MillID tiny, step 106000

README.md

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+---
+library_name: pytorch
+pipeline_tag: text-classification
+tags:
+- language-identification
+datasets:
+- baobabtech/millid-dataset
+---
+
+# MillID tiny: MultiLingual Language IDentifier
+
+A byte-level, encoder-only transformer that names the language of a passage: **1641 language-script
+labels** (ISO 639-3 + script, e.g. `eng_Latn`, `amh_Ethi`). 2.9M parameters, trained from
+scratch on [baobabtech/millid-dataset](https://huggingface.co/datasets/baobabtech/millid-dataset).
+
+```python
+from millid import MillID   # millid.py, in this repository
+
+model = MillID.from_pretrained("baobabtech/millid-tiny-v1.5")
+model.predict(["Lave men ou ak savon anvan ou manje."], k=3)
+```
+
+## Architecture
+
+UTF-8 bytes (after Unicode NFKC), each embedded as byte + Unicode script + byte-in-character + hashed character
+2-, 3- and 4-grams (4,096 buckets) -> depthwise convolution stem (kernel 5) -> strided
+convolution, 1 bytes per token -> 3 pre-norm blocks (RMSNorm, attention with RoPE,
+3 heads, SwiGLU; d=192) -> masked mean pooling -> language head, each language's vector the sum of
+its own, its language's and its script's. Up to 256 bytes per passage. See `ARCHITECTURE.md`.
+
+## Evaluation against GlotLID and fastText
+
+`validation` chose the checkpoint; `test` is split by document from training; `udhr` is the Universal Declaration
+of Human Rights, labelled by its translators, over the languages MillID knows, and `udhr-all` over all of them.
+`glotlid` is [GlotLID](https://huggingface.co/cis-lmu/glotlid) as it is; `glotlid@millid` is GlotLID restricted to
+MillID's labels, the like-for-like comparison. GlotLID's own labels came from training on data that includes UDHR
+and labelled FinePDFs and FineWeb-2, so its scores on `udhr` and on those sources are optimistic. `fasttext` is
+Meta's [fastText language identifier](https://huggingface.co/facebook/fasttext-language-identification) (NLLB,
+218 labels), its answers mapped onto MillID's spelling (Simplified and Traditional Han as `Hani`, `pes`/`prs` as
+`fas`, `est` as `ekk`). It cannot answer most of the 1,600 languages here, so `test-common` and `udhr-common`
+score every model on the passages whose language all of them know: the head-to-head on equal terms.
+
+| Set | Model | Top-1 | Top-3 | Top-5 | Macro P | Macro R | Macro F1 | Median F1 | Languages |
+|---|---|---|---|---|---|---|---|---|---|
+| validation | millid | 0.9578 | 0.9889 | 0.9933 | 0.9539 | 0.9591 | 0.9551 | 0.9822 | 1626 |
+| test | millid | 0.9587 | 0.9890 | 0.9932 | 0.9550 | 0.9588 | 0.9554 | 0.9825 | 1621 |
+| test | glotlid | 0.9776 | 0.9977 | 0.9984 | 0.9783 | 0.9752 | 0.9748 | 1.0000 | 1621 |
+| test | glotlid@millid | 0.9783 | 0.9978 | 0.9985 | 0.9778 | 0.9765 | 0.9755 | 1.0000 | 1621 |
+| test | fasttext | 0.2511 | 0.2610 | 0.2625 | 0.0520 | 0.1205 | 0.0657 | 0.0000 | 1621 |
+| test | fasttext@millid | 0.2515 | 0.2611 | 0.2625 | 0.0521 | 0.1209 | 0.0659 | 0.0000 | 1621 |
+| test-common | millid | 0.9396 | 0.9857 | 0.9909 | 0.9709 | 0.9353 | 0.9513 | 0.9726 | 213 |
+| test-common | glotlid | 0.9738 | 0.9925 | 0.9946 | 0.9924 | 0.9771 | 0.9835 | 0.9983 | 213 |
+| test-common | glotlid@millid | 0.9749 | 0.9927 | 0.9948 | 0.9923 | 0.9790 | 0.9846 | 0.9983 | 213 |
+| test-common | fasttext | 0.9339 | 0.9707 | 0.9762 | 0.9357 | 0.9169 | 0.9149 | 0.9680 | 213 |
+| test-common | fasttext@millid | 0.9352 | 0.9710 | 0.9763 | 0.9354 | 0.9201 | 0.9173 | 0.9680 | 213 |
+| udhr | millid | 0.8492 | 0.9117 | 0.9257 | 0.9148 | 0.8736 | 0.8764 | 0.9907 | 358 |
