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moderation evaluation

Threshold 0.84 (calibrated on the validation split, objective macro_f1). The config default of 0.5 scored 0.923 against 0.931 for the calibrated value, on validation. Every table below is on test, at the calibrated threshold.

Per language

Language Support P R F1 Notes
bg Bulgarian 60 1.000 0.967 0.983
cs Czech 60 1.000 1.000 1.000
da Danish 60 1.000 0.983 0.992
de German 60 1.000 0.983 0.992
el Greek 60 1.000 1.000 1.000
en English 59 0.983 1.000 0.992
es Spanish 59 1.000 0.983 0.991
et Estonian 60 1.000 1.000 1.000
fi Finnish 60 1.000 1.000 1.000
fr French 59 1.000 1.000 1.000
ga Irish 60 0.952 0.983 0.967
hr Croatian 58 0.983 1.000 0.991
hu Hungarian 60 1.000 1.000 1.000
it Italian 59 1.000 1.000 1.000
lt Lithuanian 60 0.984 1.000 0.992
lv Latvian 60 1.000 0.967 0.983
mt Maltese 59 0.982 0.949 0.966 not in base model pretraining
nl Dutch 60 1.000 0.983 0.992
pl Polish 60 1.000 1.000 1.000
pt Portuguese 60 0.968 1.000 0.984
ro Romanian 60 1.000 1.000 1.000
sk Slovak 60 1.000 0.983 0.992
sl Slovenian 59 1.000 1.000 1.000
sv Swedish 60 1.000 1.000 1.000
tr Turkish 60 0.984 1.000 0.992
az Azerbaijani 60 1.000 0.967 0.983

The base-model note is a fact about pretraining, not a cause of the score beside it. nsfw Maltese carried the same note at 0.000 and reached 1.000 on corpus size alone, with nothing about the base model changed. Check how many examples a weak score rests on before reaching for this.

Per register

Register Support P R F1 FPR
cyber_intrusion 130 1.000 1.000 1.000 0.000
cyber_intrusion_near_miss 0 0.000 0.000 0.000 0.000
defamation 130 1.000 1.000 1.000 0.000
defamation_near_miss 0 0.000 0.000 0.000 0.019
election_integrity 130 1.000 0.992 0.996 0.000
election_integrity_near_miss 0 0.000 0.000 0.000 0.000
extremism 129 1.000 0.984 0.992 0.000
extremism_near_miss 0 0.000 0.000 0.000 0.038
fraud_deception 130 1.000 1.000 1.000 0.000
fraud_deception_near_miss 0 0.000 0.000 0.000 0.058
hate_incitement 126 1.000 1.000 1.000 0.000
hate_incitement_near_miss 0 0.000 0.000 0.000 0.000
illicit_drugs 130 1.000 1.000 1.000 0.000
illicit_drugs_near_miss 0 0.000 0.000 0.000 0.000
mundane_informational 0 0.000 0.000 0.000 0.000
mundane_operational 0 0.000 0.000 0.000 0.000
mundane_transactional 0 0.000 0.000 0.000 0.000
property_crime 129 1.000 0.977 0.988 0.000
property_crime_near_miss 0 0.000 0.000 0.000 0.019
self_harm 129 1.000 0.992 0.996 0.000
self_harm_near_miss 0 0.000 0.000 0.000 0.019
sexual_exploitation 129 1.000 0.969 0.984 0.000
sexual_exploitation_near_miss 0 0.000 0.000 0.000 0.019
violent_facilitation 130 1.000 0.977 0.988 0.000
violent_facilitation_near_miss 0 0.000 0.000 0.000 0.019
weapons_cbrn 130 1.000 0.992 0.996 0.000
weapons_cbrn_near_miss 0 0.000 0.000 0.000 0.000

Known weaknesses

The three weakest languages by F1: mt at 0.966, ga at 0.967, az at 0.983.

These are published rather than dropped. A coverage table with the bad rows removed is not a coverage table.