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Upload vicon.py with huggingface_hub
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vicon.py
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# coding=utf-8
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# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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ViCon, comprises pairs of synonyms and antonymys across \
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noun, verb, and adjective classes, offerring data to \
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distinguish between similarity and dissimilarity.
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"""
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import os
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from pathlib import Path
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from typing import Dict, List, Tuple
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import datasets
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import pandas as pd
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from seacrowd.utils import schemas
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from seacrowd.utils.configs import SEACrowdConfig
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from seacrowd.utils.constants import Licenses, Tasks
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_CITATION = """\
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@inproceedings{nguyen-etal-2018-introducing,
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title = "Introducing Two {V}ietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness",
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author = "Nguyen, Kim Anh and
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Schulte im Walde, Sabine and
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Vu, Ngoc Thang",
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editor = "Walker, Marilyn and
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Ji, Heng and
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Stent, Amanda",
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booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
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month = jun,
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year = "2018",
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address = "New Orleans, Louisiana",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/N18-2032",
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doi = "10.18653/v1/N18-2032",
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pages = "199--205",
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}
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"""
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_DATASETNAME = "vicon"
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_DESCRIPTION = """\
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ViCon, comprises pairs of synonyms and antonymys across \
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noun, verb, and adjective classes, offerring data to \
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distinguish between similarity and dissimilarity.
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"""
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_HOMEPAGE = "https://www.ims.uni-stuttgart.de/forschung/ressourcen/experiment-daten/vnese-sem-datasets/"
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_LANGUAGES = ["vie"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
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_LICENSE = Licenses.CC_BY_NC_SA_2_0.value # example: Licenses.MIT.value, Licenses.CC_BY_NC_SA_4_0.value, Licenses.UNLICENSE.value, Licenses.UNKNOWN.value
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_LOCAL = False
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_URLS = {
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"noun": "https://www.ims.uni-stuttgart.de/documents/ressourcen/experiment-daten/ViData.zip",
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"adj": "https://www.ims.uni-stuttgart.de/documents/ressourcen/experiment-daten/ViData.zip",
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"verb": "https://www.ims.uni-stuttgart.de/documents/ressourcen/experiment-daten/ViData.zip",
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}
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# This task is more suitable for TEXTUAL_ENTAILMENT
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# because the labels (antonym, synonym) roughly correlates to (contradiction, entailment)
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_SUPPORTED_TASKS = [Tasks.TEXTUAL_ENTAILMENT]
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_SOURCE_VERSION = "1.0.0"
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_SEACROWD_VERSION = "2024.06.20"
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class ViConDataset(datasets.GeneratorBasedBuilder):
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"""
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ViCon, comprises pairs of synonyms and antonymys across \
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noun, verb, and adjective classes, offerring data to \
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distinguish between similarity and dissimilarity.
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"""
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POS_TAGS = ["noun", "adj", "verb"]
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
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BUILDER_CONFIGS = [SEACrowdConfig(name=f"{_DATASETNAME}_{POS_TAG}_source", version=_SOURCE_VERSION, description=f"{_DATASETNAME}_{POS_TAG} source schema", schema="source", subset_id=f"{_DATASETNAME}_{POS_TAG}",) for POS_TAG in POS_TAGS] + [
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SEACrowdConfig(
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name=f"{_DATASETNAME}_{POS_TAG}_seacrowd_pairs",
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version=_SEACROWD_VERSION,
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description=f"{_DATASETNAME}_{POS_TAG} SEACrowd schema",
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schema="seacrowd_pairs",
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subset_id=f"{_DATASETNAME}_{POS_TAG}",
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)
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for POS_TAG in POS_TAGS
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]
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_noun_source"
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def _info(self) -> datasets.DatasetInfo:
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if self.config.schema == "source":
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features = datasets.Features(
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{
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"Word1": datasets.Value("string"),
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"Word2": datasets.Value("string"),
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"Relation": datasets.Value("string"),
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}
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)
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elif self.config.schema == "seacrowd_pairs":
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features = schemas.pairs_features(["ANT", "SYN"])
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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"""Returns SplitGenerators."""
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POS_TAG = self.config.name.split("_")[1]
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if POS_TAG == "noun" or POS_TAG == "verb":
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number = 400
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elif POS_TAG == "adj":
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number = 600
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if POS_TAG in self.POS_TAGS:
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data_dir = dl_manager.download_and_extract(_URLS[POS_TAG])
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else:
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data_dir = [dl_manager.download_and_extract(_URLS[POS_TAG]) for POS_TAG in self.POS_TAGS]
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"filepath": os.path.join(data_dir, f"ViData/ViCon/{number}_{POS_TAG}_pairs.txt"),
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"split": "train",
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},
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)
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]
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def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
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"""Yields examples as (key, example) tuples."""
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with open(filepath, "r", encoding="utf-8") as file:
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lines = file.readlines()
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data = []
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for line in lines:
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columns = line.strip().split("\t")
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data.append(columns)
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+
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df = pd.DataFrame(data[1:], columns=data[0])
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+
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for index, row in df.iterrows():
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+
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if self.config.schema == "source":
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example = row.to_dict()
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+
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elif self.config.schema == "seacrowd_pairs":
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example = {
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"id": str(index),
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"text_1": str(row["Word1"]),
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"text_2": str(row["Word2"]),
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"label": str(row["Relation"]),
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}
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yield index, example
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