+| udhr | glotlid | 0.8598 | 0.9280 | 0.9421 | 0.9261 | 0.8826 | 0.8865 | 0.9915 | 358 |
+| udhr | glotlid@millid | 0.8614 | 0.9294 | 0.9433 | 0.9258 | 0.8844 | 0.8873 | 0.9915 | 358 |
+| udhr | fasttext | 0.4625 | 0.4857 | 0.4903 | 0.3152 | 0.4479 | 0.3497 | 0.0000 | 358 |
+| udhr | fasttext@millid | 0.4629 | 0.4857 | 0.4904 | 0.3152 | 0.4483 | 0.3499 | 0.0000 | 358 |
+| udhr-common | millid | 0.9123 | 0.9606 | 0.9655 | 0.9794 | 0.9393 | 0.9513 | 1.0000 | 170 |
+| udhr-common | glotlid | 0.9336 | 0.9678 | 0.9732 | 0.9888 | 0.9576 | 0.9657 | 1.0000 | 170 |
+| udhr-common | glotlid@millid | 0.9338 | 0.9683 | 0.9736 | 0.9888 | 0.9579 | 0.9659 | 1.0000 | 170 |
+| udhr-common | fasttext | 0.9187 | 0.9647 | 0.9741 | 0.9474 | 0.9431 | 0.9323 | 1.0000 | 170 |
+| udhr-common | fasttext@millid | 0.9195 | 0.9647 | 0.9742 | 0.9474 | 0.9441 | 0.9329 | 1.0000 | 170 |
+| udhr-all | millid | 0.7274 | 0.7809 | 0.7928 | 0.7340 | 0.7290 | 0.7149 | 0.9649 | 429 |
+| udhr-all | glotlid | 0.7502 | 0.8145 | 0.8282 | 0.7695 | 0.7529 | 0.7454 | 0.9796 | 429 |
+| udhr-all | glotlid@millid | 0.7378 | 0.7960 | 0.8079 | 0.7425 | 0.7381 | 0.7245 | 0.9748 | 429 |
+| udhr-all | fasttext | 0.3961 | 0.4160 | 0.4200 | 0.2479 | 0.3737 | 0.2795 | 0.0000 | 429 |
+| udhr-all | fasttext@millid | 0.3964 | 0.4160 | 0.4200 | 0.2479 | 0.3741 | 0.2797 | 0.0000 | 429 |
+
+Top-1 accuracy by passage length:
+
+| Set | Model | short | sentence | paragraph |
+|---|---|---|---|---|
+| validation | millid | 0.9312 | 0.9788 | 0.9850 |
+| test | millid | 0.9333 | 0.9797 | 0.9833 |
+| test | glotlid | 0.9664 | 0.9853 | 0.9911 |
+| test | glotlid@millid | 0.9674 | 0.9858 | 0.9912 |
+| test | fasttext | 0.2760 | 0.2501 | 0.1958 |
+| test | fasttext@millid | 0.2764 | 0.2506 | 0.1959 |
+| test-common | millid | 0.9125 | 0.9685 | 0.9725 |
+| test-common | glotlid | 0.9594 | 0.9876 | 0.9947 |
+| test-common | glotlid@millid | 0.9609 | 0.9885 | 0.9949 |
+| test-common | fasttext | 0.9112 | 0.9562 | 0.9651 |
+| test-common | fasttext@millid | 0.9125 | 0.9581 | 0.9655 |
+
+### Leaderboard: test
+
+MillID's F1 against the others', language by language: against GlotLID restricted to MillID's labels, MillID is better on **103** languages, worse on **869** and within 0.01 on 649; against fastText restricted to MillID's labels, MillID is better on **1616** languages, worse on **0** and within 0.01 on 5. The full table, with precision and recall, is
+`reports/test/leaderboard.csv`.
+
+The 20 languages with the most passages:
+
+| Label | Language | Passages | MillID F1 | GlotLID F1 | GlotLID F1, restricted | fastText F1 | fastText F1, restricted |
+|---|---|---|---|---|---|---|---|
+| `fra_Latn` | French | 1652 | 0.929 | 0.928 | 0.928 | 0.808 | 0.808 |
+| `eng_Latn` | English | 1444 | 0.917 | 0.858 | 0.859 | 0.653 | 0.648 |
+| `spa_Latn` | Spanish | 1136 | 0.931 | 0.950 | 0.950 | 0.574 | 0.574 |
+| `tam_Taml` | Tamil | 754 | 0.997 | 0.993 | 0.993 | 0.965 | 0.965 |
+| `arb_Arab` | Arabic (Modern Standard) | 745 | 0.814 | 0.785 | 0.785 | 0.513 | 0.512 |
+| `por_Latn` | Portuguese | 657 | 0.947 | 0.947 | 0.949 | 0.756 | 0.755 |
+| `kan_Knda` | Kannada | 601 | 0.969 | 0.993 | 0.993 | 0.922 | 0.922 |
+| `zul_Latn` | Zulu | 594 | 0.846 | 0.896 | 0.896 | 0.539 | 0.539 |
+| `swh_Latn` | Swahili | 592 | 0.848 | 0.918 | 0.916 | 0.346 | 0.346 |
+| `mar_Deva` | Marathi | 589 | 0.965 | 0.989 | 0.990 | 0.926 | 0.926 |
+| `tso_Latn` | Tsonga | 581 | 0.965 | 0.956 | 0.956 | 0.426 | 0.426 |
+| `hun_Latn` | Hungarian | 576 | 0.987 | 0.999 | 0.999 | 0.232 | 0.232 |
