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Upload bloom_vist.py with huggingface_hub
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bloom_vist.py
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1 |
+
"""
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2 |
+
SEA Crowd Data Loader for Bloom VIST.
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3 |
+
"""
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4 |
+
from typing import Dict, List, Tuple
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5 |
+
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+
import datasets
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+
from datasets.download.download_manager import DownloadManager
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8 |
+
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+
from seacrowd.utils import schemas
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10 |
+
from seacrowd.utils.configs import SEACrowdConfig
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+
from seacrowd.utils.constants import TASK_TO_SCHEMA, Licenses, Tasks
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+
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_CITATION = r"""
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14 |
+
@inproceedings{leong-etal-2022-bloom,
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+
title = "Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks",
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+
author = "Leong, Colin and
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+
Nemecek, Joshua and
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+
Mansdorfer, Jacob and
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+
Filighera, Anna and
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Owodunni, Abraham and
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Whitenack, Daniel",
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editor = "Goldberg, Yoav and
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Kozareva, Zornitsa and
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+
Zhang, Yue",
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booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
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month = dec,
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year = "2022",
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address = "Abu Dhabi, United Arab Emirates",
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publisher = "Association for Computational Linguistics",
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+
url = "https://aclanthology.org/2022.emnlp-main.590",
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doi = "10.18653/v1/2022.emnlp-main.590",
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pages = "8608--8621",
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}
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+
"""
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+
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logger = datasets.logging.get_logger(__name__)
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+
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# this config is created for SEACrowd Dataloader
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_LANG_CONFIG = {
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40 |
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"abc": "Ambala Ayta",
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"ahk": "Akha",
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"bfn": "Bunak",
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"bjn": "Banjar",
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"bkx": "Baikeno",
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"brb": "Brao",
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"brv": "Western Bru",
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"bya": "Batak",
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"bzi": "Bisu",
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"ceb": "Cebuano",
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"cgc": "Kagayanen",
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"cmo": "Central Mnong",
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"ddg": "Fataluku",
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"dmg": "Upper Kinabatangan",
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"dnw": "Western Dani",
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"dtp": "Kadazan Dusun",
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"enc": "En",
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"fil": "Filipino",
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"hil": "Hiligaynon",
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"hro": "Haroi",
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"idt": "Idaté",
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"ilo": "Ilocano",
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"ind": "Indonesian",
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"jra": "Jarai",
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"kak": "Kalanguya",
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"khb": "Lü",
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"khm": "Khmer",
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"kqr": "Kimaragang",
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"krr": "Krung",
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"ksw": "S’gaw Karen",
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"lhu": "Lahu",
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"lsi": "Lacid",
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"lwl": "Eastern Lawa",
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"mdr": "Mandar",
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"mgm": "Mambae",
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"mhx": "Lhao Vo",
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"mkz": "Makasae",
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"mry": "Mandaya",
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"msb": "Masbatenyo",
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"mya": "Burmese",
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"nod": "Northern Thai",
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"nxa": "Nauete",
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"nxl": "South Nuaulu",
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"pag": "Pangasinan",
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"pce": "Ruching Palaung",
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"pea": "Peranakan Indonesian",
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"pmf": "Pamona",
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"psp": "Filipino Sign Language",
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"sea": "Semai",
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"sgd": "Surigaonon",
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"sml": "Central Sama",
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"snl": "Sangil",
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"tdt": "Tetun Dili",
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"tet": "Tetun",
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"tha": "Thai",
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"tkd": "Tukudede",
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"tpu": "Tampuan",
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"war": "Waray-Waray",
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"wms": "Wambon",
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"yet": "Yetfa",
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"yin": "Riang Lai",
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"zlm": "Malay",
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}
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_LOCAL = False
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_LANGUAGES = list(_LANG_CONFIG.keys())
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+
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+
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_DATASETNAME = "bloom_vist"
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_DESCRIPTION = r"""
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BLOOM VIST is a visual storytelling of books that consists of 62 languages indigenous to SEA.
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This dataset is owned by Bloom, a free, open-source software developed by SIL International and associated with Bloom Library, app, and services.
