Package: sacrebleu-macrof
sacreBLEUSacreBLEU (Post, 2018) provides hassle-free computation of shareable, comparable, and reproducibleBLEUscores.
Inspired by Rico Sennrich'smulti-bleu-detok.perl, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.The official version is hosted athttps://github.com/mjpost/sacrebleu.MotivationComparing BLEU scores is harder than it should be. Every decoder has its own implementation, often borrowed from Moses, but maybe with subtle changes.
Moses itself has a number of implementations as standalone scripts, with little indication of how they differ (note: they mostly don't, butmulti-bleu.plexpects tokenized input). Different flags passed to each of these scripts can produce wide swings in the final score. All of these may handle tokenization in different ways. On top of this, downloading and managing test sets is a moderate annoyance.Sacre bleu! What a mess.SacreBLEUaims to solve these problems by wrapping the original reference implementation (Papineni et al., 2002) together with other useful features.
The defaults are set the way that BLEU should be computed, and furthermore, the script outputs a short version string that allows others to know exactly what you did.
As an added bonus, it automatically downloads and manages test sets for you, so that you can simply tell it to score againstwmt14, without having to hunt down a path on your local file system.
It is all designed to take BLEU a little more seriously.
After all, even with all its problems, BLEU is the default and---admit it---well-loved metric of our entire research community.
Sacre BLEU.FeaturesIt automatically downloads common WMT test sets and processes them to plain textIt produces a short version string that facilitates cross-paper comparisonsIt properly computes scores on detokenized outputs, using WMT (Conference on Machine Translation) standard tokenizationIt produces the same values as the official script (mteval-v13a.pl) used by WMTIt outputs the BLEU score without the comma, so you don't have to remove it withsed(Looking at you,multi-bleu.perl)It supports different tokenizers for BLEU including support for Japanese and ChineseIt supportschrF, chrF++andTranslation error rate (TER)metricsIt performs paired bootstrap resampling and paired approximate randomization tests for statistical significance reportingBreaking Changesv2.0.0As of v2.0.0, the default output format is changed tojsonfor less painful parsing experience. This means that software that parse the output of sacreBLEU should be modified to either (i) parse the JSON using for example thejqutility or (ii) pass-f textto sacreBLEU to preserve the old textual output. The latter change can also be madepersistentlyby exportingSACREBLEU_FORMAT=textin relevant shell configuration files.Here's an example of parsing thescorekey of the JSON output usingjq:$ sacrebleu -i output.detok.txt -t wmt17 -l en-de | jq -r .score
20.8InstallationInstall the official Python module from PyPI (Python>=3.6 only):pip install sacrebleuIn order to install Japanese tokenizer support throughmecab-python3, you need to run the
following command instead, to perform a full installation with dependencies:pip install sacrebleu[ja]Command-line UsageYou can get a list of available test sets withsacrebleu --list. Please seeDATASETS.mdfor an up-to-date list of supported datasets.BasicsDownloading test setsDownload thesourcefor one of the pre-defined test sets:$ sacrebleu -t wmt17 -l en-de --echo src | head -n1
28-Year-Old Chef Found Dead at San Francisco MallDownload thereferencefor one of the pre-defined test sets:$ sacrebleu -t wmt17 -l en-de --echo ref | head -n1
28-jähriger Koch in San Francisco Mall tot aufgefundenJSON outputAs of version>=2.0.0, sacreBLEU prints the computed scores in JSON format to make parsing less painful:$ sacrebleu -i output.detok.txt -t wmt17 -l en-de{"name":"BLEU","score":20.8,"signature":"nrefs:1|case:mixed|eff:no|tok:13a|smooth:exp|version:2.0.0","verbose_score":"54.4/26.6/14.9/8.7 (BP = 1.000 ratio = 1.026 hyp_len = 62880 ref_len = 61287)","nrefs":"1","case":"mixed","eff":"no","tok":"13a","smooth":"exp","version":"2.0.0"}If you want to keep the old behavior, you can pass-f textor exportSACREBLEU_FORMAT=text:$ sacrebleu -i output.detok.txt -t wmt17 -l en-de -f text
BLEU|nrefs:1|case:mixed|eff:no|tok:13a|smooth:exp|version:2.0.0 = 20.8 54.4/26.6/14.9/8.7 (BP = 1.000 ratio = 1.026 hyp_len = 62880 ref_len = 61287)Scoring(All examples below assume old-style text output for a compact representation that save space)Let's say that you just translated theen-detest set of WMT17 with your fancy MT system and thedetokenizedtranslations are in a file calledoutput.detok.txt:# Option 1: Redirect system output to STDIN
$ cat output.detok.txt | sacrebleu -t wmt17 -l en-de
BLEU|nrefs:1|case:mixed|eff:no|tok:13a|smooth:exp|version:2.0.0 = 20.8 54.4/26.6/14.9/8.7 (BP = 1.000 ratio = 1.026 hyp_len = 62880 ref_len = 61287)

# Option 2: Use the --input/-i argument
$ sacrebleu -t wmt17 -l en-de -i output.detok.txt
BLEU|nrefs:1|case:mixed|eff:no|tok:13a|smooth:exp|version:2.0.0 = 20.8 54.4/26.6/14.9/8.7 (BP = 1.000 ratio = 1.026 hyp_len = 62880 ref_len = 61287)You can obtain a short version of the signature with--short/-sh:$ sacrebleu -t wmt17 -l en-de -i output.detok.txt -sh
BLEU|#:1|c:mixed|e:no|tok:13a|s:exp|v:2.0.0 = 20.8 54.4/26.6/14.9/8.7 (BP = 1.000 ratio = 1.026 hyp_len = 62880 ref_len = 61287)If you only want the score to be printed, you can use the--score-only/-bflag:$ sacrebleu -t wmt17 -l en-de -i output.detok.txt -b
20.8The precision of the scores can be configured via the--width/-wflag:$ sacrebleu -t wmt17 -l en-de -i output.detok.txt -b -w 4
20.7965Using your own reference fileSacreBLEU knows about common test sets (as detailed in the--listexample above), but you can also use it to score system outputs with arbitrary references. In this case, do not forget to providedetokenizedreference and hypotheses files:# Let's save the reference to a text file
$ sacrebleu -t wmt17 -l en-de --echo ref > ref.detok.txt