+| `afr_Latn` | Afrikaans | 569 | 0.967 | 0.985 | 0.985 | 0.861 | 0.860 |
+| `tel_Telu` | Telugu | 555 | 0.991 | 0.994 | 0.994 | 0.951 | 0.951 |
+| `rus_Cyrl` | Russian | 528 | 0.932 | 0.949 | 0.949 | 0.462 | 0.462 |
+| `nbl_Latn` | South Ndebele | 528 | 0.903 | 0.948 | 0.949 | 0.000 | 0.000 |
+| `ory_Orya` | Odia (individual language) | 511 | 0.988 | 0.991 | 0.994 | 0.976 | 0.976 |
+| `sot_Latn` | Southern Sotho | 491 | 0.966 | 0.977 | 0.978 | 0.660 | 0.660 |
+| `nso_Latn` | Northern Sotho | 479 | 0.956 | 0.968 | 0.968 | 0.915 | 0.914 |
+| `kua_Latn` | Kuanyama | 476 | 0.962 | 0.986 | 0.986 | 0.000 | 0.000 |
+
+Where MillID gains most over GlotLID:
+
+| Label | Language | Passages | MillID F1 | GlotLID F1 | GlotLID F1, restricted | fastText F1 | fastText F1, restricted |
+|---|---|---|---|---|---|---|---|
+| `lbj_Tibt` | Ladakhi | 66 | 0.943 | 0.000 | 0.000 | 0.000 | 0.000 |
+| `ain_Latn` | Ainu | 1 | 0.667 | 0.000 | 0.000 | 0.000 | 0.000 |
+| `ajp_Arab` | South Levantine Arabic | 28 | 0.945 | 0.581 | 0.581 | 0.000 | 0.000 |
+| `und_Laoo` | und | 20 | 0.923 | 0.621 | 0.621 | 0.000 | 0.000 |
+| `und_Thaa` | und | 29 | 0.881 | 0.651 | 0.651 | 0.000 | 0.000 |
+| `sgh_Cyrl` | Shughni | 9 | 0.857 | 0.667 | 0.667 | 0.000 | 0.000 |
+| `und_Geor` | und | 32 | 0.877 | 0.694 | 0.694 | 0.000 | 0.000 |
+| `tuk_Arab` | Turkmen | 135 | 0.905 | 0.761 | 0.761 | 0.000 | 0.000 |
+| `und_Syrc` | und | 43 | 0.966 | 0.822 | 0.822 | 0.000 | 0.000 |
+| `gmh_Latn` | Middle High German | 177 | 0.922 | 0.786 | 0.786 | 0.000 | 0.000 |
+| `orv_Cyrl` | Old Russian | 239 | 0.931 | 0.800 | 0.800 | 0.000 | 0.000 |
+| `und_Kana` | und | 136 | 0.982 | 0.862 | 0.862 | 0.000 | 0.000 |
+| `und_Yiii` | und | 38 | 1.000 | 0.894 | 0.884 | 0.000 | 0.000 |
+| `neb_Latn` | Toura (Côte d'Ivoire) | 11 | 0.957 | 0.625 | 0.842 | 0.000 | 0.000 |
+| `dag_Latn` | Dagbani | 149 | 0.767 | 0.662 | 0.660 | 0.000 | 0.000 |
+
+Where MillID loses most to GlotLID:
+
+| Label | Language | Passages | MillID F1 | GlotLID F1 | GlotLID F1, restricted | fastText F1 | fastText F1, restricted |
+|---|---|---|---|---|---|---|---|
+| `mus_Latn` | Muscogee | 2 | 0.333 | 1.000 | 1.000 | 0.000 | 0.000 |
+| `ttj_Latn` | Tooro | 28 | 0.485 | 0.945 | 0.945 | 0.000 | 0.000 |
+| `mbd_Latn` | Dibabawon Manobo | 14 | 0.596 | 1.000 | 1.000 | 0.000 | 0.000 |
+| `knc_Arab` | Central Kanuri | 68 | 0.462 | 0.823 | 0.825 | 0.007 | 0.007 |
+| `biu_Latn` | Biete | 7 | 0.667 | 1.000 | 1.000 | 0.000 | 0.000 |
+| `dks_Latn` | Southeastern Dinka | 3 | 0.667 | 1.000 | 1.000 | 0.000 | 0.000 |
+| `esi_Latn` | North Alaskan Inupiatun | 9 | 0.455 | 0.783 | 0.783 | 0.000 | 0.000 |
+| `hrv_Latn` | Croatian | 168 | 0.622 | 0.944 | 0.944 | 0.178 | 0.178 |
+| `plw_Latn` | Brooke's Point Palawano | 8 | 0.727 | 1.000 | 1.000 | 0.000 | 0.000 |
+| `bos_Latn` | Bosnian | 198 | 0.649 | 0.902 | 0.902 | 0.181 | 0.181 |
+| `xbr_Latn` | Kambera | 8 | 0.750 | 1.000 | 1.000 | 0.000 | 0.000 |
+| `nyf_Latn` | Giryama | 10 | 0.645 | 0.952 | 0.870 | 0.000 | 0.000 |
+| `ldi_Latn` | Laari | 62 | 0.777 | 0.992 | 0.992 | 0.000 | 0.000 |
+| `hak_Hani` | Hakka Chinese | 29 | 0.621 | 0.824 | 0.824 | 0.000 | 0.000 |
+| `ccp_Latn` | Chakma | 3 | 0.800 | 1.000 | 1.000 | 0.000 | 0.000 |
+
+### Leaderboard: udhr
+
+MillID's F1 against the others', language by language: against GlotLID restricted to MillID's labels, MillID is better on **52** languages, worse on **105** and within 0.01 on 201; against fastText restricted to MillID's labels, MillID is better on **303** languages, worse on **15** and within 0.01 on 40. The full table, with precision and recall, is
+`reports/udhr/leaderboard.csv`.
