Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
unknown
Language Creators:
machine-generated
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
License:
| """Test-Stanford dataset by Bansal et al..""" | |
| import datasets | |
| import pandas as pd | |
| _CITATION = """ | |
| @misc{bansal2015deep, | |
| title={Towards Deep Semantic Analysis Of Hashtags}, | |
| author={Piyush Bansal and Romil Bansal and Vasudeva Varma}, | |
| year={2015}, | |
| eprint={1501.03210}, | |
| archivePrefix={arXiv}, | |
| 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( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "index": datasets.Value("int32"), | |
| "hashtag": datasets.Value("string"), | |
| "segmentation": datasets.Value("string"), | |
| "gold_position": datasets.Value("int32"), | |
| "rank": datasets.Sequence( | |
| { | |
| "position": datasets.Value("int32"), | |
| "candidate": datasets.Value("string") | |
| } | |
| ) | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage="", | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| downloaded_files = dl_manager.download(_URLS) | |
| return [ | |
| 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, | |
| names=names) | |
| for col in names[0:-1]: | |
| 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: | |
| hashtag = row["hashtag"] | |
| if current_hashtag != hashtag: | |
| new_row = { | |
| "hashtag": current_hashtag, | |
| "candidate": candidates, | |
| "id": ids, | |
| "label": label | |
| } | |
| if current_hashtag: | |
| output.append(new_row) | |
| current_hashtag = row['hashtag'] | |
| candidates = [row["candidate"]] | |
| ids = int(row["id"]) | |
| label = [int(row["label"])] | |
| else: | |
| candidates.append(row["candidate"]) | |
| 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, | |
| "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"]), | |
| "hashtag": row["hashtag"], | |
| "segmentation": get_segmentation(row), | |
| "gold_position": get_gold_position(row), | |
| "rank": get_rank(row) | |
| } |