⚠️ RuSpellEval
-------------------

RuSpellEval is described in `paper <https://www.dialog-21.ru/media/3427/sorokinaaetal.pdf>`_.

The metric does not account for punctuation and register associated errors, so we primarily used it in earlier releases.

You can still invoke the RuSpellEval metric by calling the following:

.. code-block:: python

    from sage.evaluation import Scorer
    from sage.utils import DatasetsAvailable, load_available_dataset_from_hf

    sources, corrections = load_available_dataset_from_hf(DatasetsAvailable.RUSpellRU.name, for_labeler=True, split="test")

    scorer = Scorer()
    metrics = scorer.score(sources, corrections, corrections, metrics=["ruspelleval"])
    print(metrics)

    # {'Precision': 100.0, 'Recall': 100.0, 'F1': 100.0}


... or in conjunction with RuErrant metric:

.. code-block:: python

    import os
    import torch
    from sage.utils import DatasetsAvailable
    from sage.spelling_correction import AvailableCorrectors
    from sage.spelling_correction import T5ModelForSpellingCorruption

    corrector_fred_95m = T5ModelForSpellingCorruption.from_pretrained(AvailableCorrectors.sage_fredt5_distilled_95m.value)
    corrector_mt5 = T5ModelForSpellingCorruption.from_pretrained(AvailableCorrectors.sage_mt5_large.value)

    corrector_fred_95m.model.to(torch.device("cuda:0"))
    corrector_mt5.model.to(torch.device("cuda:0"))

    metrics = corrector_fred_95m.evaluate("RUSpellRU", metrics=["errant", "ruspelleval"], batch_size=32)
    print(metrics)
    # {'CASE_Precision': 94.41, 'CASE_Recall': 92.55, 'CASE_F1': 93.47, 'SPELL_Precision': 77.52, 'SPELL_Recall': 64.09, 'SPELL_F1': 70.17, 'PUNCT_Precision': 86.77, 'PUNCT_Recall': 80.59, 'PUNCT_F1': 83.56, 'YO_Precision': 46.21, 'YO_Recall': 73.83, 'YO_F1': 56.84, 'Precision': 83.48, 'Recall': 74.75, 'F1': 78.87}