# Option 1: Pass the reference file as a positional argument to sacreBLEU
$ sacrebleu ref.detok.txt -i output.detok.txt -m bleu -b -w 4
20.7965

# Option 2: Redirect the system into STDIN (Compatible with multi-bleu.perl way of doing things)
$ cat output.detok.txt | sacrebleu ref.detok.txt -m bleu -b -w 4
20.7965Using multiple metricsLet's first compute BLEU, chrF and TER with the default settings:$ sacrebleu -t wmt17 -l en-de -i output.detok.txt -m bleu chrf ter
        BLEU|nrefs:1|case:mixed|eff:no|tok:13a|smooth:exp|version:2.0.0 = 20.8 <stripped>
      chrF2|nrefs:1|case:mixed|eff:yes|nc:6|nw:0|space:no|version:2.0.0 = 52.0
TER|nrefs:1|case:lc|tok:tercom|norm:no|punct:yes|asian:no|version:2.0.0 = 69.0Let's now enablechrF++which is a revised version of chrF that takes into account word n-grams.
Observe how thenw:0gets changed intonw:2in the signature:$ sacrebleu -t wmt17 -l en-de -i output.detok.txt -m bleu chrf ter --chrf-word-order 2
        BLEU|nrefs:1|case:mixed|eff:no|tok:13a|smooth:exp|version:2.0.0 = 20.8 <stripped>
    chrF2++|nrefs:1|case:mixed|eff:yes|nc:6|nw:2|space:no|version:2.0.0 = 49.0
TER|nrefs:1|case:lc|tok:tercom|norm:no|punct:yes|asian:no|version:2.0.0 = 69.0Metric-specific arguments are detailed in the output of--help:BLEU related arguments:
  --smooth-method {none,floor,add-k,exp}, -s {none,floor,add-k,exp}
                        Smoothing method: exponential decay, floor (increment zero counts), add-k (increment num/denom by k for n>1), or none. (Default: exp)
  --smooth-value BLEU_SMOOTH_VALUE, -sv BLEU_SMOOTH_VALUE
                        The smoothing value. Only valid for floor and add-k. (Defaults: floor: 0.1, add-k: 1)
  --tokenize {none,zh,13a,char,intl,ja-mecab}, -tok {none,zh,13a,char,intl,ja-mecab}
                        Tokenization method to use for BLEU. If not provided, defaults to `zh` for Chinese, `ja-mecab` for Japanese and `13a` (mteval) otherwise.
  --lowercase, -lc      If True, enables case-insensitivity. (Default: False)
  --force               Insist that your tokenized input is actually detokenized.

chrF related arguments:
  --chrf-char-order CHRF_CHAR_ORDER, -cc CHRF_CHAR_ORDER
                        Character n-gram order. (Default: 6)
  --chrf-word-order CHRF_WORD_ORDER, -cw CHRF_WORD_ORDER
                        Word n-gram order (Default: 0). If equals to 2, the metric is referred to as chrF++.
  --chrf-beta CHRF_BETA
                        Determine the importance of recall w.r.t precision. (Default: 2)
  --chrf-whitespace     Include whitespaces when extracting character n-grams. (Default: False)
  --chrf-lowercase      Enable case-insensitivity. (Default: False)
  --chrf-eps-smoothing  Enables epsilon smoothing similar to chrF++.py, NLTK and Moses; instead of effective order smoothing. (Default: False)

TER related arguments (The defaults replicate TERCOM's behavior):
  --ter-case-sensitive  Enables case sensitivity (Default: False)
  --ter-asian-support   Enables special treatment of Asian characters (Default: False)
  --ter-no-punct        Removes punctuation. (Default: False)
  --ter-normalized      Applies basic normalization and tokenization. (Default: False)