+
+The 20 languages with the most passages:
+
+| Label | Language | Passages | MillID F1 | GlotLID F1 | GlotLID F1, restricted | fastText F1 | fastText F1, restricted |
+|---|---|---|---|---|---|---|---|
+| `roh_Latn` | Romansh | 399 | 0.994 | 0.999 | 0.999 | 0.945 | 0.945 |
+| `oci_Latn` | Occitan | 398 | 0.277 | 0.376 | 0.376 | 0.436 | 0.436 |
+| `cmn_Hani` | Chinese (Mandarin) | 319 | 0.892 | 0.868 | 0.868 | 0.468 | 0.468 |
+| `krl_Latn` | Karelian | 183 | 0.000 | 0.011 | 0.011 | 0.000 | 0.000 |
+| `qug_Latn` | Chimborazo Highland Quichua | 182 | 0.011 | 0.000 | 0.000 | 0.000 | 0.000 |
+| `acu_Latn` | Achuar-Shiwiar | 180 | 0.828 | 0.854 | 0.854 | 0.000 | 0.000 |
+| `hus_Latn` | Huastec | 175 | 0.977 | 0.986 | 0.986 | 0.000 | 0.000 |
+| `hau_Latn` | Hausa | 155 | 0.990 | 1.000 | 1.000 | 0.876 | 0.876 |
+| `auc_Latn` | Waorani | 154 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
+| `kwi_Latn` | Awa-Cuaiquer | 145 | 0.880 | 0.877 | 0.877 | 0.000 | 0.000 |
+| `jpn_Jpan` | Japanese | 140 | 1.000 | 1.000 | 1.000 | 0.944 | 0.944 |
+| `snn_Latn` | Siona | 136 | 0.631 | 0.514 | 0.514 | 0.000 | 0.000 |
+| `ktu_Latn` | Kituba (Democratic Republic of Congo) | 118 | 0.765 | 0.680 | 0.680 | 0.000 | 0.000 |
+| `lat_Latn` | Latin | 118 | 0.996 | 0.991 | 0.991 | 0.000 | 0.000 |
+| `hat_Latn` | Haitian Creole | 117 | 0.991 | 0.979 | 0.979 | 0.699 | 0.699 |
+| `nya_Latn` | Nyanja | 116 | 0.991 | 1.000 | 1.000 | 0.806 | 0.806 |
+| `kmb_Latn` | Kimbundu | 116 | 0.970 | 1.000 | 1.000 | 0.877 | 0.877 |
+| `rmn_Latn` | Balkan Romani | 116 | 0.678 | 0.857 | 0.857 | 0.000 | 0.000 |
+| `twi_Latn` | Twi | 116 | 0.983 | 0.996 | 0.996 | 0.399 | 0.396 |
+| `chr_Cher` | Cherokee | 115 | 0.671 | 0.663 | 0.663 | 0.000 | 0.000 |
+
+Where MillID gains most over GlotLID:
+
+| Label | Language | Passages | MillID F1 | GlotLID F1 | GlotLID F1, restricted | fastText F1 | fastText F1, restricted |
+|---|---|---|---|---|---|---|---|
+| `mic_Latn` | Mi'kmaw | 59 | 0.991 | 0.427 | 0.427 | 0.000 | 0.000 |
+| `tet_Latn` | Tetum | 58 | 0.638 | 0.167 | 0.167 | 0.000 | 0.000 |
+| `fuf_Latn` | Pular | 58 | 0.906 | 0.483 | 0.483 | 0.000 | 0.000 |
+| `sag_Latn` | Sango | 59 | 0.991 | 0.578 | 0.578 | 0.189 | 0.189 |
+| `skr_Arab` | Saraiki | 63 | 0.867 | 0.500 | 0.500 | 0.000 | 0.000 |
+| `qvh_Latn` | Huamalíes-Dos de Mayo Huánuco Quechua | 58 | 0.468 | 0.154 | 0.154 | 0.000 | 0.000 |
+| `zgh_Tfng` | Standard Moroccan Tamazight | 60 | 0.750 | 0.462 | 0.462 | 0.000 | 0.000 |
+| `lld_Latn` | Ladin | 59 | 0.909 | 0.731 | 0.731 | 0.000 | 0.000 |
+| `tah_Latn` | Tahitian | 57 | 0.973 | 0.813 | 0.813 | 0.966 | 0.966 |
+| `yue_Hani` | Cantonese | 49 | 0.734 | 0.580 | 0.580 | 0.111 | 0.111 |
+| `ote_Latn` | Mezquital Otomi | 59 | 0.154 | 0.000 | 0.000 | 0.000 | 0.000 |
+| `pnb_Arab` | Western Panjabi | 61 | 0.882 | 0.739 | 0.739 | 0.000 | 0.000 |
+| `mxv_Latn` | Metlatónoc Mixtec | 58 | 0.129 | 0.000 | 0.000 | 0.000 | 0.000 |
+| `lij_Latn` | Ligurian | 58 | 0.983 | 0.859 | 0.859 | 0.487 | 0.487 |
+| `bum_Latn` | Bulu | 58 | 0.215 | 0.098 | 0.098 | 0.000 | 0.000 |
+
+Where MillID loses most to GlotLID:
+
