier.
|
| """
|
| _URL = "https://raw.githubusercontent.com/ruanchaves/hashformers/master/datasets/bt11.csv"
|
|
|
| class BT11(datasets.GeneratorBasedBuilder):
|
|
|
| VERSION = datasets.Version("1.0.0")
|
|
|
| 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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| "identifier": datasets.Value("string"),
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| "segmentation": datasets.Value("string")
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| }
|
| ),
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| supervised_keys=None,
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| homepage="",
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| citation=_CITATION,
|
| )
|
|
|
| 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}),
|
| ]
|
|
|
| def _generate_examples(self, filepath):
|
|
|
| def get_segmentation(needle, haystack, sep="-"):
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| output = haystack
|
| needle = needle.lower()
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| haystack = haystack.lower()
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| counter = 0
|
| pos = deque()
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| iterator = iter(haystack)
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| for char in needle:
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| if char == sep:
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| pos.appendleft(counter)
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| continue
|
| while True:
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| try:
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| next_char = next(iterator)
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| counter += 1
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| if next_char == char:
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| break
|
| except StopIteration:
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| break
|
| while pos:
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| next_pos = pos.popleft()
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| output = output[:next_pos] + " " + output[next_pos:]
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| return output
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|
|
| df = pd.read_csv(filepath, header=None)[[0,1]]
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| df = df.dropna()
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| records = df.to_dict("records")
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|
|
| for idx, item in enumerate(records):
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| yield idx, {
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| "index": idx,
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| "identifier": item[0],
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| "segmentation": get_segmentation(item[1], item[0])
|
| } |