MacroF and MicroF related arguments (the defaults replicate Gowda et al NAACL 2021):
  --f-beta F_BETA       Determine the importance of recall w.r.t precision. (Default: 1)
  --f-lowercase         Enable case-insensitivity. (Default: False)
  --f-tokenize {none,zh,13a,char,intl,ja-mecab}
                        Tokenization method to use for Macro and MicroF, same as BLEU --tokenize. (Default: None)
  --f-smooth-value F_SMOOTH_VALUE
                        The smoothing value. only add-k smoothing method is supported for MacroF and MicroF (Default: 1)Version SignaturesAs you may have noticed, sacreBLEU generates version strings such asBLEU|nrefs:1|case:mixed|eff:no|tok:13a|smooth:exp|version:2.0.0for reproducibility reasons. It's strongly recommended to share these signatures in your papers!Translationese SupportIf you are interested in the translationese effect, you can evaluate BLEU on a subset of sentences
with a given original language (identified based on theoriglangtag in the raw SGM files).
E.g., to evaluate only against originally German sentences translated to English use:$ sacrebleu -t wmt13 -l de-en --origlang=de -i my-wmt13-output.txtand to evaluate against the complement (in this caseoriglangen, fr, cs, ru, de) use:$ sacrebleu -t wmt13 -l de-en --origlang=non-de -i my-wmt13-output.txtPlease notethat the evaluator will return a BLEU score only on the requested subset,
but it expects that you pass through the entire translated test set.Languages & PreprocessingBLEUYou can compute case-insensitive BLEU by passing--lowercaseto sacreBLEUThe default tokenizer for BLEU is13awhich mimics themteval-v13ascript from Moses.Other tokenizers are:nonewhich will not apply any kind of tokenization at allcharfor language-agnostic character-level tokenizationintlapplies international tokenization and mimics themteval-v14script from Moseszhseparates outChinesecharacters and tokenizes the non-Chinese parts using13atokenizerja-mecabtokenizesJapaneseinputs using theMeCabmorphological analyzerYou can switch tokenizers using the--tokenizeflag of sacreBLEU. Alternatively, if you provide language-pair strings
using--language-pair/-l,zhandja-mecabtokenizers will be used if the target language iszhorja, respectively.Note thatthere's no automatic language detection from the hypotheses so you need to make sure that you are correctly
selecting the tokenizer forJapaneseandChinese.Default 13a tokenizer will produce poor results for Japanese:$ sacrebleu kyoto-test.ref.ja -i kyoto-test.hyp.ja -b
2.1Let's use theja-mecabtokenizer:$ sacrebleu kyoto-test.ref.ja -i kyoto-test.hyp.ja --tokenize ja-mecab -b
14.5If you provide the language-pair, sacreBLEU will use ja-mecab automatically:$ sacrebleu kyoto-test.ref.ja -i kyoto-test.hyp.ja -l en-ja -b
14.5chrF / chrF++chrF applies minimum to none pre-processing as it deals with character n-grams:If you pass--chrf-whitespace, whitespace characters will be preserved when computing character n-grams.If you pass--chrf-lowercase, sacreBLEU will compute case-insensitive chrF.If you enable non-zero--chrf-word-order(pass2forchrF++), a very simple punctuation tokenization will be internally applied.TERTranslation Error Rate (TER) has its own special tokenizer that you can configure through the command line.
The defaults provided arecompatible with the upstream TER implementation (TERCOM)but you can nevertheless modify the
behavior through the command-line:TER is by default case-insensitive. Pass--ter-case-sensitiveto enable case-sensitivity.Pass--ter-normalizeto apply a general Western tokenizationPass--ter-asian-supportto enable the tokenization of Asian characters. If provided with--ter-normalize,
both will be applied.Pass--ter-no-punctto strip punctuation.Multi-reference EvaluationAll three metrics support the use of multiple references during evaluation. Let's first pass all references as positional arguments:$ sacrebleu ref1 ref2 -i system -m bleu chrf ter
        BLEU|nrefs:2|case:mixed|eff:no|tok:13a|smooth:exp|version:2.0.0 = 61.8 <stripped>
      chrF2|nrefs:2|case:mixed|eff:yes|nc:6|nw:0|space:no|version:2.0.0 = 75.0
TER|nrefs:2|case:lc|tok:tercom|norm:no|punct:yes|asian:no|version:2.0.0 = 31.2Alternatively (less recommended), we can concatenate references using tabs as delimiters as well. Don't forget to pass--num-refs/-nrin this case!$ paste ref1 ref2 > refs.tsv

$ sacrebleu refs.tsv --num-refs 2 -i system -m bleu
BLEU|nrefs:2|case:mixed|eff:no|tok:13a|smooth:exp|version:2.0.0 = 61.8 <stripped>Multi-system EvaluationAs of version>=2.0.0, SacreBLEU supports evaluation of an arbitrary number of systems for a particular
test set and language-pair. This has the advantage of seeing all results in a
nicely formatted table.Let's pass all system output files that match the shell globnewstest2017.online-*to sacreBLEU for evaluation:$ sacrebleu -t wmt17 -l en-de -i newstest2017.online-* -m bleu chrf
╒═══════════════════════════════╤════════╤═════════╕
│                        System │  BLEU  │  chrF2  │
╞═══════════════════════════════╪════════╪═════════╡
│ newstest2017.online-A.0.en-de │  20.8  │  52.0   │
├───────────────────────────────┼────────┼─────────┤
│ newstest2017.online-B.0.en-de │  26.7  │  56.3   │
├───────────────────────────────┼────────┼─────────┤
│ newstest2017.online-F.0.en-de │  15.5  │  49.3   │
├───────────────────────────────┼────────┼─────────┤
│ newstest2017.online-G.0.en-de │  18.2  │  51.6   │
╘═══════════════════════════════╧════════╧═════════╛

-----------------
Metric signatures
-----------------
 - BLEU       nrefs:1|case:mixed|eff:no|tok:13a|smooth:exp|version:2.0.0
 - chrF2      nrefs:1|case:mixed|eff:yes|nc:6|nw:0|space:no|version:2.0.0You can also change the output format tolatex:$ sacrebleu -t wmt17 -l en-de -i newstest2017.online-* -m bleu chrf -f latex
\begin{tabular}{rcc}
\toprule
                        System &  BLEU  &  chrF2  \\
\midrule
 newstest2017.online-A.0.en-de &  20.8  &  52.0   \\
 newstest2017.online-B.0.en-de &  26.7  &  56.3   \\
 newstest2017.online-F.0.en-de &  15.5  &  49.3   \\
 newstest2017.online-G.0.en-de &  18.2  &  51.6   \\
\bottomrule
\end{tabular}

