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"""
SEA Crowd Data Loader for Bloom Captioning.
"""
from typing import Dict, List, Tuple
import datasets
from datasets.download.download_manager import DownloadManager
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import TASK_TO_SCHEMA, Licenses, Tasks
_CITATION = r"""
@inproceedings{leong-etal-2022-bloom,
title = "Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks",
author = "Leong, Colin and
Nemecek, Joshua and
Mansdorfer, Jacob and
Filighera, Anna and
Owodunni, Abraham and
Whitenack, Daniel",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.590",
doi = "10.18653/v1/2022.emnlp-main.590",
pages = "8608--8621",
}
"""
logger = datasets.logging.get_logger(__name__)
# this config is created for SEACrowd Dataloader
_LANG_CONFIG = {
"abc": "Ambala Ayta",
"ahk": "Akha",
"bfn": "Bunak",
"bjn": "Banjar",
"bkx": "Baikeno",
"brb": "Brao",
"brv": "Western Bru",
"bya": "Batak",
"bzi": "Bisu",
"ceb": "Cebuano",
"cgc": "Kagayanen",
"cmo": "Central Mnong",
"ddg": "Fataluku",
"dmg": "Upper Kinabatangan",
"dnw": "Western Dani",
"dtp": "Kadazan Dusun",
"dtr": "Lotud",
"enc": "En",
"fil": "Filipino",
"gal": "Galolen",
"hil": "Hiligaynon",
"hre": "Hre",
"hro": "Haroi",
"idt": "Idaté",
"ilo": "Ilocano",
"ind": "Indonesian",
"jra": "Jarai",
"kak": "Kalanguya",
"khb": "Lü",
"khm": "Khmer",
"kqr": "Kimaragang",
"krr": "Krung",
"ksw": "S’gaw Karen",
"lhu": "Lahu",
"llg": "Lole",
"lsi": "Lacid",
"lwl": "Eastern Lawa",
"mdr": "Mandar",
"mgm": "Mambae",
"mhx": "Lhao Vo",
"mkz": "Makasae",
"mnw": "Mon",
"mqj": "Mamasa",
"mry": "Mandaya",
"msb": "Masbatenyo",
"mya": "Burmese",
"nod": "Northern Thai",
"nst": "Tangshang Naga",
"nxa": "Nauete",
"nxl": "South Nuaulu",
"pag": "Pangasinan",
"pce": "Ruching Palaung",
"pdu": "Kayan",
"pea": "Peranakan Indonesian",
"pmf": "Pamona",
"sea": "Semai",
"sgd": "Surigaonon",
"shn": "Shan",
"sml": "Central Sama",
"snl": "Sangil",
"tdt": "Tetun Dili",
"tet": "Tetun",
"tha": "Thai",
"tkd": "Tukudede",
"tnt": "Tontemboan",
"tom": "Tombulu",
"tpu": "Tampuan",
"vie": "Vietnamese",
"war": "Waray-Waray",
"wms": "Wambon",
"wnk": "Wanukaka",
"xmm": "Manado Malay",
"yet": "Yetfa",
"zlm": "Malay",
}
_LOCAL = False
_LANGUAGES = list(_LANG_CONFIG.keys())
_DATASETNAME = "bloom_captioning"
_DESCRIPTION = r"""
This is a Bloom Library dataset developed for the image captioning task.
It covers 74 languages indigenous to SEA overall, amounting to total data of 21K.
This dataset belongs to a CC license, where its datapoints has specific license attached to it.
Before using this dataloader, please accept the acknowledgement at https://huggingface.co/datasets/sil-ai/bloom-captioning and use huggingface-cli login for authentication.
