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import json |
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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 jsonlines |
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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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@article{, |
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author = {Audah, Hanif Arkan and Yuliawati, Arlisa and Alfina, Ika}, |
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title = {A Comparison Between SymSpell and a Combination of Damerau-Levenshtein Distance With the Trie Data Structure}, |
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journal = {2023 10th International Conference on Advanced Informatics: Concept, Theory and Application (ICAICTA)}, |
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volume = {}, |
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year = {2023}, |
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url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10390399&casa_token=HtJUCIGGlWYAAAAA:q8ll1RWmpHtSAq2Qp5uQAE1NJETx7tUYFZIvTO1IWoaYy4eqFETSsm9p6C7tJwLZBGq5y8zc3A&tag=1}, |
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doi = {}, |
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biburl = {https://github.com/ir-nlp-csui/saltik?tab=readme-ov-file#references}, |
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bibsource = {https://github.com/ir-nlp-csui/saltik?tab=readme-ov-file#references} |
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} |
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""" |
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_DATASETNAME = "saltik" |
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_DESCRIPTION = """\ |
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Saltik is a dataset for benchmarking non-word error correction method accuracy in evaluating Indonesian words. |
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It consists of 58,532 non-word errors generated from 3,000 of the most popular Indonesian words. |
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""" |
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_HOMEPAGE = "https://github.com/ir-nlp-csui/saltik" |
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_LANGUAGES = ["ind"] |
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_LICENSE = Licenses.AGPL_3_0.value |
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_LOCAL = False |
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_URLS = { |
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_DATASETNAME: "https://raw.githubusercontent.com/ir-nlp-csui/saltik/main/saltik.json", |
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} |
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_SUPPORTED_TASKS = [] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class Saltik(datasets.GeneratorBasedBuilder): |
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"""It consists of 58,532 non-word errors generated from 3,000 of the most popular Indonesian words.""" |
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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 = [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_source", |
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version=SOURCE_VERSION, |
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description=f"{_DATASETNAME} source schema", |
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schema="source", |
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subset_id=f"{_DATASETNAME}", |
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), |
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] |
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_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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"id": datasets.Value("string"), |
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"word": datasets.Value("string"), |
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"errors": [ |
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{ |
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"typo": datasets.Value("string"), |
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"error_type": datasets.Value("string"), |
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} |
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], |
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} |
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) |
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else: |
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raise NotImplementedError() |
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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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urls = _URLS[_DATASETNAME] |
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file_path = dl_manager.download(urls) |
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data = self._read_jsonl(file_path) |
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all_words = list(data.keys()) |
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processed_data = [] |
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id = 0 |
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for word in all_words: |
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processed_data.append({"id": id, "word": word, "errors": data[word]}) |
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id += 1 |
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self._write_jsonl(file_path + ".jsonl", processed_data) |
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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": file_path + ".jsonl", |
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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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if self.config.schema == "source": |
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i = 0 |
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with jsonlines.open(filepath) as f: |
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for each_data in f.iter(): |
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ex = { |
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"id": each_data["id"], |
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"word": each_data["word"], |
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"errors": each_data["errors"], |
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} |
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yield i, ex |
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i += 1 |
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def _read_jsonl(self, filepath: Path): |
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with open(filepath) as user_file: |
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parsed_json = json.load(user_file) |
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return parsed_json |
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def _write_jsonl(self, filepath, values): |
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with jsonlines.open(filepath, "w") as writer: |
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for line in values: |
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writer.write(line) |
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