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83d24b2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | from __future__ import annotations
import datasets
class MultiSubsetLoader:
def load_data(self, **kwargs):
"""Load dataset containing multiple subsets from HuggingFace hub"""
if self.data_loaded:
return
fast_loading = self.fast_loading if hasattr(self, "fast_loading") else False
if fast_loading:
self.fast_load()
else:
self.slow_load()
self.dataset_transform()
self.data_loaded = True
def fast_load(self, **kwargs):
"""Load all subsets at once, then group by language with Polars. Using fast loading has two requirements:
- Each row in the dataset should have a 'lang' feature giving the corresponding language/language pair
- The datasets must have a 'default' config that loads all the subsets of the dataset (see https://huggingface.co/docs/datasets/en/repository_structure#configurations)
"""
self.dataset = {}
merged_dataset = datasets.load_dataset(
**self.metadata_dict["dataset"]
) # load "default" subset
for split in merged_dataset.keys():
df_split = merged_dataset[split].to_polars()
df_grouped = dict(df_split.group_by("lang"))
for lang in set(df_split["lang"].unique()) & set(self.hf_subsets):
self.dataset.setdefault(lang, {})
self.dataset[lang][split] = datasets.Dataset.from_polars(
df_grouped[lang].drop("lang")
) # Remove lang column and convert back to HF datasets, not strictly necessary but better for compatibility
for lang, subset in self.dataset.items():
self.dataset[lang] = datasets.DatasetDict(subset)
def slow_load(self, **kwargs):
"""Load each subsets iteratively"""
self.dataset = {}
for lang in self.hf_subsets:
self.dataset[lang] = datasets.load_dataset(
name=lang,
**self.metadata_dict.get("dataset", None),
)
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