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bloom_lm.py

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+"""
+SEA Crowd Data Loader for Bloom LM.
+"""
+from typing import Dict, Iterator, 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",
+    "kvt": "Lahta",
+    "lao": "Lao",
+    "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",
+    "psp_ceb": "Filipino Sign Language",
+    "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",
+    "yin": "Riang Lai",
+    "zlm": "Malay",
+}
+
+_LOCAL = False
+_LANGUAGES = list(_LANG_CONFIG.keys())
+
+_DATASETNAME = "bloom_lm"
+_DESCRIPTION = r"""
+This is a Bloom Library dataset developed for the self-supervised language modeling 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-lm and use huggingface-cli login for authentication.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/sil-ai/bloom-lm"
+_LICENSE = Licenses.CC.value
+
+_URL = "https://huggingface.co/datasets/sil-ai/bloom-lm"
+_HF_REMOTE_REF = "/".join(_URL.split("/")[-2:])
+
+_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
+_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() -> List[SEACrowdConfig]:
+    """
+    The function `construct_configs` constructs a list of SEACrowdConfig objects based on `_LANGUAGES` var, and returns the list.
+
+    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}",
+            # since the actual subset_id in source for "psp_ceb" is "psp", we are defining the subset_id as following for loading to source HF
+            subset_id=_LANG if _LANG != "psp_ceb" else "psp",
+        )
+        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}",
+                # since the actual subset_id in source for "psp_ceb" is "psp", we are defining the subset_id as following for loading to source HF
+                subset_id=_LANG if _LANG != "psp_ceb" else "psp",
+            )
+            for _LANG in _LANGUAGES
+        ]
+    return config_list
+
+
+class BloomLMDataset(datasets.GeneratorBasedBuilder):
+    """Bloom LM dataset, subsetted from https://huggingface.co/datasets/sil-ai/bloom-lm"""
+
+    # get all schema w/o lang arg + get all schema w/ lang arg
+    BUILDER_CONFIGS = construct_configs_on_langs()
+
+    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(
+                {
+                    "text": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "license": datasets.Value("string"),
+                    "copyright": datasets.Value("string"),
+                    "pageCount": datasets.Value("int32"),
+                    "bookInstanceId": datasets.Value("string"),
+                    "bookLineage": datasets.Value("string"),
+                }
+            )
+
+        # ssp schema
+        elif _config_schema_name == "seacrowd_ssp":
+            features = schemas.ssp_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) -> Iterator[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_ssp":
+                yield _idx, {"id": _idx, "text": datapoints["text"]}
+
+            else:
+                raise ValueError(f"Received unexpected config schema of {_config_schema_name}!")
+
+            _idx += 1