+| Label | Language | Passages | MillID F1 | GlotLID F1 | GlotLID F1, restricted | fastText F1 | fastText F1, restricted |
+|---|---|---|---|---|---|---|---|
+| `gyr_Latn` | Guarayu | 49 | 0.537 | 0.990 | 0.990 | 0.000 | 0.000 |
+| `shp_Latn` | Shipibo-Conibo | 62 | 0.469 | 0.912 | 0.912 | 0.000 | 0.000 |
+| `dyu_Latn` | Dyula | 58 | 0.000 | 0.434 | 0.434 | 0.015 | 0.015 |
+| `tbz_Latn` | Ditammari | 60 | 0.000 | 0.378 | 0.378 | 0.000 | 0.000 |
+| `cbu_Latn` | Candoshi-Shapra | 57 | 0.500 | 0.882 | 0.874 | 0.000 | 0.000 |
+| `idu_Latn` | Idoma | 51 | 0.603 | 0.879 | 0.938 | 0.000 | 0.000 |
+| `ell_Grek` | Greek | 115 | 0.671 | 1.000 | 1.000 | 1.000 | 1.000 |
+| `qvc_Latn` | Cajamarca Quechua | 58 | 0.648 | 0.906 | 0.906 | 0.000 | 0.000 |
+| `cpu_Latn` | Pichis Ashéninka | 60 | 0.729 | 0.974 | 0.974 | 0.000 | 0.000 |
+| `qvn_Latn` | North Junín Quechua | 57 | 0.219 | 0.459 | 0.459 | 0.000 | 0.000 |
+| `hin_Deva` | Hindi | 59 | 0.723 | 0.957 | 0.957 | 0.975 | 0.975 |
+| `hrv_Latn` | Croatian | 58 | 0.500 | 0.727 | 0.727 | 0.474 | 0.474 |
+| `kbp_Latn` | Kabiyè | 59 | 0.781 | 1.000 | 1.000 | 0.389 | 0.389 |
+| `cni_Latn` | Asháninka | 52 | 0.773 | 0.963 | 0.963 | 0.000 | 0.000 |
+| `rmn_Latn` | Balkan Romani | 116 | 0.678 | 0.857 | 0.857 | 0.000 | 0.000 |
+
+### Leaderboard: test-common
+
+MillID's F1 against the others', language by language: against GlotLID restricted to MillID's labels, MillID is better on **6** languages, worse on **154** and within 0.01 on 53; against fastText restricted to MillID's labels, MillID is better on **85** languages, worse on **51** and within 0.01 on 77. The full table, with precision and recall, is
+`reports/test-common/leaderboard.csv`.
+
+The 20 languages with the most passages:
+
+| Label | Language | Passages | MillID F1 | GlotLID F1 | GlotLID F1, restricted | fastText F1 | fastText F1, restricted |
+|---|---|---|---|---|---|---|---|
+| `fra_Latn` | French | 1652 | 0.943 | 0.955 | 0.955 | 0.963 | 0.963 |
+| `eng_Latn` | English | 1444 | 0.932 | 0.876 | 0.877 | 0.857 | 0.857 |
+| `spa_Latn` | Spanish | 1136 | 0.937 | 0.958 | 0.958 | 0.941 | 0.941 |
+| `tam_Taml` | Tamil | 754 | 0.997 | 1.000 | 1.000 | 0.997 | 0.997 |
+| `arb_Arab` | Arabic (Modern Standard) | 745 | 0.871 | 0.886 | 0.886 | 0.957 | 0.954 |
+| `por_Latn` | Portuguese | 657 | 0.959 | 0.954 | 0.956 | 0.950 | 0.949 |
+| `kan_Knda` | Kannada | 601 | 0.973 | 0.994 | 0.994 | 0.999 | 0.999 |
+| `zul_Latn` | Zulu | 594 | 0.883 | 0.904 | 0.904 | 0.970 | 0.970 |
+| `swh_Latn` | Swahili | 592 | 0.893 | 0.954 | 0.953 | 0.945 | 0.945 |
+| `mar_Deva` | Marathi | 589 | 0.968 | 0.989 | 0.990 | 0.987 | 0.987 |
+| `tso_Latn` | Tsonga | 581 | 0.979 | 0.956 | 0.956 | 0.986 | 0.986 |
+| `hun_Latn` | Hungarian | 576 | 0.989 | 1.000 | 1.000 | 0.980 | 0.980 |
+| `afr_Latn` | Afrikaans | 569 | 0.972 | 0.985 | 0.985 | 0.986 | 0.986 |
+| `tel_Telu` | Telugu | 555 | 0.997 | 0.998 | 0.998 | 0.999 | 0.999 |
+| `rus_Cyrl` | Russian | 528 | 0.945 | 0.985 | 0.985 | 0.927 | 0.927 |
+| `ory_Orya` | Odia (individual language) | 511 | 0.994 | 0.997 | 1.000 | 0.996 | 0.996 |
+| `sot_Latn` | Southern Sotho | 491 | 0.969 | 0.977 | 0.978 | 0.856 | 0.856 |
+| `nso_Latn` | Northern Sotho | 479 | 0.958 | 0.970 | 0.970 | 0.967 | 0.967 |
