Egothor/Radixor
2
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
Files
75d31aeda5e2ab8c28dfeb3b0eb5b0933fab9ae0
Radixor/docs/benchmarks/languages/french.md
Leo Galambos b29699b763 Refresh multilingual benchmarks and fix overlapping gold evaluation 
Recompute published benchmark results for all default language models,
exclude Polish Polimorf, add Hebrew documentation, and record the current
benchmark environment. Evaluate repeated surface forms as an overlapping
gold cover and publish only applicable metrics for candidate policies.
2026-07-23 17:06:41 +02:00

25 KiB
Raw Blame History

French Stemmer Benchmarks

This page reports same-language stemming benchmarks for French. Accuracy is listed first because speed without root agreement is not enough to interpret stemmer quality.

All speed values are environment-specific and were measured on the hardware and JVM listed in the benchmark overview. Speed benchmark operations process changed dictionary tokens only. Accuracy uses the complete Radixor dictionary for the language.

Radixor must not be read as simply "slower" when a narrow competitor has a lower timing row. In these tables Radixor is the quality-oriented baseline: its exact-root accuracy is typically close to 100%, while many faster rule-based, light, minimal, or possessive filters reach that speed by doing much less linguistic work and often score far lower in All exact and Changed exact. The Radixor rows in this benchmark refresh use the contracted compiled patch trie: compilation collapses uniform patch-command subtrees into accepting leaves, reducing hot lookup depth while preserving the preferred stemming result measured by the accuracy pass. The EnglishRadixorDictionaryCoverageBenchmark table shows the resulting quality/speed envelope explicitly. The same interpretation applies to this language page: speed rows must be read together with the accuracy table above them.

Dictionary Corpus

Model ID Model version Language Dictionary rows Complete quality tokens Already-root tokens Changed speed tokens
fr-fr-default 1.0.0 FR_FR 59,240 474,110 108,141 365,969

Radixor Patch Command Distribution

Radixor stores the preferred transformation for each normalized dictionary word form as a compiled patch command. This distribution shows which runtime command class is selected by the trained trie for the complete default-model dictionary. The total number of preferred patch commands analyzed for this language is 474,110.

Command class Meaning Word forms Share
AppendCharacterCommand Appends one character to the end of the word form. 10,082 2.127%
BackwardCompoundCommand Applies a multi-step backward patch made from skip, delete, insert, and replace operations. 184,521 38.919%
DeleteSuffixCommand Deletes one or more trailing characters from the word form. 154,760 32.642%
PreserveCommand Returns the word form unchanged because it already matches the preferred root. 110,933 23.398%
ReplaceLastCharacterCommand Replaces the final character of the word form. 13,814 2.914%

Accuracy

Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.

Stemmer All exact Changed exact Root preserved Note
Radixor 94.831% 94.859% 94.734% Full Radixor dictionary patch-command stemmer.
Lucene HunspellStemFilter 68.923% 63.617% 86.876% Benchmark-only French Hunspell dictionary compared via Lucene HunspellStemFilter.
Lucene FrenchMinimalStemFilter 11.472% 6.236% 29.192% Minimal suffix reducer; narrow baseline, not a full stemmer.
Lucene SnowballFilter 8.551% 5.183% 19.952% Lucene TokenFilter integration path around the Snowball algorithm.
Official Snowball direct 8.462% 5.067% 19.952% Official Snowball generated Java stemmer; rule-based suffix algorithm.
Lucene FrenchLightStemFilter 6.377% 3.965% 14.540% Light suffix stemmer; intentionally narrower than a dictionary-derived stemmer.

Speed

Speed uses JMH average time, 5 warmup iterations, 10 measurement iterations, 3 independent forks, and 1 thread. Relative factor is computed against the single Radixor row on this language page. Values below 1.000 are faster than that Radixor baseline; values above 1.000 are slower.