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This dataset is released with the LICENSE family of Creative Commons (although each story datapoints has its licensing in more detail,
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e.g cc-by, cc-by-nc, cc-by-nd, cc-by-sa, cc-by-nc-nd, cc-by-nc-sa).
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+
Before using this dataloader, please accept the acknowledgement at https://huggingface.co/datasets/sil-ai/bloom-vist and use huggingface-cli login for authentication.
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+
"""
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+
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_HOMEPAGE = "https://huggingface.co/datasets/sil-ai/bloom-vist"
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_LICENSE = Licenses.CC.value
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+
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_URL = "https://huggingface.co/datasets/sil-ai/bloom-vist"
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_HF_REMOTE_REF = "/".join(_URL.split("/")[-2:])
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_SUPPORTED_TASKS = [Tasks.IMAGE_CAPTIONING]
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_SOURCE_VERSION = "0.1.0"
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_SEACROWD_VERSION = "2024.06.20"
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CONFIG_SUFFIXES_FOR_TASK = [TASK_TO_SCHEMA.get(task).lower() for task in _SUPPORTED_TASKS]
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+
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+
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def conform_init_config():
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"""Assertion Function for Instantiated Configs"""
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if len(_LANGUAGES) == 0:
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raise AssertionError("No Languages detected from config!")
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if len(CONFIG_SUFFIXES_FOR_TASK) != len(_SUPPORTED_TASKS):
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raise AssertionError("Config prefixes don't matched in terms of `len` with `_SUPPORTED_TASKS`!")
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if len(CONFIG_SUFFIXES_FOR_TASK) == 0:
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raise AssertionError("Config prefixes and `_SUPPORTED_TASKS` have `len` of 0!")
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+
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+
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conform_init_config()
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+
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+
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def construct_configs_on_langs(languages: list = None) -> List[SEACrowdConfig]:
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"""
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145 |
+
The function `construct_configs` constructs a list of SEACrowdConfig objects based on the provided
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languages or a default language, and returns the list.
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+
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input:
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+
languages (list, default None): The `languages` parameter is a list that specifies the languages for which the
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+
configurations need to be constructed. If no languages are provided (value=None), the first value in language config
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will be used.
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output:
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+
a list of `SEACrowdConfig` objects based on instantiated init variables
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+
"""
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+
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# set output var
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config_list = []
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+
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# construct zipped arg for config instantiation
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+
TASKS_AND_CONFIG_SUFFIX_PAIRS = list(zip(_SUPPORTED_TASKS, CONFIG_SUFFIXES_FOR_TASK))
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+
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# implement source schema
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version, config_name_prefix = _SOURCE_VERSION, "source"
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+
config_list += [
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165 |
+
SEACrowdConfig(
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+
name=f"{_DATASETNAME}_{_LANG}_{config_name_prefix}",
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167 |
+
version=datasets.Version(version),
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168 |
+
description=f"{_DATASETNAME} {config_name_prefix} schema for language code {_LANG}",
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169 |
+
schema=f"{config_name_prefix}",
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+
subset_id=_LANG,
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+
)
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+
for _LANG in languages
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+
]
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+
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+
# implement SEACrowd schema
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+
version, config_name_prefix = _SEACROWD_VERSION, "seacrowd"
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+
for task_obj, config_name_suffix in TASKS_AND_CONFIG_SUFFIX_PAIRS:
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+
config_list += [
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179 |
+
SEACrowdConfig(
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180 |
+
name=f"{_DATASETNAME}_{_LANG}_{config_name_prefix}_{config_name_suffix}",
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181 |
+
version=datasets.Version(version),
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182 |
+
description=f"{_DATASETNAME} {config_name_prefix} schema for {task_obj.name} and language code {_LANG}",
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183 |
+
schema=f"{config_name_prefix}_{config_name_suffix}",
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184 |
+
subset_id=_LANG,
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+
)
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+
for _LANG in languages
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187 |
+
]
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188 |
+
return config_list
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189 |
+
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190 |