...Confidence Intervals for Single System EvaluationWhen enabled with the--confidenceflag, SacreBLEU will print
(1) the actual system score, (2) the true mean estimated from bootstrap resampling and (3),
the 95%confidence intervalaround the mean.
By default, the number of bootstrap resamples is 1000 (bs:1000in the signature)
and can be changed with--confidence-n:$ sacrebleu -t wmt17 -l en-de -i output.detok.txt -m bleu chrf --confidence -f text --short
   BLEU|#:1|bs:1000|rs:12345|c:mixed|e:no|tok:13a|s:exp|v:2.0.0 = 22.675 (μ = 22.669 ± 0.598) ...
chrF2|#:1|bs:1000|rs:12345|c:mixed|e:yes|nc:6|nw:0|s:no|v:2.0.0 = 51.953 (μ = 51.953 ± 0.462)NOTE:Although provided as a functionality, having access to confidence intervals for just one system
may not reveal much information about the underlying model. It often makes more sense to performpaired statistical testsacross multiple systems.NOTE:When resampling, the seed of thenumpy's random number generator (RNG)
is fixed to12345. If you want to relax this and set your own seed, you can
export the environment variableSACREBLEU_SEEDto an integer. Alternatively, you can exportSACREBLEU_SEED=Noneto skip initializing the RNG's seed and allow for non-deterministic
behavior.Paired Significance Tests for Multi System EvaluationIdeally, one would have access to many systems in cases such as (1) investigating
whether a newly added feature yields significantly different scores than the baseline or
(2) evaluating submissions for a particular shared task. SacreBLEU offers two different paired significance tests that are widely used in MT research.Paired bootstrap resampling (--paired-bs)This is an efficient implementation of the paperStatistical Significance Tests for Machine Translation Evaluationand is result-compliant with thereference Moses implementation. The number of bootstrap resamples can be changed with the--paired-bs-nflag and its default is 1000.When launched, paired bootstrap resampling will perform:Bootstrap resampling to estimate 95% CI for all systems and the baselineA significance test between thebaselineand eachsystemto compute ap-value.Paired approximate randomization (--paired-ar)Paired approximate randomization (AR) is another type of paired significance test that is claimed to be more accurate than paired bootstrap resampling when it comes to Type-I errors (Riezler and Maxwell III, 2005). Type-I errors indicate failures to reject the null hypothesis when it is true. In other words, AR should in theory be more robust to subtle changes across systems.Our implementation is verified to be result-compliant with theMulteval toolkitthat also uses paired AR test for pairwise comparison. The number of approximate randomization trials is set to 10,000 by default. This can be changed with the--paired-ar-nflag.Running the testsThefirst systemprovided to--input/-iwill be automatically taken as thebaseline systemagainst which you want to compareother systems.When--input/-iis used, the system output files will be automatically named according to the file paths. For the sake of simplicity, SacreBLEU will automatically discard thebaseline systemif it also appears amongstother systems. This is useful if you would like to run the tool by passing-i systems/baseline.txt systems/*.txt. Here, thebaseline.txtfile will not be also considered as a candidate system.Alternatively, you can also use a tab-separated input file redirected to SacreBLEU. In this case, the first column hypotheses will be taken as thebaseline system. However, this method isnot recommendedas it won't allow naming your systems in a human-readable way. It will instead enumerate the systems from 1 to N following the column order in the tab-separated input.On Linux and Mac OS X, you can launch the tests on multiple CPU's by passing the flag--paired-jobs N. IfN == 0, SacreBLEU will launch one worker for each pairwise comparison. IfN > 0,Nworker processes will be spawned. This feature will substantially speed up the runtime especially if you want theTERmetric to be computed.Example: Paired bootstrap resamplingIn the example below, we selectnewstest2017.LIUM-NMT.4900.en-deas the baseline and compare it to 4 other WMT17 submissions using paired bootstrap resampling. According to the results, the null hypothesis (i.e. the two systems being essentially the same) could not be rejected (at the significance level of 0.05) for the following comparisons:0.1 BLEU difference between the baseline and the online-B system (p = 0.3077)$ sacrebleu -t wmt17 -l en-de -i newstest2017.LIUM-NMT.4900.en-de newstest2017.online-* -m bleu chrf --paired-bs
╒════════════════════════════════════════════╤═════════════════════╤══════════════════════╕
│                                     System │  BLEU (μ ± 95% CI)  │  chrF2 (μ ± 95% CI)  │
╞════════════════════════════════════════════╪═════════════════════╪══════════════════════╡
│ Baseline: newstest2017.LIUM-NMT.4900.en-de │  26.6 (26.6 ± 0.6)  │  55.9 (55.9 ± 0.5)   │
├────────────────────────────────────────────┼─────────────────────┼──────────────────────┤
│              newstest2017.online-A.0.en-de │  20.8 (20.8 ± 0.6)  │  52.0 (52.0 ± 0.4)   │
│                                            │    (p = 0.0010)*    │    (p = 0.0010)*     │
├────────────────────────────────────────────┼─────────────────────┼──────────────────────┤
│              newstest2017.online-B.0.en-de │  26.7 (26.6 ± 0.7)  │  56.3 (56.3 ± 0.5)   │
│                                            │    (p = 0.3077)     │    (p = 0.0240)*     │
├────────────────────────────────────────────┼─────────────────────┼──────────────────────┤
│              newstest2017.online-F.0.en-de │  15.5 (15.4 ± 0.5)  │  49.3 (49.3 ± 0.4)   │
│                                            │    (p = 0.0010)*    │    (p = 0.0010)*     │
├────────────────────────────────────────────┼─────────────────────┼──────────────────────┤
│              newstest2017.online-G.0.en-de │  18.2 (18.2 ± 0.5)  │  51.6 (51.6 ± 0.4)   │
│                                            │    (p = 0.0010)*    │    (p = 0.0010)*     │
╘════════════════════════════════════════════╧═════════════════════╧══════════════════════╛

------------------------------------------------------------
Paired bootstrap resampling test with 1000 resampling trials
------------------------------------------------------------
 - Each system is pairwise compared to Baseline: newstest2017.LIUM-NMT.4900.en-de.
   Actual system score / bootstrap estimated true mean / 95% CI are provided for each metric.