"""
_HOMEPAGE = "https://huggingface.co/datasets/sil-ai/bloom-captioning"
_LICENSE = Licenses.CC.value
_URL = "https://huggingface.co/datasets/sil-ai/bloom-captioning"
_HF_REMOTE_REF = "/".join(_URL.split("/")[-2:])
_SUPPORTED_TASKS = [Tasks.IMAGE_CAPTIONING]
_SOURCE_VERSION = "0.1.0"
_SEACROWD_VERSION = "2024.06.20"
CONFIG_SUFFIXES_FOR_TASK = [TASK_TO_SCHEMA.get(task).lower() for task in _SUPPORTED_TASKS]
def construct_configs_on_langs(languages: list = None) -> List[SEACrowdConfig]:
"""
The function `construct_configs` constructs a list of SEACrowdConfig objects based on the provided
languages or a default language, and returns the list.
input:
languages (list, default None): The `languages` parameter is a list that specifies the languages for which the
configurations need to be constructed. If no languages are provided (value=None), the first value in language config
will be used.
output:
a list of `SEACrowdConfig` objects based on instantiated init variables
"""
# set output var
config_list = []
# construct zipped arg for config instantiation
TASKS_AND_CONFIG_SUFFIX_PAIRS = list(zip(_SUPPORTED_TASKS, CONFIG_SUFFIXES_FOR_TASK))
# implement source schema
version, config_name_prefix = _SOURCE_VERSION, "source"
config_list += [
SEACrowdConfig(
name=f"{_DATASETNAME}_{_LANG}_{config_name_prefix}",
version=datasets.Version(version),
description=f"{_DATASETNAME} {config_name_prefix} schema for language code {_LANG}",
schema=f"{config_name_prefix}",
subset_id=_LANG,
)
for _LANG in languages
]
# implement SEACrowd schema
version, config_name_prefix = _SEACROWD_VERSION, "seacrowd"
for task_obj, config_name_suffix in TASKS_AND_CONFIG_SUFFIX_PAIRS:
config_list += [
SEACrowdConfig(
name=f"{_DATASETNAME}_{_LANG}_{config_name_prefix}_{config_name_suffix}",
version=datasets.Version(version),
description=f"{_DATASETNAME} {config_name_prefix} schema for {task_obj.name} and language code {_LANG}",
schema=f"{config_name_prefix}_{config_name_suffix}",
subset_id=_LANG,
)
for _LANG in languages
]
return config_list
class BloomCaptioningDataset(datasets.GeneratorBasedBuilder):
"""Bloom Captioning dataset, subsetted from https://huggingface.co/datasets/sil-ai/bloom-captioning"""
# get all schema w/o lang arg + get all schema w/ lang arg
BUILDER_CONFIGS = construct_configs_on_langs(_LANGUAGES)
def _info(self) -> datasets.DatasetInfo:
_config_schema_name = self.config.schema
logger.info(f"Received schema name: {self.config.schema}")
# source schema
if _config_schema_name == "source":
features = datasets.Features(
{
"image_id": datasets.Value("string"),
"image_url": datasets.Value("string"),
"caption": datasets.Value("string"),
"story_id": datasets.Value("string"),
"album_id": datasets.Value("string"),
"license": datasets.Value("string"),
"original_bloom_language_tag": datasets.Value("string"),
"index_in_story": datasets.Value("uint16"),
}
)
# image-text schema
elif _config_schema_name == "seacrowd_imtext":
features = schemas.image_text_features()
else:
raise ValueError(f"Received unexpected config schema of {_config_schema_name}!")
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]:
hf_dset_dict = datasets.load_dataset(_HF_REMOTE_REF, self.config.subset_id)
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]
def _generate_examples(self, hf_dset) -> Tuple[int, Dict]:
_config_schema_name = self.config.schema
_idx = 0
for datapoints in hf_dset:
# the `_idx` will be generated manually since no `id` present in the dataset fulfill the purpose as primary key
if _config_schema_name == "source":
yield _idx, {colname: datapoints[colname] for colname in self.info.features}
elif _config_schema_name == "seacrowd_imtext":
yield _idx, {"id": _idx, "image_paths": [datapoints["image_url"]], "texts": datapoints["caption"], "metadata": {"context": "", "labels": []}}
else:
raise ValueError(f"Received unexpected config schema of {_config_schema_name}!")
_idx += 1
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