Module pysimt.metrics.multibleu

Tokenized BLEU through sacreBLEU API.

"""Tokenized BLEU through sacreBLEU API."""

from typing import Union, Iterable, TextIO

from sacrebleu import corpus_bleu

from ..utils.misc import listify
from ..utils.io import read_reference_files, read_hypothesis_file
from .metric import Metric


class BLEUScorer:
    """Computes the multi-bleu equivalent using SacreBLEU, with tokenization
    option disabled.

    Args:
        refs: List of reference text files
        hyps: A file path, or a list of hypothesis strings or an open file handle
        language: unused
    """
    def compute(self, refs: Iterable[str],
                hyps: Union[str, Iterable[str], TextIO],
                language=None) -> Metric:
        if isinstance(hyps, str):
            hyps = read_hypothesis_file(hyps)

        assert isinstance(hyps, list)

        refs = read_reference_files(*listify(refs))

        score = corpus_bleu(hyps, refs, tokenize='none')
        verbose_score = ' '.join(score.format().split()[2:])
        float_score = score.score
        return Metric('BLEU', float_score, verbose_score)

Classes

class BLEUScorer

Computes the multi-bleu equivalent using SacreBLEU, with tokenization option disabled.

Args

refs

List of reference text files

hyps

A file path, or a list of hypothesis strings or an open file handle

language

unused

Expand source code

class BLEUScorer:
    """Computes the multi-bleu equivalent using SacreBLEU, with tokenization
    option disabled.

    Args:
        refs: List of reference text files
        hyps: A file path, or a list of hypothesis strings or an open file handle
        language: unused
    """
    def compute(self, refs: Iterable[str],
                hyps: Union[str, Iterable[str], TextIO],
                language=None) -> Metric:
        if isinstance(hyps, str):
            hyps = read_hypothesis_file(hyps)

        assert isinstance(hyps, list)

        refs = read_reference_files(*listify(refs))

        score = corpus_bleu(hyps, refs, tokenize='none')
        verbose_score = ' '.join(score.format().split()[2:])
        float_score = score.score
        return Metric('BLEU', float_score, verbose_score)

Methods

def compute(self, refs: Iterable[str], hyps: Union[str, Iterable[str], TextIO], language=None) ‑> Metric

Expand source code

def compute(self, refs: Iterable[str],
            hyps: Union[str, Iterable[str], TextIO],
            language=None) -> Metric:
    if isinstance(hyps, str):
        hyps = read_hypothesis_file(hyps)

    assert isinstance(hyps, list)

    refs = read_reference_files(*listify(refs))

    score = corpus_bleu(hyps, refs, tokenize='none')
    verbose_score = ' '.join(score.format().split()[2:])
    float_score = score.score
    return Metric('BLEU', float_score, verbose_score)