+| `asm_Beng` | Assamese | 467 | 0.997 | 0.999 | 0.999 | 0.997 | 0.997 |
+| `ben_Beng` | Bangla | 466 | 0.988 | 0.997 | 0.997 | 0.989 | 0.989 |
+
+Where MillID gains most over GlotLID:
+
+| Label | Language | Passages | MillID F1 | GlotLID F1 | GlotLID F1, restricted | fastText F1 | fastText F1, restricted |
+|---|---|---|---|---|---|---|---|
+| `ewo_Latn` | Ewondo | 134 | 0.959 | 0.879 | 0.883 | 0.860 | 0.860 |
+| `eng_Latn` | English | 1444 | 0.932 | 0.876 | 0.877 | 0.857 | 0.857 |
+| `arb_Latn` | Standard Arabic | 163 | 0.942 | 0.906 | 0.913 | 0.852 | 0.852 |
+| `tso_Latn` | Tsonga | 581 | 0.979 | 0.956 | 0.956 | 0.986 | 0.986 |
+| `fuv_Latn` | Nigerian Fulfulde | 164 | 0.907 | 0.877 | 0.892 | 0.859 | 0.859 |
+| `knc_Latn` | Central Kanuri | 293 | 0.967 | 0.952 | 0.952 | 0.981 | 0.981 |
+| `quy_Latn` | Ayacucho Quechua | 271 | 0.940 | 0.925 | 0.933 | 0.979 | 0.979 |
+| `kam_Latn` | Kamba | 272 | 0.980 | 0.972 | 0.974 | 0.862 | 0.862 |
+| `bjn_Arab` | Banjar | 116 | 0.991 | 0.834 | 0.987 | 0.453 | 0.960 |
+| `por_Latn` | Portuguese | 657 | 0.959 | 0.954 | 0.956 | 0.950 | 0.949 |
+| `mlt_Latn` | Maltese | 291 | 0.995 | 0.993 | 0.993 | 0.961 | 0.961 |
+| `san_Deva` | Sanskrit | 239 | 0.990 | 0.989 | 0.989 | 0.970 | 0.970 |
+| `hye_Armn` | Armenian | 178 | 1.000 | 1.000 | 1.000 | 0.977 | 0.977 |
+| `guj_Gujr` | Gujarati | 354 | 1.000 | 1.000 | 1.000 | 0.984 | 0.984 |
+| `mal_Mlym` | Malayalam | 338 | 1.000 | 1.000 | 1.000 | 0.994 | 0.994 |
+
+Where MillID loses most to GlotLID:
+
+| Label | Language | Passages | MillID F1 | GlotLID F1 | GlotLID F1, restricted | fastText F1 | fastText F1, restricted |
+|---|---|---|---|---|---|---|---|
+| `knc_Arab` | Central Kanuri | 68 | 0.522 | 0.857 | 0.860 | 0.029 | 0.029 |
+| `hrv_Latn` | Croatian | 168 | 0.659 | 0.994 | 0.994 | 0.856 | 0.856 |
+| `bos_Latn` | Bosnian | 198 | 0.685 | 1.000 | 1.000 | 0.897 | 0.897 |
+| `tzm_Tfng` | Central Atlas Tamazight | 157 | 0.697 | 0.935 | 0.935 | 0.751 | 0.751 |
+| `zsm_Latn` | Standard Malay | 244 | 0.789 | 0.998 | 0.998 | 0.908 | 0.908 |
+| `taq_Tfng` | Tamasheq | 104 | 0.654 | 0.805 | 0.805 | 0.000 | 0.000 |
+| `ind_Latn` | Indonesian | 335 | 0.842 | 0.991 | 0.991 | 0.887 | 0.887 |
+| `kas_Deva` | Kashmiri | 47 | 0.557 | 0.703 | 0.703 | 0.587 | 0.587 |
+| `cjk_Latn` | Chokwe | 24 | 0.857 | 1.000 | 1.000 | 0.807 | 0.807 |
+| `nob_Latn` | Norwegian Bokmål | 265 | 0.821 | 0.961 | 0.959 | 0.894 | 0.894 |
+| `kin_Latn` | Kinyarwanda | 210 | 0.849 | 0.981 | 0.981 | 0.854 | 0.854 |
+| `mag_Deva` | Magahi | 97 | 0.890 | 1.000 | 1.000 | 0.772 | 0.772 |
+| `lim_Latn` | Limburgish | 196 | 0.896 | 1.000 | 1.000 | 0.956 | 0.956 |
+| `run_Latn` | Rundi | 291 | 0.883 | 0.985 | 0.985 | 0.854 | 0.854 |
+| `taq_Latn` | Tamasheq | 106 | 0.902 | 1.000 | 1.000 | 0.838 | 0.838 |
+
+Per-language precision, recall (sensitivity), F1 and specificity for every model are in `reports/<set>/`.
+
+## Training
+
+1,943,957 passages, best validation macro F1 at step 106000, 0.6 hours.
+Languages sampled in proportion to n^0.3; 25% of passages cut to a 20 to 80 character
+snippet; batch 512, AdamW at 0.001, cosine schedule, label smoothing 0.1.