Stemmer Benchmark method Score ms/op Error ms ns/token Relative vs Radixor Note
Radixor frenchRadixor 49.340 0.986 134.8 1.000 Full Radixor dictionary patch-command stemmer.
Lucene HunspellStemFilter luceneHunspellStemFilter 1781.070 43.544 4866.7 36.098 Benchmark-only French Hunspell dictionary compared via Lucene HunspellStemFilter.
Lucene FrenchMinimalStemFilter frenchLuceneFrenchMinimalStemFilter 19.093 0.681 52.2 0.387 Minimal French suffix reducer; narrow baseline.
Lucene FrenchLightStemFilter frenchLuceneFrenchLightStemFilter 29.553 0.465 80.8 0.599 Light French suffix stemmer.
Official Snowball direct snowballDirect[FRENCH] 121.376 0.865 331.7 2.460 Official Snowball generated Java stemmer; direct API.
Lucene SnowballFilter luceneSnowballFilter[FRENCH] 126.574 4.671 345.9 2.565 Lucene TokenFilter path around Snowball; includes TokenStream overhead.

Interpretation Notes

  • Radixor is a dictionary-derived patch-command stemmer. Its quality depends on the language resource used to train the compiled trie.
  • Light, minimal, plural, and possessive filters are narrow baselines. They can be fast because they intentionally perform less linguistic work.
  • Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
  • Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
  • Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.

Stemming Quality

Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language FR_FR using the complete validated stemming-quality result matrix. Every distinct surface form is one evaluated item and can belong to several dictionary groups. Two forms are a positive pair when their group-membership sets intersect and a negative pair when those sets are disjoint. A pair shared through several groups is counted once. Exact equality with a predetermined lemma is not required.

ALL_WORDS includes every valid group and its original forms. LOWERCASE_GROUPS_ONLY excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. Download the complete machine-readable result snapshot.

Evaluation Scope and Key Findings

The default model is fr-fr-default, loaded from classpath resource org/egothor/stemmer/models/fr-fr-default/stemmer.gz. The following findings compare only deterministic PRIMARY_OUTPUT rows over identical included groups; candidate policies are reported separately as capability analyses.

  • ALL_WORDS: Radixor ranks first by balanced accuracy at 0.958627 among 6 deterministic stemmers. The runner-up is SNOWBALL FRENCH DIRECT at 0.848662, a difference of 0.109965. This rank does not imply leadership in throughput or every secondary metric.
  • LOWERCASE_GROUPS_ONLY: Radixor ranks first by balanced accuracy at 0.958856 among 6 deterministic stemmers. The runner-up is SNOWBALL FRENCH DIRECT at 0.848826, a difference of 0.110031. This rank does not imply leadership in throughput or every secondary metric.

ALL_WORDS

This mode contains 10 result rows, 6 evaluated stemmers, and 3 output policies. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. PRIMARY_OUTPUT and ALL_CANDIDATES rankings are ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. ANY_CANDIDATE has no single rank metric and is listed alphabetically. Balanced accuracy is a navigation metric, not a universally authoritative quality score.

