| """Test-Stanford dataset by Bansal et al.."""
|
|
|
| import datasets
|
| import pandas as pd
|
|
|
| _CITATION = """
|
| @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}
|
| }
|
| """
|
|
|
| _DESCRIPTION = """
|
| Manually Annotated Stanford Sentiment Analysis Dataset by Bansal et al..
|
| """
|
| _URLS = {
|
| "test": "https://raw.githubusercontent.com/ruanchaves/hashformers/master/datasets/Test-Stanford.txt"
|
| }
|
|
|
| class TestStanford(datasets.GeneratorBasedBuilder):
|
|
|
| VERSION = datasets.Version("1.0.0")
|
|
|
| def _info(self):
|
| 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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| 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(_URLS)
|
| return [
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| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"] }),
|
| ]
|
|
|
| def _generate_examples(self, filepath):
|
|
|
| names = ["id","hashtag","candidate", "label"]
|
| df = pd.read_csv(filepath, sep="\t", skiprows=1, header=None,
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| names=names)
|
|
|
| for col in names[0:-1]:
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| df[col] = df[col].apply(lambda x: x.strip("'").strip())
|
|
|
| records = df.to_dict('records')
|
|
|
| output = []
|
|
|
| current_hashtag = None
|
| hashtag = None
|
| candidates = []
|
| ids = []
|
| label = []
|
|
|
|
|
| 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
|
| }
|
|
|
| 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:
|
| candidates.append(row["candidate"])
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| label.append(int(row["label"]))
|
|
|
| def get_gold_position(row):
|
| try:
|
| return row["label"].index(1)
|
| except ValueError:
|
| return None
|
|
|
| def get_rank(row):
|
| return [{
|
| "position": idx + 1,
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| "candidate": item
|
| } for idx, item in enumerate(row["candidate"])]
|
|
|
| def get_segmentation(row):
|
| try:
|
| gold_idx = row["label"].index(1)
|
| return row["candidate"][gold_idx]
|
| except ValueError:
|
| return None
|
|
|
| for idx, row in enumerate(output):
|
| yield idx, {
|
| "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)
|
| } |
