30 lines
1.2 KiB
Python
30 lines
1.2 KiB
Python
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import pytest
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import torch
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from pytorch_lightning.metrics.nlp import BLEUScore
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# example taken from
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# https://www.nltk.org/api/nltk.translate.html?highlight=bleu%20score#nltk.translate.bleu_score.corpus_bleu
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HYP1 = "It is a guide to action which ensures that the military always obeys the commands of the party".split()
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HYP2 = "he read the book because he was interested in world history".split()
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REF1A = "It is a guide to action that ensures that the military will forever heed Party commands".split()
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REF1B = "It is a guiding principle which makes the military forces always being under the command of the Party".split()
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REF1C = "It is the practical guide for the army always to heed the directions of the party".split()
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REF2A = "he was interested in world history because he read the book".split()
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LIST_OF_REFERENCES = [[REF1A, REF1B, REF1C], [REF2A]]
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HYPOTHESES = [HYP1, HYP2]
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@pytest.mark.parametrize(
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["n_gram", "smooth"],
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[pytest.param(1, True), pytest.param(2, False), pytest.param(3, True), pytest.param(4, False),],
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)
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def test_bleu(smooth, n_gram):
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bleu = BLEUScore(n_gram=n_gram, smooth=smooth)
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assert bleu.name == "bleu"
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pl_output = bleu(HYPOTHESES, LIST_OF_REFERENCES)
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assert isinstance(pl_output, torch.Tensor)
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