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"""Test-Stanford dataset by Bansal et al.."""
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import datasets
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import pandas as pd
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_CITATION = """
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@misc{bansal2015deep,
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title={Towards Deep Semantic Analysis Of Hashtags},
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author={Piyush Bansal and Romil Bansal and Vasudeva Varma},
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year={2015},
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eprint={1501.03210},
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archivePrefix={arXiv},
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primaryClass={cs.IR}
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}
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"""
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_DESCRIPTION = """
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Manually Annotated Stanford Sentiment Analysis Dataset by Bansal et al..
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"""
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_URLS = {
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"test": "https://raw.githubusercontent.com/ruanchaves/hashformers/master/datasets/Test-Stanford.txt"
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}
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class TestStanford(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.0.0")
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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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"gold_position": datasets.Value("int32"),
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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="",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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downloaded_files = dl_manager.download(_URLS)
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return [
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"] }),
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]
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def _generate_examples(self, filepath):
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names = ["id","hashtag","candidate", "label"]
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df = pd.read_csv(filepath, sep="\t", skiprows=1, header=None,
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names=names)
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for col in names[0:-1]:
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df[col] = df[col].apply(lambda x: x.strip("'").strip())
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records = df.to_dict('records')
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output = []
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current_hashtag = None
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hashtag = None
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candidates = []
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ids = []
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label = []
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for row in records:
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hashtag = row["hashtag"]
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if current_hashtag != hashtag:
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new_row = {
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"hashtag": current_hashtag,
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"candidate": candidates,
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"id": ids,
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"label": label
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}
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if current_hashtag:
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output.append(new_row)
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current_hashtag = row['hashtag']
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candidates = [row["candidate"]]
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ids = int(row["id"])
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label = [int(row["label"])]
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else:
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candidates.append(row["candidate"])
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label.append(int(row["label"]))
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def get_gold_position(row):
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try:
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return row["label"].index(1)
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except ValueError:
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return None
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def get_rank(row):
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return [{
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"position": idx + 1,
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"candidate": item
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} for idx, item in enumerate(row["candidate"])]
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def get_segmentation(row):
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try:
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gold_idx = row["label"].index(1)
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return row["candidate"][gold_idx]
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except ValueError:
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return None
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for idx, row in enumerate(output):
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yield idx, {
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"index": int(row["id"]),
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"hashtag": row["hashtag"],
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"segmentation": get_segmentation(row),
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"gold_position": get_gold_position(row),
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"rank": get_rank(row)
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} |