config.json

@@ -0,0 +1,65 @@
+{
+  "model": {
+    "num_labels": 1641,
+    "max_len": 256,
+    "d_model": 192,
+    "n_layers": 3,
+    "n_heads": 3,
+    "dropout": 0.1,
+    "conv_kernel": 5,
+    "downsample": 1,
+    "ngram_buckets": 4096,
+    "script_buckets": 512,
+    "rope_theta": 10000.0,
+    "vocab_size": 259,
+    "modalities": [
+      "text"
+    ],
+    "model_type": "millid-byte-encoder"
+  },
+  "training": {
+    "dataset": "baobabtech/millid-dataset",
+    "preset": "tiny",
+    "max_len": 256,
+    "steps": 120000,
+    "batch_size": 512,
+    "lr": 0.001,
+    "warmup": 2000,
+    "weight_decay": 0.01,
+    "dropout": 0.1,
+    "label_smoothing": 0.1,
+    "alpha": 0.3,
+    "short_p": 0.25,
+    "eval_every": 2000,
+    "eval_per_label": 20,
+    "patience": 12000,
+    "commercial_only": false,
+    "extra": [],
+    "compare_glotlid": true,
+    "compare_fasttext": true,
+    "final_eval": "all",
+    "output_dir": "millid-out",
+    "push_to": "baobabtech/millid-tiny-v1.5",
+    "public": false,
+    "seed": 7,
+    "log_every": 100,
+    "threads": 0,
+    "workers": -1,
+    "compile": true,
+    "gpus": 0,
+    "wandb_project": "millid",
+    "wandb_entity": "mscsr000324-must",
+    "wandb_run_name": "millid-tiny-v1.5",
+    "wandb_tags": [
+      "tiny",
+      "v1.5"
+    ],
+    "no_wandb": false
+  },
+  "best_step": 106000,
+  "parameters": 2927145,
+  "gpus": 1,
+  "train_passages": 1943957,
+  "trained_labels": 1641,
+  "seconds": 1999
+}

labels.json

@@ -0,0 +1,3320 @@
+{
+ "scheme": "ISO 639-3 language + '_' + ISO 15924 script, e.g. eng_Latn, amh_Ethi",
+ "text_encoding": "Unicode NFKC, whitespace collapsed, then UTF-8 bytes",
+ "labels": [
+  "aai_Latn",
+  "aak_Latn",
+  "aau_Latn",
+  "aaz_Latn",
+  "aba_Latn",
+  "abk_Cyrl",
+  "abn_Latn",
+  "abq_Cyrl",
+  "abs_Latn",
+  "abt_Latn",
+  "abx_Latn",
+  "aby_Latn",
+  "abz_Latn",
+  "aca_Latn",
+  "acd_Latn",
+  "ace_Latn",
+  "acf_Latn",
+  "ach_Latn",
+  "acm_Arab",
+  "acn_Latn",
+  "acr_Latn",
+  "acu_Latn",
+  "ada_Latn",
+  "ade_Latn",
+  "adh_Latn",
+  "adi_Latn",
+  "adj_Latn",
+  "ady_Cyrl",
+  "adz_Latn",
+  "aeb_Arab",
+  "aeb_Latn",
+  "aer_Latn",
+  "aeu_Latn",
+  "aey_Latn",
+  "afr_Latn",
+  "agd_Latn",
+  "agg_Latn",
+  "agm_Latn",
+  "agn_Latn",
+  "agr_Latn",
+  "agt_Latn",
+  "agu_Latn",
+  "agx_Cyrl",
+  "ahk_Latn",
+  "aia_Latn",
+  "aii_Syrc",
+  "ain_Latn",
+  "ajg_Latn",
+  "aji_Latn",
+  "ajp_Arab",
+  "akb_Latn",
+  "ake_Latn",
+  "akh_Latn",
+  "akp_Latn",
+  "aln_Latn",
+  "alp_Latn",
+  "alq_Latn",
+  "als_Latn",
+  "alt_Cyrl",
+  "aly_Latn",
+  "alz_Latn",
+  "ame_Latn",
+  "amf_Latn",
+  "amh_Ethi",
+  "ami_Latn",
+  "amk_Latn",
+  "amm_Latn",
+  "amn_Latn",
+  "amp_Latn",
+  "amr_Latn",
+  "amu_Latn",
+  "amx_Latn",
+  "ang_Latn",
+  "ann_Latn",
+  "anp_Deva",
+  "anv_Latn",
+  "any_Latn",
+  "aoi_Latn",
+  "aoj_Latn",
+  "aom_Latn",
+  "aoz_Latn",
+  "apb_Latn",
+  "apc_Arab",
+  "ape_Latn",
+  "apn_Latn",
+  "apr_Latn",
+  "apt_Latn",
+  "apu_Latn",
+  "apw_Latn",
+  "apy_Latn",
+  "apz_Latn",
+  "arb_Arab",
+  "arb_Latn",
+  "are_Latn",
+  "arg_Latn",
+  "arl_Latn",
+  "arn_Latn",
+  "arp_Latn",
+  "arq_Arab",
+  "ars_Arab",
+  "ary_Arab",
+  "arz_Arab",
+  "asm_Beng",
+  "asm_Latn",
+  "aso_Latn",
