| 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"] |
| ) |
| 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") |
| ) |
| 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), |
| ) |
|
|