 - Null hypothesis: the system and the baseline translations are essentially
   generated by the same underlying process. For a given system and the baseline,
   the p-value is roughly the probability of the absolute score difference (delta)
   or higher occurring due to chance, under the assumption that the null hypothesis is correct.

 - Assuming a significance threshold of 0.05, the null hypothesis can be rejected
   for p-values < 0.05 (marked with "*"). This means that the delta is unlikely to be attributed
   to chance, hence the system is significantly "different" than the baseline.
   Otherwise, the p-values are highlighted in red.

 - NOTE: Significance does not tell whether a system is "better" than the baseline but rather
   emphasizes the "difference" of the systems in terms of the replicability of the delta.

-----------------
Metric signatures
-----------------
 - BLEU       nrefs:1|bs:1000|seed:12345|case:mixed|eff:no|tok:13a|smooth:exp|version:2.0.0
 - chrF2      nrefs:1|bs:1000|seed:12345|case:mixed|eff:yes|nc:6|nw:0|space:no|version:2.0.0Example: Paired approximate randomizationLet's now run the paired approximate randomization test for the same comparison. According to the results, the findings are compatible with the paired bootstrap resampling test. However, the p-value for thebaseline vs. online-Bcomparison is much higher (0.8066) than the paired bootstrap resampling test.(Note thatthe AR test does not provide confidence intervals around the true mean as it does not perform bootstrap resampling.)$ sacrebleu -t wmt17 -l en-de -i newstest2017.LIUM-NMT.4900.en-de newstest2017.online-* -m bleu chrf --paired-ar
╒════════════════════════════════════════════╤═══════════════╤═══════════════╕
│                                     System │     BLEU      │     chrF2     │
╞════════════════════════════════════════════╪═══════════════╪═══════════════╡
│ Baseline: newstest2017.LIUM-NMT.4900.en-de │     26.6      │     55.9      │
├────────────────────────────────────────────┼───────────────┼───────────────┤
│              newstest2017.online-A.0.en-de │     20.8      │     52.0      │
│                                            │ (p = 0.0001)* │ (p = 0.0001)* │
├────────────────────────────────────────────┼───────────────┼───────────────┤
│              newstest2017.online-B.0.en-de │     26.7      │     56.3      │
│                                            │ (p = 0.8066)  │ (p = 0.0385)* │
├────────────────────────────────────────────┼───────────────┼───────────────┤
│              newstest2017.online-F.0.en-de │     15.5      │     49.3      │
│                                            │ (p = 0.0001)* │ (p = 0.0001)* │
├────────────────────────────────────────────┼───────────────┼───────────────┤
│              newstest2017.online-G.0.en-de │     18.2      │     51.6      │
│                                            │ (p = 0.0001)* │ (p = 0.0001)* │
╘════════════════════════════════════════════╧═══════════════╧═══════════════╛

-------------------------------------------------------
Paired approximate randomization test with 10000 trials
-------------------------------------------------------
 - Each system is pairwise compared to Baseline: newstest2017.LIUM-NMT.4900.en-de.
   Actual system score is provided for each metric.

 - Null hypothesis: the system and the baseline translations are essentially
   generated by the same underlying process. For a given system and the baseline,
   the p-value is roughly the probability of the absolute score difference (delta)
   or higher occurring due to chance, under the assumption that the null hypothesis is correct.

 - Assuming a significance threshold of 0.05, the null hypothesis can be rejected
   for p-values < 0.05 (marked with "*"). This means that the delta is unlikely to be attributed
   to chance, hence the system is significantly "different" than the baseline.
   Otherwise, the p-values are highlighted in red.

 - NOTE: Significance does not tell whether a system is "better" than the baseline but rather
   emphasizes the "difference" of the systems in terms of the replicability of the delta.

