In mainland China, it is normal for downloading this kind of thing to time out. First, install the datasets package and evaluate package (two packages under huggingface)
So we manually added the following three files, please rename them according to the file name I gave:
File name: bleu.py
""" BLEU metric. """
import datasets
import evaluate
from .bleu_ import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
from .tokenizer_13a import Tokenizer13a
_CITATION = """\
@INPROCEEDINGS{Papineni02bleu: a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
booktitle = {},
year = {2002},
pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",
author = "Lin, Chin-Yew and
Och, Franz Josef",
booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",
month = "aug 23{--}aug 27",
year = "2004",
address = "Geneva, Switzerland",
publisher = "COLING",
url = "https://www.aclweb.org/anthology/C04-1072",
pages = "501--507",
}
"""
_DESCRIPTION = """\
BLEU (Bilingual Evaluation Understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine's output and that of a human: "the closer a machine translation is to a professional human translation, the better it is"
– this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and remains one of the most popular automated and inexpensive metrics.
Scores are calculated for individual translated segments-generally sentences-by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality.
Neither intelligibility nor grammatical correctness are not taken into account.
"""
_KWARGS_DESCRIPTION = """
Computes BLEU score of translated segments against one or more references.
Args:
predictions: list of translations to score.
references: list of lists of or just a list of references for each translation.
tokenizer : approach used for tokenizing `predictions` and `references`.
The default tokenizer is `tokenizer_13a`, a minimal tokenization approach that is equivalent to `mteval-v13a`, used by WMT.
This can be replaced by any function that takes a string as input and returns a list of tokens as output.
max_order: Maximum n-gram order to use when computing BLEU score.
smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
'bleu': bleu score,
'precisions': geometric mean of n-gram precisions,
'brevity_penalty': brevity penalty,
'length_ratio': ratio of lengths,
'translation_length': translation_length,
'reference_length': reference_length
Examples:
>>> predictions = ["hello there general kenobi", "foo bar foobar"]
>>> references = [
... ["hello there general kenobi", "hello there!"],
... ["foo bar foobar"]
... ]
>>> bleu = evaluate.load("bleu")
>>> results = bleu.compute(predictions=predictions, references=references)
>>> print(results["bleu"])
1.0
"""
class Bleu(evaluate. Metric):
def _info(self):
return evaluate. MetricInfo(
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=[
datasets. Features(
{
"predictions": datasets. Value("string", id="sequence"),
"references": datasets.Sequence(datasets.Value("string", id="sequence"), id="references"),
}
),
datasets. Features(
{
"predictions": datasets. Value("string", id="sequence"),
"references": datasets. Value("string", id="sequence"),
}
),
],
codebase_urls=["https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"],
reference_urls=[
"https://en.wikipedia.org/wiki/BLEU",
"https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
],
)
def _compute(self, predictions, references, tokenizer=Tokenizer13a(), max_order=4, smooth=False):
# if only one reference is provided make sure we still use list of lists
if isinstance(references[0], str):
references = [[ref] for ref in references]
references = [[tokenizer(r) for r in ref] for ref in references]
predictions = [tokenizer(p) for p in predictions]
score = compute_bleu(
reference_corpus=references, translation_corpus=predictions, max_order=max_order, smooth=smooth
)
(bleu, precisions, bp, ratio, translation_length, reference_length) = score
return {
"bleu": bleu,
"precisions": precisions,
"brevity_penalty": bp,
"length_ratio": ratio,
"translation_length": translation_length,
"reference_length": reference_length,
}
File name: bleu_.py
# Copyright 2017 Google Inc. All Rights Reserved.
#
# 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.
# ==================================================== ===============================
"""Python implementation of BLEU and smooth-BLEU.
This module provides a Python implementation of BLEU and smooth-BLEU.
Smooth BLEU is computed following the method outlined in the paper:
Chin-Yew Lin, Franz Josef Och. ORANGE: a method for evaluating automatic
evaluation metrics for machine translation. COLING 2004.
"""
import collections
import math
def _get_ngrams(segment, max_order):
"""Extracts all n-grams up to a given maximum order from an input segment.
Args:
segment: text segment from which n-grams will be extracted.
max_order: maximum length in tokens of the n-grams returned by this
methods.
Returns:
The Counter containing all n-grams up to max_order in segment
with a count of how many times each n-gram occurred.
"""
ngram_counts = collections. Counter()
for order in range(1, max_order + 1):
for i in range(0, len(segment) - order + 1):
ngram = tuple(segment[i:i + order])
ngram_counts[ngram] += 1
return ngram_counts
def compute_bleu(reference_corpus, translation_corpus, max_order=4,
smooth=False):
"""Computes BLEU score of translated segments against one or more references.