PRIMARY_OUTPUT ranking

Rank Stemmer Balanced accuracy Over-stemming (OI) Under-stemming (UI)
1 Radixor 0.958627 <0.000001% 8.274665%
2 SNOWBALL FRENCH DIRECT 0.848662 0.001338% 30.266309%
3 SNOWBALL FRENCH LUCENE FILTER 0.848404 0.001345% 30.317815%
4 HUNSPELL FRENCH LUCENE FILTER 0.816824 0.000540% 36.634583%
5 FRENCH LUCENE FRENCH LIGHT STEM FILTER 0.518478 0.000187% 96.304159%
6 FRENCH LUCENE FRENCH MINIMAL STEM FILTER 0.516784 0.000083% 96.643216%
Classification metrics
Rank Stemmer Output policy Precision Recall Specificity Balanced accuracy Pairwise accuracy Error rate
1 Radixor PRIMARY_OUTPUT 0.999994 0.917253 1.000000 0.958627 0.999995 0.000005
2 SNOWBALL FRENCH DIRECT PRIMARY_OUTPUT 0.774195 0.697337 0.999987 0.848662 0.999967 0.000033
3 SNOWBALL FRENCH LUCENE FILTER PRIMARY_OUTPUT 0.773169 0.696822 0.999987 0.848404 0.999967 0.000033
4 HUNSPELL FRENCH LUCENE FILTER PRIMARY_OUTPUT 0.885315 0.633654 0.999995 0.816824 0.999970 0.000030
5 FRENCH LUCENE FRENCH LIGHT STEM FILTER PRIMARY_OUTPUT 0.565022 0.036958 0.999998 0.518478 0.999935 0.000065
6 FRENCH LUCENE FRENCH MINIMAL STEM FILTER PRIMARY_OUTPUT 0.726387 0.033568 0.999999 0.516784 0.999936 0.000064
Pair-relation metrics
Rank Stemmer Output policy F0.5 F1 F2 Jaccard Fowlkes–Mallows MCC
1 Radixor PRIMARY_OUTPUT 0.982273 0.956838 0.932688 0.917248 0.957731 0.957728
2 SNOWBALL FRENCH DIRECT PRIMARY_OUTPUT 0.757497 0.733759 0.711463 0.579478 0.734761 0.734745
3 SNOWBALL FRENCH LUCENE FILTER PRIMARY_OUTPUT 0.756590 0.733013 0.710861 0.578548 0.734003 0.733987
4 HUNSPELL FRENCH LUCENE FILTER PRIMARY_OUTPUT 0.820168 0.738637 0.671850 0.585587 0.748988 0.748975
5 FRENCH LUCENE FRENCH LIGHT STEM FILTER PRIMARY_OUTPUT 0.146469 0.069379 0.045455 0.035936 0.144507 0.144495
6 FRENCH LUCENE FRENCH MINIMAL STEM FILTER PRIMARY_OUTPUT 0.141655 0.064170 0.041481 0.033149 0.156151 0.156143
Raw pair counts
Rank Stemmer Output policy TP FP FN TN Over error / possible Under error / possible
1 Radixor PRIMARY_OUTPUT 4925833 29 444366 81606871827 29 / 81606871856 444366 / 5370199
2 SNOWBALL FRENCH DIRECT PRIMARY_OUTPUT 3744838 1092238 1625361 81605779618 1092238 / 81606871856 1625361 / 5370199
3 SNOWBALL FRENCH LUCENE FILTER PRIMARY_OUTPUT 3742072 1097843 1628127 81605774013 1097843 / 81606871856 1628127 / 5370199
4 HUNSPELL FRENCH LUCENE FILTER PRIMARY_OUTPUT 3402849 440809 1967350 81606431047 440809 / 81606871856 1967350 / 5370199
5 FRENCH LUCENE FRENCH LIGHT STEM FILTER PRIMARY_OUTPUT 198474 152794 5171725 81606719062 152794 / 81606871856 5171725 / 5370199
6 FRENCH LUCENE FRENCH MINIMAL STEM FILTER PRIMARY_OUTPUT 180266 67902 5189933 81606803954 67902 / 81606871856 5189933 / 5370199

ANY_CANDIDATE oracle bounds

These results are measured, not missing. ANY_CANDIDATE answers two separate optimistic questions for each pair: a gold-related pair avoids under-stemming when the candidate sets intersect, while a gold-negative pair avoids over-stemming when some non-colliding candidate selection exists. The oracle may choose a different candidate for the same word in different pairs. Consequently, these decisions do not form one globally realizable predicted relation or one TP/FP/FN/TN confusion matrix. Balanced accuracy, F-scores, Jaccard, Fowlkes–Mallows, and MCC are therefore mathematically not applicable, rather than unknown.

Stemmer Optimistic over-stemming (OI) Optimistic under-stemming (UI)
Radixor 0.000000% 0.004320%
HUNSPELL FRENCH LUCENE FILTER 0.000539% 33.189869%
Oracle-bound pair counts
Stemmer Unavoidable over errors / gold-negative pairs Unrepairable under errors / gold-related pairs
Radixor 0 / 81606871856 232 / 5370199
HUNSPELL FRENCH LUCENE FILTER 439665 / 81606871856 1782362 / 5370199