-----------------
Metric signatures
-----------------
 - BLEU       nrefs:1|ar:10000|seed:12345|case:mixed|eff:no|tok:13a|smooth:exp|version:2.0.0
 - chrF2      nrefs:1|ar:10000|seed:12345|case:mixed|eff:yes|nc:6|nw:0|space:no|version:2.0.0Using SacreBLEU from PythonFor evaluation, it may be useful to compute BLEU, chrF or TER from a Python script. The recommended
way of doing this is to use the object-oriented API, by creating an instance of themetrics.BLEUclass
for example:In[1]:fromsacrebleu.metricsimportBLEU,CHRF,TER...:...:refs=[# First set of references...:['The dog bit the man.','It was not unexpected.','The man bit him first.'],...:# Second set of references...:['The dog had bit the man.','No one was surprised.','The man had bitten the dog.'],...:]...:sys=['The dog bit the man.',"It wasn't surprising.",'The man had just bitten him.']In[2]:bleu=BLEU()In[3]:bleu.corpus_score(sys,refs)Out[3]:BLEU=48.5382.4/50.0/45.5/37.5(BP=0.943ratio=0.944hyp_len=17ref_len=18)In[4]:bleu.get_signature()Out[4]:nrefs:2|case:mixed|eff:no|tok:13a|smooth:exp|version:2.0.0In[5]:chrf=CHRF()In[6]:chrf.corpus_score(sys,refs)Out[6]:chrF2=59.73Variable Number of ReferencesLet's now remove the first reference sentence for the first system sentenceThe dog bit the man.by replacing it with eitherNoneor the empty string''.
This allows using a variable number of reference segments per hypothesis. Observe how the signature changes fromnrefs:2tonrefs:var:In[1]:fromsacrebleu.metricsimportBLEU,CHRF,TER...:...:refs=[# First set of references# 1st sentence does not have a ref here...:['','It was not unexpected.','The man bit him first.'],...:# Second set of references...:['The dog had bit the man.','No one was surprised.','The man had bitten the dog.'],...:]...:sys=['The dog bit the man.',"It wasn't surprising.",'The man had just bitten him.']In[2]:bleu=BLEU()In[3]:bleu.corpus_score(sys,refs)Out[3]:BLEU=29.4482.4/42.9/27.3/12.5(BP=0.889ratio=0.895hyp_len=17ref_len=19)In[4]:bleu.get_signature()Out[4]:nrefs:var|case:mixed|eff:no|tok:13a|smooth:exp|version:2.0.0Compatibility APIYou can also use the compatibility API that provides wrapper functions around the object-oriented API to
compute sentence-level and corpus-level BLEU, chrF and TER: (It should be noted that this API can be
removed in future releases)In[1]:importsacrebleu...:...:refs=[# First set of references...:['The dog bit the man.','It was not unexpected.','The man bit him first.'],...:# Second set of references...:['The dog had bit the man.','No one was surprised.','The man had bitten the dog.'],...:]...:sys=['The dog bit the man.',"It wasn't surprising.",'The man had just bitten him.']In[2]:sacrebleu.corpus_bleu(sys,refs)Out[2]:BLEU=48.5382.4/50.0/45.5/37.5(BP=0.943ratio=0.944hyp_len=17ref_len=18)LicenseSacreBLEU is licensed under theApache 2.0 License.CreditsThis was all Rico Sennrich's idea.
Originally written by Matt Post.
New features and ongoing support provided by Martin Popel (@martinpopel) and Ozan Caglayan (@ozancaglayan).If you use SacreBLEU, please cite the following:@inproceedings{post-2018-call,
  title = "A Call for Clarity in Reporting {BLEU} Scores",
  author = "Post, Matt",
  booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
  month = oct,
  year = "2018",
  address = "Belgium, Brussels",
  publisher = "Association for Computational Linguistics",
  url = "https://www.aclweb.org/anthology/W18-6319",
  pages = "186--191",
}Release Notes2.0.0 (2021-07-XX)Build: Add Windows and OS X testing to Travis CI.Improve documentation and type annotations.DropPython < 3.6support and migrate to f-strings.Relaxportalockerversion pinning, addregex, tabulate, numpydependencies.Drop input type manipulation throughisinstancechecks. If the user does not obey
to the expected annotations, exceptions will be raised. Robustness attempts lead to
confusions and obfuscated score errors in the past (#121)Variable # references per segment is supported for all metrics by default. It is
still only available through the API.Use colored strings in tabular outputs (multi-system evaluation mode) through
the help ofcoloramapackage.tokenizers: Add caching to tokenizers which seem to speed up things a bit.intltokenizer: Useregexmodule. Speed goes from ~4 seconds to ~0.6 seconds
for a particular test set evaluation. (#46)Signature: Formatting changed (mostly to remove '+' separator as it was
interfering with chrF++). The field separator is now '|' and key values
are separated with ':' rather than '.'.Signature: Boolean true / false values are shortened to yes / no.Signature: Number of references isvarif variable number of references is used.Signature: Add effective order (yes/no) to BLEU and chrF signatures.Metrics: Scale all metrics into the [0, 100] range (#140)Metrics API: Use explicit argument names and defaults for the metrics instead of
passing obscureargparse.Namespaceobjects.Metrics API: A base abstractMetricclass is introduced to guide further
metric development. This class defines the methods that should be implemented
in the derived classes and offers boilerplate methods for the common functionality.
A new metric implemented this way will automatically support significance testing.Metrics API: All metrics now receive an optionalreferencesargument at
initialization time to process and cache the references. Further evaluations
of different systems against the same references becomes faster this way
for example when using significance testing.BLEU: In case of no n-gram matches at all, skip smoothing and return 0.0 BLEU (#141).CHRF: Added multi-reference support, verified the scores against chrF++.py, added test case.CHRF: Added chrF+ support throughword_orderargument. Added test cases against chrF++.py.
Exposed it through the CLI (--chrf-word-order) (#124)CHRF: Add possibility to disable effective order smoothing (pass --chrf-eps-smoothing).
This way, the scores obtained are exactly the same as chrF++, Moses and NLTK implementations.