+
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191 |
+
class BloomVISTDataset(datasets.GeneratorBasedBuilder):
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192 |
+
"""Bloom VIST dataset, subsetted from https://huggingface.co/datasets/sil-ai/bloom-vist"""
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193 |
+
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194 |
+
# get all schema w/o lang arg + get all schema w/ lang arg
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195 |
+
BUILDER_CONFIGS = construct_configs_on_langs(_LANGUAGES)
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196 |
+
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197 |
+
def _info(self) -> datasets.DatasetInfo:
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198 |
+
_config_schema_name = self.config.schema
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199 |
+
logger.info(f"Received schema name: {self.config.schema}")
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200 |
+
# source schema
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201 |
+
if _config_schema_name == "source":
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202 |
+
features = datasets.Features(
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203 |
+
{
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204 |
+
"title": datasets.Value("string"),
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205 |
+
"license": datasets.Value("string"),
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206 |
+
"album_id": datasets.Value("string"),
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207 |
+
"story": datasets.Sequence(
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208 |
+
feature={"image_id": datasets.Value("string"), "image_url": datasets.Value("string"), "story_index": datasets.Value("int32"), "story_id": datasets.Value("string"), "text": datasets.Value("string")}, length=-1, id=None
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209 |
+
),
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210 |
+
}
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211 |
+
)
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212 |
+
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213 |
+
# image-text schema
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214 |
+
elif _config_schema_name == "seacrowd_imtext":
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215 |
+
features = schemas.image_text_features()
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216 |
+
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217 |
+
else:
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218 |
+
raise ValueError(f"Received unexpected config schema of {_config_schema_name}!")
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219 |
+
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220 |
+
return datasets.DatasetInfo(
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221 |
+
description=_DESCRIPTION,
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222 |
+
features=features,
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223 |
+
homepage=_HOMEPAGE,
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224 |
+
license=_LICENSE,
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225 |
+
citation=_CITATION,
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226 |
+
)
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227 |
+
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228 |
+
def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]:
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229 |
+
hf_dset_dict = datasets.load_dataset(_HF_REMOTE_REF, self.config.subset_id)
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230 |
+
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231 |
+
return [datasets.SplitGenerator(name=datasets.Split(dset_key), gen_kwargs={"hf_dset": dset}) for dset_key, dset in hf_dset_dict.items() if dset.num_rows > 0]
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232 |
+
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233 |
+
def _generate_examples(self, hf_dset) -> Tuple[int, Dict]:
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234 |
+
_config_schema_name = self.config.schema
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235 |
+
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236 |
+
_idx = 0
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237 |
+
for datapoints in hf_dset:
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238 |
+
# for source schema, the `_idx` will be taken from "album_id" value
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239 |
+
if _config_schema_name == "source":
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240 |
+
yield datapoints["album_id"], {colname: datapoints[colname] for colname in self.info.features}
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241 |
+
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242 |
+
# for seacrowd schema, the `_idx` will be created manually
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243 |
+
# since one album_id has multiple pairs of image-text
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244 |
+
elif _config_schema_name == "seacrowd_imtext":
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245 |
+
# check the len of the features in sequenced columns
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246 |
+
# since in source hf there's no validation on data integrity
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247 |
+
_len_vars = []
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248 |
+
_ftrs_in_seq = ("image_id", "image_url", "story_index", "story_id", "text")
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249 |
+
story_data = datapoints["story"]
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250 |
+
for ftr in _ftrs_in_seq:
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251 |
+
_len_vars.append(len(story_data[ftr]))
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252 |
+
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253 |
+
# skip story w/ mismatched infos
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254 |
+
if max(_len_vars) != min(_len_vars):
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255 |
+
continue
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256 |
+
|
257 |
+
for num_data in range(max(_len_vars)):
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258 |
+
yield _idx, {"id": _idx, "image_paths": [story_data["image_url"][num_data]], "texts": story_data["text"][num_data], "metadata": {"context": datapoints["title"], "labels": []}}
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259 |
+
_idx += 1
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260 |
+
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261 |
+
else:
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262 |
+
raise ValueError(f"Received unexpected config schema of {_config_schema_name}!")
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