ALL_CANDIDATES ranking

Rank Stemmer Balanced accuracy Over-stemming (OI) Under-stemming (UI)
1 Radixor 0.999978 0.000003% 0.004320%
2 HUNSPELL FRENCH LUCENE FILTER 0.834048 0.000614% 33.189869%
Classification metrics
Rank Stemmer Output policy Precision Recall Specificity Balanced accuracy Pairwise accuracy Error rate
1 Radixor ALL_CANDIDATES 0.999571 0.999957 1.000000 0.999978 1.000000 0.000000
2 HUNSPELL FRENCH LUCENE FILTER ALL_CANDIDATES 0.877537 0.668101 0.999994 0.834048 0.999972 0.000028
Pair-relation metrics
Rank Stemmer Output policy F0.5 F1 F2 Jaccard Fowlkes–Mallows MCC
1 Radixor ALL_CANDIDATES 0.999648 0.999764 0.999880 0.999528 0.999764 0.999764
2 HUNSPELL FRENCH LUCENE FILTER ALL_CANDIDATES 0.825765 0.758630 0.701590 0.611123 0.765691 0.765678
Raw pair counts
Rank Stemmer Output policy TP FP FN TN Over error / possible Under error / possible
1 Radixor ALL_CANDIDATES 5369967 2303 232 81606869553 2303 / 81606871856 232 / 5370199
2 HUNSPELL FRENCH LUCENE FILTER ALL_CANDIDATES 3587837 500695 1782362 81606371161 500695 / 81606871856 1782362 / 5370199

Multi-output analysis

Alternative candidates are capability analyses, not replacements for the deterministic comparison.

Stemmer Under pairs repaired Best-case over pairs avoided All-candidate collisions added Multi-candidate forms Multi-candidate share Maximum candidates Total candidate assignments
Radixor 444134 29 2274 21844 5.406783% 56 427440
HUNSPELL FRENCH LUCENE FILTER 184988 1144 59886 8230 2.037073% 4 412364

LOWERCASE_GROUPS_ONLY

This mode contains 10 result rows, 6 evaluated stemmers, and 3 output policies. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. PRIMARY_OUTPUT and ALL_CANDIDATES rankings are ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. ANY_CANDIDATE has no single rank metric and is listed alphabetically. Balanced accuracy is a navigation metric, not a universally authoritative quality score.

