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""" |
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This template serves as a starting point for contributing a dataset to the SEACrowd Datahub repo. |
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Full documentation on writing dataset loading scripts can be found here: |
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https://huggingface.co/docs/datasets/add_dataset.html |
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To create a dataset loading script you will create a class and implement 3 methods: |
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* `_info`: Establishes the schema for the dataset, and returns a datasets.DatasetInfo object. |
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* `_split_generators`: Downloads and extracts data for each split (e.g. train/val/test) or associate local data with each split. |
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* `_generate_examples`: Creates examples from data on disk that conform to each schema defined in `_info`. |
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""" |
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import json |
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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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from seacrowd.utils import schemas |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils.constants import (DEFAULT_SEACROWD_VIEW_NAME, |
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DEFAULT_SOURCE_VIEW_NAME, Tasks) |
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_CITATION = """\ |
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@misc{MALINDO-parallel, |
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title = "MALINDO-parallel", |
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howpublished = "https://github.com/matbahasa/MALINDO_Parallel/blob/master/README.md", |
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note = "Accessed: 2023-01-27", |
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} |
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""" |
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_DATASETNAME = "malindo_parallel" |
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_DESCRIPTION = """\ |
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Teks ini adalah skrip video untuk Kampus Terbuka Universiti Bahasa Asing Tokyo pada tahun 2020. Tersedia parallel sentences dalam Bahasa Melayu/Indonesia dan Bahasa Jepang |
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""" |
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_HOMEPAGE = "https://github.com/matbahasa/MALINDO_Parallel/tree/master/OpenCampusTUFS" |
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_LANGUAGES = ["zlm", "jpn"] |
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_LICENSE = "Creative Commons Attribution 4.0 (cc-by-4.0)" |
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_LOCAL = False |
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_URLS = { |
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_DATASETNAME: "https://raw.githubusercontent.com/matbahasa/MALINDO_Parallel/master/OpenCampusTUFS/OCTUFS2020.txt", |
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} |
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_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class MalindoParallelDataset(datasets.GeneratorBasedBuilder): |
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"""Data terjemahan bahasa Melayu/Indonesia""" |
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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="malindo_parallel_source", |
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version=SOURCE_VERSION, |
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description="malindo_parallel source schema", |
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schema="source", |
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subset_id="malindo_parallel", |
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), |
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SEACrowdConfig( |
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name="malindo_parallel_seacrowd_t2t", |
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version=SEACROWD_VERSION, |
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description="malindo_parallel SEACrowd schema", |
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schema="seacrowd_t2t", |
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subset_id="malindo_parallel", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "malindo_parallel_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({"id": datasets.Value("string"), "text": datasets.Value("string")}) |
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elif self.config.schema == "seacrowd_t2t": |
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features = schemas.text2text_features |
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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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data_dir = dl_manager.download_and_extract(urls) |
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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": data_dir, |
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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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rows = [] |
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temp_cols = None |
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with open(filepath) as file: |
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while line := file.readline(): |
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if temp_cols is None: |
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cols = [] |
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for col in line.split('\t'): |
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if len(col.strip('\n'))>0: |
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cols.append(col) |
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if len(cols) > 2: |
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correct_line = line.rstrip() |
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rows.append(correct_line) |
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else: |
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temp_cols = cols |
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else: |
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temp_cols.append(line) |
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correct_line = "\t".join(temp_cols).rstrip() |
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temp_cols = None |
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rows.append(correct_line) |
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if self.config.schema == "source": |
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for i, row in enumerate(rows): |
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t1idx = row.find("\t") + 1 |
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t2idx = row[t1idx:].find("\t") |
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row_id = row[:t1idx] |
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row_melayu = row[t1idx : t1idx + t2idx] |
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row_japanese = row[t1idx + t2idx + 1 : -1] |
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ex = {"id": row_id.rstrip(), |
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"text": row_melayu + "\t" + row_japanese} |
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yield i, ex |
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elif self.config.schema == "seacrowd_t2t": |
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for i, row in enumerate(rows): |
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t1idx = row.find("\t") + 1 |
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t2idx = row[t1idx:].find("\t") |
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row_id = row[:t1idx] |
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row_melayu = row[t1idx : t1idx + t2idx] |
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row_japanese = row[t1idx + t2idx + 1 : -1] |
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ex = { |
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"id": row_id.rstrip(), |
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"text_1": row_melayu, |
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"text_2": row_japanese, |
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"text_1_name": "zlm", |
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"text_2_name": "jpn", |
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} |
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yield i, ex |
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