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from collections.abc import Sequence |
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from typing import Any, Optional, Union |
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from torch import Tensor |
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from torchmetrics.functional.text.bleu import _bleu_score_update |
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from torchmetrics.functional.text.sacre_bleu import _SacreBLEUTokenizer, _TokenizersLiteral |
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from torchmetrics.text.bleu import BLEUScore |
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from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE |
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from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE |
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if not _MATPLOTLIB_AVAILABLE: |
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__doctest_skip__ = ["SacreBLEUScore.plot"] |
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class SacreBLEUScore(BLEUScore): |
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"""Calculate `BLEU score`_ of machine translated text with one or more references. |
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This implementation follows the behaviour of `SacreBLEU`_. The SacreBLEU implementation differs from the NLTK BLEU |
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implementation in tokenization techniques. |
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As input to ``forward`` and ``update`` the metric accepts the following input: |
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- ``preds`` (:class:`~Sequence`): An iterable of machine translated corpus |
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- ``target`` (:class:`~Sequence`): An iterable of iterables of reference corpus |
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As output of ``forward`` and ``compute`` the metric returns the following output: |
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- ``sacre_bleu`` (:class:`~torch.Tensor`): A tensor with the SacreBLEU Score |
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Args: |
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n_gram: Gram value ranged from 1 to 4 |
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smooth: Whether to apply smoothing, see `SacreBLEU`_ |
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tokenize: Tokenization technique to be used. Choose between ``'none'``, ``'13a'``, ``'zh'``, ``'intl'``, |
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``'char'``, ``'ja-mecab'``, ``'ko-mecab'``, ``'flores101'`` and ``'flores200'``. |
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lowercase: If ``True``, BLEU score over lowercased text is calculated. |
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kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. |
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weights: |
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Weights used for unigrams, bigrams, etc. to calculate BLEU score. |
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If not provided, uniform weights are used. |
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Raises: |
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ValueError: |
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If ``tokenize`` not one of 'none', '13a', 'zh', 'intl' or 'char' |
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ValueError: |
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If ``tokenize`` is set to 'intl' and `regex` is not installed |
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ValueError: |
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If a length of a list of weights is not ``None`` and not equal to ``n_gram``. |
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Example: |
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>>> from torchmetrics.text import SacreBLEUScore |
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>>> preds = ['the cat is on the mat'] |
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>>> target = [['there is a cat on the mat', 'a cat is on the mat']] |
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>>> sacre_bleu = SacreBLEUScore() |
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>>> sacre_bleu(preds, target) |
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tensor(0.7598) |
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Additional References: |
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- Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence |
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and Skip-Bigram Statistics by Chin-Yew Lin and Franz Josef Och `Machine Translation Evolution`_ |
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""" |
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is_differentiable: bool = False |
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higher_is_better: bool = True |
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full_state_update: bool = True |
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plot_lower_bound: float = 0.0 |
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plot_upper_bound: float = 1.0 |
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def __init__( |
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self, |
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n_gram: int = 4, |
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smooth: bool = False, |
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tokenize: _TokenizersLiteral = "13a", |
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lowercase: bool = False, |
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weights: Optional[Sequence[float]] = None, |
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**kwargs: Any, |
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) -> None: |
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super().__init__(n_gram=n_gram, smooth=smooth, weights=weights, **kwargs) |
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self.tokenizer = _SacreBLEUTokenizer(tokenize, lowercase) |
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def update(self, preds: Sequence[str], target: Sequence[Sequence[str]]) -> None: |
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"""Update state with predictions and targets.""" |
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self.preds_len, self.target_len = _bleu_score_update( |
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preds, |
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target, |
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self.numerator, |
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self.denominator, |
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self.preds_len, |
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self.target_len, |
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self.n_gram, |
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self.tokenizer, |
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) |
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def plot( |
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self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None |
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) -> _PLOT_OUT_TYPE: |
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"""Plot a single or multiple values from the metric. |
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Args: |
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val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. |
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If no value is provided, will automatically call `metric.compute` and plot that result. |
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ax: An matplotlib axis object. If provided will add plot to that axis |
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Returns: |
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Figure and Axes object |
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Raises: |
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ModuleNotFoundError: |
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If `matplotlib` is not installed |
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.. plot:: |
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:scale: 75 |
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>>> # Example plotting a single value |
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>>> from torchmetrics.text import SacreBLEUScore |
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>>> metric = SacreBLEUScore() |
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>>> preds = ['the cat is on the mat'] |
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>>> target = [['there is a cat on the mat', 'a cat is on the mat']] |
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>>> metric.update(preds, target) |
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>>> fig_, ax_ = metric.plot() |
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.. plot:: |
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:scale: 75 |
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>>> # Example plotting multiple values |
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>>> from torchmetrics.text import SacreBLEUScore |
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>>> metric = SacreBLEUScore() |
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>>> preds = ['the cat is on the mat'] |
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>>> target = [['there is a cat on the mat', 'a cat is on the mat']] |
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>>> values = [ ] |
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>>> for _ in range(10): |
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... values.append(metric(preds, target)) |
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>>> fig_, ax_ = metric.plot(values) |
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""" |
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return self._plot(val, ax) |
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