PRIMARY_OUTPUT ranking

Rank Stemmer Balanced accuracy Over-stemming (OI) Under-stemming (UI)
1 Radixor 0.958856 <0.000001% 8.228703%
2 SNOWBALL FRENCH DIRECT 0.848826 0.001356% 30.233460%
3 SNOWBALL FRENCH LUCENE FILTER 0.848580 0.001353% 30.282729%
4 HUNSPELL FRENCH LUCENE FILTER 0.816702 0.000540% 36.658999%
5 FRENCH LUCENE FRENCH LIGHT STEM FILTER 0.518338 0.000181% 96.332173%
6 FRENCH LUCENE FRENCH MINIMAL STEM FILTER 0.516654 0.000076% 96.669051%
Classification metrics
Rank Stemmer Output policy Precision Recall Specificity Balanced accuracy Pairwise accuracy Error rate
1 Radixor PRIMARY_OUTPUT 1.000000 0.917713 1.000000 0.958856 0.999995 0.000005
2 SNOWBALL FRENCH DIRECT PRIMARY_OUTPUT 0.774357 0.697665 0.999986 0.848826 0.999966 0.000034
3 SNOWBALL FRENCH LUCENE FILTER PRIMARY_OUTPUT 0.774620 0.697173 0.999986 0.848580 0.999966 0.000034
4 HUNSPELL FRENCH LUCENE FILTER PRIMARY_OUTPUT 0.886736 0.633410 0.999995 0.816702 0.999970 0.000030
5 FRENCH LUCENE FRENCH LIGHT STEM FILTER PRIMARY_OUTPUT 0.575115 0.036678 0.999998 0.518338 0.999934 0.000066
6 FRENCH LUCENE FRENCH MINIMAL STEM FILTER PRIMARY_OUTPUT 0.746071 0.033309 0.999999 0.516654 0.999935 0.000065
Pair-relation metrics
Rank Stemmer Output policy F0.5 F1 F2 Jaccard Fowlkes–Mallows MCC
1 Radixor PRIMARY_OUTPUT 0.982383 0.957091 0.933069 0.917713 0.957973 0.957971
2 SNOWBALL FRENCH DIRECT PRIMARY_OUTPUT 0.757699 0.734013 0.711764 0.579795 0.735012 0.734995
3 SNOWBALL FRENCH LUCENE FILTER PRIMARY_OUTPUT 0.757784 0.733859 0.711398 0.579603 0.734877 0.734860
4 HUNSPELL FRENCH LUCENE FILTER PRIMARY_OUTPUT 0.821061 0.738965 0.671794 0.585999 0.749445 0.749431
5 FRENCH LUCENE FRENCH LIGHT STEM FILTER PRIMARY_OUTPUT 0.146117 0.068959 0.045128 0.035711 0.145238 0.145227
6 FRENCH LUCENE FRENCH MINIMAL STEM FILTER PRIMARY_OUTPUT 0.141311 0.063772 0.041177 0.032936 0.157643 0.157634
Raw pair counts
Rank Stemmer Output policy TP FP FN TN Over error / possible Under error / possible
1 Radixor PRIMARY_OUTPUT 4915501 1 440750 80279496864 1 / 80279496865 440750 / 5356251
2 SNOWBALL FRENCH DIRECT PRIMARY_OUTPUT 3736871 1088903 1619380 80278407962 1088903 / 80279496865 1619380 / 5356251
3 SNOWBALL FRENCH LUCENE FILTER PRIMARY_OUTPUT 3734232 1086494 1622019 80278410371 1086494 / 80279496865 1622019 / 5356251
4 HUNSPELL FRENCH LUCENE FILTER PRIMARY_OUTPUT 3392703 433354 1963548 80279063511 433354 / 80279496865 1963548 / 5356251
5 FRENCH LUCENE FRENCH LIGHT STEM FILTER PRIMARY_OUTPUT 196458 145140 5159793 80279351725 145140 / 80279496865 5159793 / 5356251
6 FRENCH LUCENE FRENCH MINIMAL STEM FILTER PRIMARY_OUTPUT 178414 60724 5177837 80279436141 60724 / 80279496865 5177837 / 5356251

ANY_CANDIDATE oracle bounds

These results are measured, not missing. ANY_CANDIDATE answers two separate optimistic questions for each pair: a gold-related pair avoids under-stemming when the candidate sets intersect, while a gold-negative pair avoids over-stemming when some non-colliding candidate selection exists. The oracle may choose a different candidate for the same word in different pairs. Consequently, these decisions do not form one globally realizable predicted relation or one TP/FP/FN/TN confusion matrix. Balanced accuracy, F-scores, Jaccard, Fowlkes–Mallows, and MCC are therefore mathematically not applicable, rather than unknown.

Stemmer Optimistic over-stemming (OI) Optimistic under-stemming (UI)
Radixor 0.000000% 0.000000%
HUNSPELL FRENCH LUCENE FILTER 0.000539% 33.211718%
Oracle-bound pair counts
Stemmer Unavoidable over errors / gold-negative pairs Unrepairable under errors / gold-related pairs
Radixor 0 / 80279496865 0 / 5356251
HUNSPELL FRENCH LUCENE FILTER 432307 / 80279496865 1778903 / 5356251

