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sacre_bleu.py
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sacre_bleu.py
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# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# referenced from
# Library Name: torchtext
# Authors: torchtext authors and @sluks
# Date: 2020-07-18
# Link: https://pytorch.org/text/_modules/torchtext/data/metrics.html#bleu_score
from typing import Any, Callable, Optional, Sequence
from typing_extensions import Literal
from torchmetrics.functional.text.bleu import _bleu_score_update
from torchmetrics.functional.text.sacre_bleu import _SacreBLEUTokenizer
from torchmetrics.text.bleu import BLEUScore
from torchmetrics.utilities.imports import _REGEX_AVAILABLE
AVAILABLE_TOKENIZERS = ("none", "13a", "zh", "intl", "char")
class SacreBLEUScore(BLEUScore):
"""Calculate `BLEU score`_ [1] of machine translated text with one or more references. This implementation
follows the behaviour of SacreBLEU [2] implementation from https://github.com/mjpost/sacrebleu.
The SacreBLEU implementation differs from the NLTK BLEU implementation in tokenization techniques.
Args:
n_gram:
Gram value ranged from 1 to 4 (Default 4)
smooth:
Whether or not to apply smoothing – see [2]
tokenize:
Tokenization technique to be used. (Default '13a')
Supported tokenization: ['none', '13a', 'zh', 'intl', 'char']
lowercase:
If ``True``, BLEU score over lowercased text is calculated.
compute_on_step:
Forward only calls ``update()`` and returns None if this is set to False. default: True
dist_sync_on_step:
Synchronize metric state across processes at each ``forward()``
before returning the value at the step.
process_group:
Specify the process group on which synchronization is called. default: None (which selects the entire world)
dist_sync_fn:
Callback that performs the allgather operation on the metric state. When `None`, DDP
will be used to perform the allgather.
Raises:
ValueError:
If ``tokenize`` not one of 'none', '13a', 'zh', 'intl' or 'char'
ValueError:
If ``tokenize`` is set to 'intl' and `regex` is not installed
Example:
>>> translate_corpus = ['the cat is on the mat']
>>> reference_corpus = [['there is a cat on the mat', 'a cat is on the mat']]
>>> metric = SacreBLEUScore()
>>> metric(reference_corpus, translate_corpus)
tensor(0.7598)
References:
[1] BLEU: a Method for Automatic Evaluation of Machine Translation by Papineni,
Kishore, Salim Roukos, Todd Ward, and Wei-Jing Zhu `BLEU`_
[2] A Call for Clarity in Reporting BLEU Scores by Matt Post.
[3] Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence
and Skip-Bigram Statistics by Chin-Yew Lin and Franz Josef Och `Machine Translation Evolution`_
"""
def __init__(
self,
n_gram: int = 4,
smooth: bool = False,
tokenize: Literal["none", "13a", "zh", "intl", "char"] = "13a",
lowercase: bool = False,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Optional[Callable] = None,
):
super().__init__(
n_gram=n_gram,
smooth=smooth,
compute_on_step=compute_on_step,
dist_sync_on_step=dist_sync_on_step,
process_group=process_group,
dist_sync_fn=dist_sync_fn,
)
if tokenize not in AVAILABLE_TOKENIZERS:
raise ValueError(f"Argument `tokenize` expected to be one of {AVAILABLE_TOKENIZERS} but got {tokenize}.")
if tokenize == "intl" and not _REGEX_AVAILABLE:
raise ValueError(
"`'intl'` tokenization requires `regex` installed. Use `pip install regex` or `pip install "
"torchmetrics[text]`."
)
self.tokenizer = _SacreBLEUTokenizer(tokenize, lowercase)
def update( # type: ignore
self, reference_corpus: Sequence[Sequence[str]], translate_corpus: Sequence[str]
) -> None:
"""Compute Precision Scores.
Args:
reference_corpus: An iterable of iterables of reference corpus
translate_corpus: An iterable of machine translated corpus
"""
reference_corpus_: Sequence[Sequence[Sequence[str]]] = [
[self.tokenizer(line) for line in reference] for reference in reference_corpus
]
translate_corpus_: Sequence[Sequence[str]] = [self.tokenizer(line) for line in translate_corpus]
self.trans_len, self.ref_len = _bleu_score_update(
reference_corpus_,
translate_corpus_,
self.numerator,
self.denominator,
self.trans_len,
self.ref_len,
self.n_gram,
)