Args:
reference_corpus: list of lists of references for each translation. Each
reference should be tokenized into a list of tokens.
translation_corpus: list of translations to score. Each translation
should be tokenized into a list of tokens.
max_order: Maximum n-gram order to use when computing BLEU score.
smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
3-Tuple with the BLEU score, n-gram precisions, geometric mean of n-gram
precisions and brevity penalties.
"""
matches_by_order = [0] * max_order
possible_matches_by_order = [0] * max_order
reference_length = 0
translation_length = 0
for (references, translation) in zip(reference_corpus,
translation_corpus):
reference_length + = min(len(r) for r in references)
translation_length + = len(translation)
merged_ref_ngram_counts = collections. Counter()
for reference in references:
merged_ref_ngram_counts |= _get_ngrams(reference, max_order)
translation_ngram_counts = _get_ngrams(translation, max_order)
overlap = translation_ngram_counts & merged_ref_ngram_counts
for ngram in overlap:
matches_by_order[len(ngram)-1] + = overlap[ngram]
for order in range(1, max_order + 1):
possible_matches = len(translation) - order + 1
if possible_matches > 0:
possible_matches_by_order[order-1] += possible_matches
precisions = [0] * max_order
for i in range(0, max_order):
if smooth:
precisions[i] = ((matches_by_order[i] + 1.) /
(possible_matches_by_order[i] + 1.))
else:
if possible_matches_by_order[i] > 0:
precisions[i] = (float(matches_by_order[i]) /
possible_matches_by_order[i])
else:
precisions[i] = 0.0
if min(precisions) > 0:
p_log_sum = sum((1. / max_order) * math. log(p) for p in precisions)
geo_mean = math.exp(p_log_sum)
else:
geo_mean = 0
ratio = float(translation_length) / reference_length
if ratio > 1.0:
bp = 1.
else:
bp = math. exp(1 - 1. / ratio)
bleu = geo_mean * bp
return (bleu, precisions, bp, ratio, translation_length, reference_length)
File name: tokenizer_13a.py
# Source: https://github.com/mjpost/sacrebleu/blob/master/sacrebleu/tokenizers/tokenizer_13a.py
# Copyright 2020 SacreBLEU Authors.
#
# 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.
import re
from functools import lru_cache
class BaseTokenizer:
"""A base dummy tokenizer to derive from."""
def signature(self):
"""
Returns a signature for the tokenizer.
:return: signature string
"""
return "none"
def __call__(self, line):
"""
Tokenizes an input line with the tokenizer.
:param line: a segment to tokenize
:return: the tokenized line
"""
return line
class TokenizerRegexp(BaseTokenizer):
def signature(self):
return "re"
def __init__(self):
self._re = [
# language-dependent part (assuming Western languages)
(re.compile(r"([\{-\~\[-\` -\ & amp;\(-\ + \:-\@\/])\ "), r" \1 "),
# tokenize period and comma unless preceded by a digit
(re. compile(r"([^0-9])([\.,])"), r"\1 \2 "),
# tokenize period and comma unless followed by a digit
(re.compile(r"([\.,])([^0-9])"), r" \1 \2"),
# tokenize dash when preceded by a digit
(re. compile(r"([0-9])(-)"), r"\1 \2 "),
# one space only between words
# NOTE: Doing this in Python (below) is faster
# (re. compile(r'\s + '), r' '),
]
@lru_cache(maxsize=2**16)
def __call__(self, line):
"""Common post-processing tokenizer for `13a` and `zh` tokenizers.
:param line: a segment to tokenize
:return: the tokenized line
"""
for (_re, repl) in self._re:
line = _re.sub(repl, line)
# no leading or trailing spaces, single space within words
# return ' '. join(line. split())
# This line is changed with regards to the original tokenizer (seen above) to return individual words
return line. split()
class Tokenizer13a(BaseTokenizer):
def signature(self):
return "13a"
def __init__(self):
self._post_tokenizer = TokenizerRegexp()
@lru_cache(maxsize=2**16)
def __call__(self, line):
"""Tokenizes an input line using a relatively minimal tokenization
that is however equivalent to mteval-v13a, used by WMT.
:param line: a segment to tokenize
:return: the tokenized line
"""
# language-independent part:
line = line.replace("<skipped>", "")
line = line.replace("-\\
", "")
line = line. replace("\\
", " ")
if " &" in line:
line = line.replace(" & amp;quot;", '"')
line = line.replace(" & amp;amp;", " & amp;")
line = line.replace(" <", "<")
line = line.replace(" >", ">")
return self._post_tokenizer(f"{line}")
Put these three files in the same folder, use:
from xxx folder.bleu import Bleu
metric = Bleu()
"""
predictions are predicted, and the references parameter refers to ground-truth
"""
metric. compute(predictions=predictions, references=labels)
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