ALL_CANDIDATES ranking

Rank Stemmer Balanced accuracy Over-stemming (OI) Under-stemming (UI)
1 Radixor 1.000000 <0.000001% 0.000000%
2 HUNSPELL FRENCH LUCENE FILTER 0.833938 0.000614% 33.211718%
Classification metrics
Rank Stemmer Output policy Precision Recall Specificity Balanced accuracy Pairwise accuracy Error rate
1 Radixor ALL_CANDIDATES 0.999986 1.000000 1.000000 1.000000 1.000000 0.000000
2 HUNSPELL FRENCH LUCENE FILTER ALL_CANDIDATES 0.878983 0.667883 0.999994 0.833938 0.999972 0.000028
Pair-relation metrics
Rank Stemmer Output policy F0.5 F1 F2 Jaccard Fowlkes–Mallows MCC
1 Radixor ALL_CANDIDATES 0.999989 0.999993 0.999997 0.999986 0.999993 0.999993
2 HUNSPELL FRENCH LUCENE FILTER ALL_CANDIDATES 0.826722 0.759029 0.701582 0.611641 0.766197 0.766184
Raw pair counts
Rank Stemmer Output policy TP FP FN TN Over error / possible Under error / possible
1 Radixor ALL_CANDIDATES 5356251 75 0 80279496790 75 / 80279496865 0 / 5356251
2 HUNSPELL FRENCH LUCENE FILTER ALL_CANDIDATES 3577348 492522 1778903 80279004343 492522 / 80279496865 1778903 / 5356251

Multi-output analysis

Alternative candidates are capability analyses, not replacements for the deterministic comparison.

Stemmer Under pairs repaired Best-case over pairs avoided All-candidate collisions added Multi-candidate forms Multi-candidate share Maximum candidates Total candidate assignments
Radixor 440750 1 74 20611 5.143594% 56 422336
HUNSPELL FRENCH LUCENE FILTER 184645 1047 59168 8194 2.044860% 4 409028

Output Policies and Metric Definitions

Each distinct surface form is one item and may belong to several gold groups. Two forms are gold-related when their membership sets intersect; a relation shared by several groups is counted once. PRIMARY_OUTPUT uses one deterministic stem per form. ANY_CANDIDATE is an optimistic oracle-assisted pairwise upper bound: a gold-related pair succeeds when candidates intersect, while a gold-negative pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and does not define one confusion matrix. ALL_CANDIDATES activates every returned candidate; forms are related when candidate sets intersect.

For PRIMARY_OUTPUT and ALL_CANDIDATES, TP = underPossiblePairs - underErrorPairs, FN = underErrorPairs, FP = overErrorPairs, and TN = overPossiblePairs - overErrorPairs. ANY_CANDIDATE publishes only its separate oracle-assisted under/over bounds; confusion-derived metrics are mathematically inapplicable and are not presented in its language-page section. Their machine-readable CSV fields remain empty. Undefined metric denominators in otherwise applicable policies are rendered as n/a.

  • Under-stemming rate (Paice UI): FN / (TP + FN), the false-negative rate over gold-related pairs.
  • Over-stemming rate (Paice OI): FP / (TN + FP), the false-positive rate over gold-negative pairs.
  • Pairwise precision: TP / (TP + FP), the fraction of predicted conflations that are gold-standard positive pairs.
  • Pairwise recall: TP / (TP + FN), the fraction of gold-standard positive pairs successfully connected.
  • Pairwise specificity: TN / (TN + FP), the fraction of gold-negative pairs correctly separated.
  • Balanced accuracy: (recall + specificity) / 2. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
  • Pairwise F-beta: ((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP). F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
  • MCC: (TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN)). It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
  • Jaccard index: TP / (TP + FP + FN).
  • Fowlkes–Mallows index: sqrt(precision * recall).
  • Pairwise accuracy: (TP + TN) / (TP + TN + FP + FN). It can be dominated by true-negative cross-group pairs.
  • Pairwise error rate: (FP + FN) / (TP + TN + FP + FN).

Standard ARI, homogeneity, completeness, V-measure, and NMI are not calculated: their usual contingency-table definitions require an exclusive gold partition, while this gold standard is an overlapping cover.

Provenance

  • Authoritative source: docs/benchmarks/data/stemming-quality.csv
  • Source SHA-256: edf16b07be8a535943ddf37caeb8807755c95e9e1fb13244145f28be74b491d8
  • Evaluation command: ./gradlew stemmingQuality --no-daemon
  • Dictionary language: FR_FR
  • Processing modes: ALL_WORDS, LOWERCASE_GROUPS_ONLY
  • Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and gradle.lockfile
  • Model ID, version, and SHA-256: recorded in every CSV row
  • Run date, core source state, JDK, operating system, and hardware: recorded on the benchmark environment page
Reference in New Issue View Git Blame Copy Permalink