We keep the effective ordering as the default for compatibility, since this only
affects sentence-level scoring with very short sentences. (#144)CLI:--input/-ican now ingest multiple systems. For this reason, the positionalreferencesshould always preceed the-iflag.CLI: Allow modifying TER arguments through CLI. We still keep the TERCOM defaults.CLI: Prefix metric-specific arguments with --chrf and --ter. To maintain compatibility,
BLEU argument names are kept the same.CLI: Separate metric-specific arguments for clarity when--helpis printed.CLI: Added--format/-fflag. The single-system output mode is nowjsonby default.
If you want to keep the old text format persistently, you can exportSACREBLEU_FORMAT=textinto your
shell.CLI: For multi-system mode,jsonfalls back to plain text.latexoutput can only
be generated for multi-system mode.CLI: sacreBLEU now supports evaluating multiple systems for a given test set
in an efficient way. Through the use oftabulatepackage, the results are
nicely rendered into a plain text table, LaTeX, HTML or RST (cf. --format/-f argument).
The systems can be either given as a list of plain text files to-i/--inputor
as a tab-separated single stream redirected intoSTDIN. In the former case,
the basenames of the files will be automatically used as system names.Statistical tests: sacreBLEU now supports confidence interval estimation
through bootstrap resampling for single-system evaluation (--confidenceflag)
as well as paired bootstrap resampling (--paired-bs) and paired approximate
randomization tests (--paired-ar) when evaluating multiple systems (#40 and #78).1.5.1 (2021-03-05)Fix extraction error for WMT18 extra test sets (test-ts) (#142)Validation and test datasets are added for multilingual TEDx1.5.0 (2021-01-15)Fix an assertion error in chrF (#121)Add missing__repr__()methods for BLEU and TERTER: Fix exception when--shortis used (#131)Pin Mecab version to 1.0.3 for Python 3.5 support[API Change]: Default value forfloorsmoothing is now 0.1 instead of 0.[API Change]:sacrebleu.sentence_bleu()now uses theexpsmoothing method,
exactly the same as the CLI's --sentence-level behavior. This was mainly done
to make two methods behave the same.Add smoothing value to BLEU signature (#98)dataset: Fix IWSLT links (#128)Allow variable number of references for BLEU (only via API) (#130).
Thanks to Ondrej Dusek (@tuetschek)1.4.14 (2020-09-13)Added character-based tokenization (-tok char).
Thanks to Christian Federmann.Added TER (-m ter). Thanks to Ales Tamchyna! (fixes #90)Allow calling the script as a standalone utility (fixes #86)Fix type annotation issues (fixes #100) and mark sacrebleu as supporting mypyAdded WMT20 robustness test sets:wmt20/robust/set1 (en-ja, en-de)wmt20/robust/set2 (en-ja, ja-en)wmt20/robust/set3 (de-en)1.4.13 (2020-07-30)Added WMT20 newstest test sets (#103)Make mecab3-python an extra dependency, adapt code to new mecab3-python
This fixes the recent Windows installation issues as well (#104)
Japanese support should now be explicitly installed through sacrebleu[ja] package.Fix return type annotation of corpus_bleu()Improve sentence_score's documentation, do not allow single ref string (#98)1.4.12 (2020-07-03)Fix a deployment bug (#96)1.4.11 (2020-07-03)Added Multi30k multimodal MT test set metadataRefactored all tokenizers into respective classes (fixes #85)Refactored all metrics into respective classesMoved utility functions intoutils.pyImplemented signatures usingBLEUSignatureandCHRFSignatureclassesSimplified checking of Chinese characters (fixes #5)Unified common regexp tokenization codes for tokenizers (fixes #27)Fixed --detail failing when no test sets are providedFixed multi-reference BLEU failing when tab-delimited reference stream is usedRemoved lowercase option for ChrF which was not functional (#85)Simplified ChrF and used the same I/O logic as BLEU to allow for future
multi-reference readingAdded score regression tests for chrF using reference chrF++ implementationAdded multi-reference & tokenizer & signature tests1.4.10 (2020-05-30)Fixed bug in signature with mecab tokenizerCleaned up deprecation warnings (thanks to Karthikeyan Singaravelan @tirkarthi)Now only lists the externaltypingmodule as a dependency for Python<= 3.4, as it was integrated in the standard
library in Python 3.5 (thanks to Erwan de Lépinau @ErwanDL).Added LICENSE to pypi (thanks to Mark Harfouche @hmaarrfk)1.4.9 (2020-04-30)Changedget_available_testsets()to return a listRemove Japanese MeCab tokenizer from requirements.
(Must be installed manually to avoid Windows incompatibility).
Many thanks to Makoto Morishita (@MorinoseiMorizo).1.4.8 (2020-04-26)Added to API:get_source_file()get_reference_files()get_available_testsets()get_langpairs_for_testset()Some internal refactoringFixed descriptions of some WMT19/google test setsAdded API test case (test/test_apy.py)1.4.7 (2020-04-19)Added Google's extra wmt19/en-de refs (-t wmt19/google/{ar,arp,hqall,hqp,hqr,wmtp})
(Freitag, Grangier, & Caswell
BLEU might be Guilty but References are not Innocenthttps://arxiv.org/abs/2004.06063)Restored SACREBLEU_DIR and smart_open to exports (thanks to Thomas Liao @tholiao)1.4.6 (2020-03-28)Large internal reorganization as a module (thanks to Thamme Gowda @thammegowda)1.4.5 (2020-03-28)Added Japanese MeCab tokenizer (-tok ja-mecab) (thanks to Makoto Morishita @MorinoseiMorizo)Added wmt20/dev test sets (thanks to Martin Popel @martinpopel)1.4.4 (2020-03-10)Smoothing changes (Sebastian Nickels @sn1c)Fixed bug that only applied smoothing to n-grams for n > 2Added default smoothing values for methods "floor" (0) and "add-k" (1)--listnow returns a list of all language pairs for a task when combined with-t(e.g.,sacrebleu -t wmt19 --list)added missing languages for IWSLT17Minor code improvements (Thomas Liao @tholiao)1.4.3 (2019-12-02)Bugfix: handling of result object for CHRFImproved API example1.4.2 (2019-10-11)Tokenization variant omitted from the chrF signature; it is relevant only for BLEU (thanks to Martin Popel)Bugfix: call to sentence_bleu (thanks to Rachel Bawden)Documentation example for Python API (thanks to Vlad Lyalin)Calls to corpus_chrf and sentence_chrf now return a an object instead of a float (use result.score)1.4.1 (2019-09-11)Added sentence-level scoring via -sl (--sentence-level)1.4.0 (2019-09-10)Many thanks to Martin Popel for all the changes below!Added evaluation on concatenated test sets (e.g.,-t wmt17,wmt18).
