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import datasets
from datasets import GeneratorBasedBuilder, BuilderConfig, Sequence, Value
from .parsing import parse_incr
from .train_test_split import train_test_split
class LanguageSpecificConfig(BuilderConfig):
def __init__(
self,
language: str,
data_file: str,
train_fraction: float = 0.8,
**kwargs
):
super().__init__(**kwargs)
self.language = language
self.data_file = data_file
self.train_fraction = train_fraction
class EnhancedCobaldDataset(GeneratorBasedBuilder):
BUILDER_CONFIGS = [
LanguageSpecificConfig(
name="en",
language="en",
data_file="https://raw.githubusercontent.com/CobaldAnnotation/CobaldEng/refs/heads/main/enhanced/train.conllu",
description="English dataset."
),
LanguageSpecificConfig(
name="ru",
language="ru",
data_file="https://raw.githubusercontent.com/CobaldAnnotation/CobaldRus/refs/heads/main/enhanced/train.conllu",
description="Russian dataset."
),
# Other languages here
]
def _info(self):
return datasets.DatasetInfo(
description="A CoBaLD dataset in CoNLL-U plus format.",
features=datasets.Features({
"ids": Sequence(Value("string")),
"words": Sequence(Value("string")),
"lemmas": Sequence(Value("string")),
"upos": Sequence(Value("string")),
"xpos": Sequence(Value("string")),
# huggingface datasets can't handle dicts with dynamic keys, so represent feats as string
"feats": Sequence(Value("string")),
"heads": Sequence(Value("int32")),
"deprels": Sequence(Value("string")),
"deps": Sequence(Value("string")),
"miscs": Sequence(Value("string")),
"deepslots": Sequence(Value("string")),
"semclasses": Sequence(Value("string")),
"sent_id": Value("string"),
"text": Value("string"),
})
)
def _split_generators(self, dl_manager):
data_path = dl_manager.download_and_extract(self.config.data_file)
# Load all sentences in memory, since train-test split depends on data.
sentences = list(parse_incr(data_path))
train_sentences, validation_sentences = train_test_split(
sentences,
train_fraction=self.config.train_fraction,
tagsets_names=[
'upos',
'xpos',
'feats',
'deprels',
'deps',
'miscs',
'deepslots',
'semclasses'
]
)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"examples": train_sentences}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"examples": validation_sentences}
)
]
def _generate_examples(self, examples: list):
yield from enumerate(examples) |