# bias evaluation

**Threshold 0.77 (calibrated on the validation split, objective macro_f1).** The config default of 0.5 scored 0.981 against 0.982 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 | 80 | 0.987 | 0.975 | 0.981 |  |
| `cs` Czech | 80 | 0.988 | 1.000 | 0.994 |  |
| `da` Danish | 80 | 1.000 | 1.000 | 1.000 |  |
| `de` German | 80 | 0.963 | 0.988 | 0.975 |  |
| `el` Greek | 80 | 1.000 | 0.988 | 0.994 |  |
| `en` English | 80 | 0.963 | 0.988 | 0.975 |  |
| `es` Spanish | 76 | 1.000 | 0.974 | 0.987 |  |
| `et` Estonian | 79 | 0.963 | 1.000 | 0.981 |  |
| `fi` Finnish | 80 | 0.988 | 0.988 | 0.988 |  |
| `fr` French | 80 | 0.976 | 1.000 | 0.988 |  |
| `ga` Irish | 77 | 0.974 | 0.961 | 0.967 |  |
| `hr` Croatian | 80 | 1.000 | 1.000 | 1.000 |  |
| `hu` Hungarian | 80 | 0.952 | 0.988 | 0.969 |  |
| `it` Italian | 80 | 1.000 | 1.000 | 1.000 |  |
| `lt` Lithuanian | 79 | 0.987 | 0.987 | 0.987 |  |
| `lv` Latvian | 80 | 0.975 | 0.988 | 0.981 |  |
| `mt` Maltese | 78 | 0.948 | 0.936 | 0.942 | not in base model pretraining |
| `nl` Dutch | 79 | 0.988 | 1.000 | 0.994 |  |
| `pl` Polish | 80 | 0.976 | 1.000 | 0.988 |  |
| `pt` Portuguese | 76 | 0.974 | 0.974 | 0.974 |  |
| `ro` Romanian | 80 | 1.000 | 0.988 | 0.994 |  |
| `sk` Slovak | 80 | 0.988 | 0.988 | 0.988 |  |
| `sl` Slovenian | 80 | 1.000 | 0.988 | 0.994 |  |
| `sv` Swedish | 80 | 0.988 | 0.988 | 0.988 |  |
| `tr` Turkish | 80 | 0.951 | 0.975 | 0.963 |  |
| `az` Azerbaijani | 80 | 0.920 | 1.000 | 0.958 |  |

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 |
|---|---|---|---|---|---|
| `age_assumption` | 415 | 1.000 | 0.986 | 0.993 | 0.000 |
| `counter_stereotype` | 0 | 0.000 | 0.000 | 0.000 | 0.011 |
| `demographic_statistic` | 0 | 0.000 | 0.000 | 0.000 | 0.000 |
| `disability_condescension` | 413 | 1.000 | 0.995 | 0.998 | 0.000 |
| `discussing_bias` | 0 | 0.000 | 0.000 | 0.000 | 0.016 |
| `ethnic_generalisation` | 411 | 1.000 | 0.990 | 0.995 | 0.000 |
| `inclusive_phrasing` | 0 | 0.000 | 0.000 | 0.000 | 0.096 |
| `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 |
| `neutral_description` | 0 | 0.000 | 0.000 | 0.000 | 0.000 |
| `occupational_stereotype` | 415 | 1.000 | 0.969 | 0.984 | 0.000 |
| `religious_assumption` | 410 | 1.000 | 0.995 | 0.998 | 0.000 |

## Known weaknesses

The three weakest languages by F1: `mt` at 0.942, `az` at 0.958, `tr` at 0.963.

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