Works as long as they all have the same language pair.Addedsacrebleu --origlang(both for evaluation on a subset and for--echo).
Note that while echoing prints just the subset, evaluation expects the complete
test set (and just skips the irrelevant parts).Addedsacrebleu --detailfor breakdown by domain-specific subsets of the test sets.
(Available for WMT19).Minor changesImproved display ofsacrebleu -hAddedsacrebleu --listCode refactoringDocumentation and tests updatesFixed a race condition bug (os.makedirs(outdir, exist_ok=True)instead ofif os.path.exists)1.3.7 (2019-07-12)Lazy loading of regexes cuts import time from ~1s to nearly nothing (thanks, @louismartin!)Added a simple (non-atomic) lock on downloadingCan now read multiple refs from a single tab-delimited file.
You need to pass--num-refs Nto tell it to run the split.
Only works with a single reference file passed from the command line.1.3.6 (2019-06-10)Removed another f-string for Python 3.5 compatibility1.3.5 (2019-06-07)Restored Python 3.5 compatibility1.3.4 (2019-05-28)Added MTNT 2019 test setsAdded a BLEU object1.3.3 (2019-05-08)Added WMT'19 test sets1.3.2 (2018-04-24)Bugfix in test case (thanks to Adam Roberts, @adarob)Passing smoothing method throughsentence_bleu1.3.1 (2019-03-20)Added another smoothing approach (add-k) and a command-line option for choosing the smoothing method
(--smooth exp|floor|add-n|none) and the associated value (--smooth-value), when relevant.Changed interface to some functions (backwards incompatible)'smooth' is now 'smooth_method''smooth_floor' is now 'smooth_value'1.2.21 (19 March 2019)Ctrl-M characters are now treated as normal characters, previously treated as newline.1.2.20 (28 February 2018)Tokenization now defaults to "zh" when language pair is known1.2.19 (19 February 2019)Updated checksum for wmt19/dev (seems to have changed)1.2.18 (19 February 2019)Fixed checksum for wmt17/dev (copy-paste error)1.2.17 (6 February 2019)Added kk-en and en-kk to wmt19/dev1.2.16 (4 February 2019)Added gu-en and en-gu to wmt19/dev1.2.15 (30 January 2019)Added MD5 checksumming of downloaded files for all datasets.1.2.14 (22 January 2019)Added mtnt1.1/train mtnt1.1/valid mtnt1.1/test data fromMTNT1.2.13 (22 January 2019)Added 'wmt19/dev' task for 'lt-en' and 'en-lt' (development data for new tasks).Added MD5 checksum for downloaded tarballs.1.2.12 (8 November 2018)Now outputs only only digit after the decimal1.2.11 (29 August 2018)Added a function for sentence-level, smoothed BLEU1.2.10 (23 May 2018)Added wmt18 test set (with references)1.2.9 (15 May 2018)Added zh-en, en-zh, tr-en, and en-tr datasets for wmt18/test-ts1.2.8 (14 May 2018)Added wmt18/test-ts, the test sources (only) forWMT18Moved README out ofsacrebleu.pyand the CHANGELOG into a separate file1.2.7 (10 April 2018)fixed another locale issue (with --echo)grudgingly enabled-tok nonefrom the command line1.2.6 (22 March 2018)added wmt17/ms (Microsoft'sadditional ZH-EN references).
Trysacrebleu -t wmt17/ms --cite.--echo refnow pastes together all references, if there is more than one1.2.5 (13 March 2018)added wmt18/dev datasets (en-et and et-en)fixed logic with --forcelocale-independent installationadded "--echo both" (tab-delimited)1.2.3 (28 January 2018)metrics (-m) are now printed in the order requestedchrF now prints a version string (including the beta parameter, importantly)attempt to remove dependence on locale setting1.2 (17 January 2018)added the chrF metric (-m chrfor-m bleu chrffor both)
See 'CHRF: character n-gram F-score for automatic MT evaluation' by Maja Popovic (WMT 2015)
[http://www.statmt.org/wmt15/pdf/WMT49.pdf]added IWSLT 2017 test and tuning sets for DE, FR, and ZH
(Thanks to Mauro Cettolo and Marcello Federico).added--citeto produce the citation for easy inclusion in papersadded--input(-i) to set input to a file instead of STDINremoved accent mark after objection from UN official1.1.7 (27 November 2017)corpus_bleu() now raises an exception if input streams are different lengthsthanks to Martin Popel for:small bugfix in tokenization_13a (not affecting WMT references)adding--tok intl(international tokenization)added wmt17/dev and wmt17/dev sets (for languages intro'd those years)1.1.6 (15 November 2017)bugfix for tokenization warning1.1.5 (12 November 2017)added -b option (only output the BLEU score)removed fi-en from list of WMT16/17 systems with more than one referenceadded WMT16/tworefs and WMT17/tworefs for scoring with both en-fi references1.1.4 (10 November 2017)added effective order for sentence-level BLEU computationadded unit tests from sockeye1.1.3 (8 November 2017).Factored code a bit to facilitate API:compute_bleu: works from raw statscorpus_bleu for use from the command lineraw_corpus_bleu: turns off tokenization, command-line sanity checks, floor smoothingSmoothing (type 'exp', now the default) fixed to produce mteval-v13a.pl resultsAdded 'floor' smoothing (adds 0.01 to 0 counts, more versatile via API), 'none' smoothing (via API)Small bugfixes, windows compatibility (H/T Christian Federmann)1.0.3 (4 November 2017).Contributions from Christian Federmann:Added explicit support for encodingFixed Windows supportBugfix in handling reference length with multiple refsversion 1.0.1 (1 November 2017).Small bugfix affecting some versions of Python.Code reformatting due to Ozan Çağlayan.version 1.0 (23 October 2017).Support for WMT 2008--2017.Single tokenization (v13a) with lowercase fix (proper lower() instead of just A-Z).Chinese tokenization.Tested to match all WMT17 scores on all arcs.