Datasets:
Formats:
json
Size:
10M - 100M
File size: 4,000 Bytes
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"""Mímir Core v1 dataset."""
import gzip
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """\\nMímir Core v1."""
_CITATION = """
"""
_URL = "https://github.com/NbAiLab/mimir-data"
_DATA_URL = "https://huggingface.co/datasets/mimir-project/mimir-core/resolve/main/data/{split_suffix}-{segment}-{index:04d}-of-{n_shards:04d}.json"
_N_SHARDS_PER_SPLIT = {
"bad": {"train": 6, "validation": 1},
"medium": {"train": 21, "validation": 1},
"good": {"train": 7, "validation": 1},
}
_SEGMENTS = ("bad", "medium", "good")
class MimirCoreConfig(datasets.BuilderConfig):
"""BuilderConfig for MimirCore."""
def __init__(self, name=None, *args, **kwargs):
"""BuilderConfig for MimirCore.
Args:
**kwargs: keyword arguments forwarded to super.
"""
if name is None:
name = "default"
elif name not in _SEGMENTS:
raise ValueError(f"Invalid segment option '{name}'. Options are {str(_SEGMENTS)}.")
self.name = name
super().__init__(
*args,
name=name,
**kwargs,
)
class MimirCore(datasets.GeneratorBasedBuilder):
"""Mimir Core v1."""
BUILDER_CONFIGS = [MimirCoreConfig()] + [MimirCoreConfig(segment) for segment in _SEGMENTS]
BUILDER_CONFIG_CLASS = MimirCoreConfig
DEFAULT_CONFIG_NAME = "default"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"doc_type": datasets.Value("string"),
"publish_year": datasets.Value("int32"),
"lang_fasttext": datasets.Value("string"),
"lang_fasttext_conf": datasets.Value("string"),
"text": datasets.Value("string"),
"perplexity": datasets.Value("float"),
"perplexity_model": datasets.Value("string"),
"harmful_pp": datasets.Value("float"),
"segment": datasets.Value("string"),
}
),
supervised_keys=None,
homepage=_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
if self.config.name != "default":
segments = [self.config.name]
else:
segments = _SEGMENTS
data_urls = {}
for split in ["train", "validation"]:
data_urls[split] = []
for segment in segments:
data_urls[split] += [
_DATA_URL.format(
split_suffix=split,
segment=segment,
index=index,
n_shards=_N_SHARDS_PER_SPLIT[segment][split],
)
for index in range(1, _N_SHARDS_PER_SPLIT[segment][split] + 1)
]
train_downloaded_files = dl_manager.download(data_urls["train"])
validation_downloaded_files = dl_manager.download(data_urls["validation"])
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": validation_downloaded_files}
),
]
def _generate_examples(self, filepaths):
"""This function returns the examples in the raw (text) form by iterating on all the files."""
id_ = 0
for filepath in filepaths:
logger.info("generating examples from = %s", filepath)
with open(filepath, "rb") as b, gzip.open(b, "rt", encoding="utf-8") as f:
for line in f:
if line.strip():
example = json.loads(line)
yield id_, example
id_ += 1
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