+  "ast_Latn",
+  "ata_Latn",
+  "atb_Latn",
+  "atd_Latn",
+  "atg_Latn",
+  "ati_Latn",
+  "atj_Latn",
+  "att_Latn",
+  "auc_Latn",
+  "aui_Latn",
+  "auy_Latn",
+  "ava_Cyrl",
+  "avk_Latn",
+  "avt_Latn",
+  "avu_Latn",
+  "awa_Deva",
+  "awb_Latn",
+  "awx_Latn",
+  "ayp_Arab",
+  "ayr_Latn",
+  "azb_Arab",
+  "azg_Latn",
+  "azj_Cyrl",
+  "azj_Latn",
+  "azz_Latn",
+  "bak_Cyrl",
+  "bam_Latn",
+  "ban_Latn",
+  "bao_Latn",
+  "bar_Latn",
+  "bas_Latn",
+  "bba_Latn",
+  "bbb_Latn",
+  "bbc_Latn",
+  "bbr_Latn",
+  "bcc_Arab",
+  "bch_Latn",
+  "bci_Latn",
+  "bcl_Latn",
+  "bco_Latn",
+  "bcw_Latn",
+  "bdd_Latn",
+  "bdh_Latn",
+  "bdq_Latn",
+  "bea_Latn",
+  "bef_Latn",
+  "bel_Cyrl",
+  "bem_Latn",
+  "ben_Beng",
+  "ben_Latn",
+  "beq_Latn",
+  "bew_Latn",
+  "bfd_Latn",
+  "bgr_Latn",
+  "bgs_Latn",
+  "bgt_Latn",
+  "bgz_Latn",
+  "bhg_Latn",
+  "bhl_Latn",
+  "bho_Deva",
+  "bhw_Latn",
+  "bib_Latn",
+  "big_Latn",
+  "bim_Latn",
+  "bin_Latn",
+  "bis_Latn",
+  "biu_Latn",
+  "biv_Latn",
+  "bjn_Arab",
+  "bjn_Latn",
+  "bjp_Latn",
+  "bjr_Latn",
+  "bjv_Latn",
+  "bkd_Latn",
+  "bkq_Latn",
+  "bku_Latn",
+  "bla_Latn",
+  "blh_Latn",
+  "blk_Mymr",
+  "blt_Latn",
+  "blw_Latn",
+  "blz_Latn",
+  "bmh_Latn",
+  "bmk_Latn",
+  "bmq_Latn",
+  "bmr_Latn",
+  "bmu_Latn",
+  "bno_Latn",
+  "bnp_Latn",
+  "boa_Latn",
+  "bod_Tibt",
+  "boj_Latn",
+  "bon_Latn",
+  "bos_Latn",
+  "box_Latn",
+  "bpr_Latn",
+  "bps_Latn",
+  "bpy_Beng",
+  "bqc_Latn",
+  "bqp_Latn",
+  "bre_Latn",
+  "brh_Arab",
+  "bru_Latn",
+  "brx_Deva",
+  "bsn_Latn",
+  "bsp_Latn",
+  "bsq_Latn",
+  "bss_Latn",
+  "btd_Latn",
+  "bth_Latn",
+  "bts_Latn",
+  "btx_Latn",
+  "bud_Latn",
+  "bug_Latn",
+  "buk_Latn",
+  "bul_Cyrl",
+  "bum_Latn",
+  "bus_Latn",
+  "bvd_Latn",
+  "bvr_Latn",
+  "bvz_Latn",
+  "bwd_Latn",
+  "bwu_Latn",
+  "bxh_Latn",
+  "bxr_Cyrl",
+  "byr_Latn",
+  "byx_Latn",
+  "bzd_Latn",
+  "bzh_Latn",
+  "bzi_Thai",
+  "bzj_Latn",
+  "caa_Latn",
+  "cab_Latn",
+  "cac_Latn",
+  "caf_Latn",
+  "cag_Latn",
+  "cak_Latn",
+  "cao_Latn",
+  "cap_Latn",
+  "caq_Latn",
+  "car_Latn",
+  "cas_Latn",
+  "cat_Latn",
+  "cav_Latn",
+  "cax_Latn",
+  "cbc_Latn",
+  "cbi_Latn",
+  "cbk_Latn",
+  "cbr_Latn",
+  "cbs_Latn",
+  "cbt_Latn",
+  "cbu_Latn",
+  "cbv_Latn",
+  "cce_Latn",
+  "cco_Latn",
+  "ccp_Latn",
+  "ceb_Latn",
+  "ceg_Latn",
+  "cek_Latn",
+  "ces_Latn",
+  "cfm_Latn",
+  "cgc_Latn",
+  "cha_Latn",
+  "chd_Latn",
+  "che_Cyrl",
+  "chf_Latn",
+  "chk_Latn",
+  "cho_Latn",
+  "chq_Latn",
+  "chr_Cher",
+  "chu_Cyrl",
+  "chv_Cyrl",
+  "chw_Latn",
+  "chz_Latn",
+  "cjk_Latn",
+  "cjp_Latn",
+  "cjs_Cyrl",
+  "cjv_Latn",
+  "ckb_Arab",
+  "ckm_Latn",
+  "cko_Latn",
+  "ckt_Cyrl",
+  "clu_Latn",
+  "cme_Latn",
+  "cmn_Hani",
+  "cmo_Latn",
+  "cnh_Latn",
+  "cni_Latn",
+  "cnk_Latn",
+  "cnr_Latn",
+  "cnw_Latn",
+  "coe_Latn",
+  "cof_Latn",
+  "cok_Latn",
+  "con_Latn",
+  "cop_Copt",
+  "cor_Latn",
+  "cos_Latn",
+  "cot_Latn",
+  "cpb_Latn",
+  "cpc_Latn",
+  "cpu_Latn",
+  "cpy_Latn",
+  "crh_Cyrl",
+  "crh_Latn",
+  "crj_Cans",
+  "crk_Cans",
+  "crk_Latn",
+  "crl_Cans",
+  "crm_Cans",
+  "crn_Latn",
+  "crs_Latn",
+  "crx_Latn",
+  "csb_Latn",
+  "csw_Latn",
+  "csy_Latn",
+  "cta_Latn",
+  "ctd_Latn",
+  "cto_Latn",
+  "ctp_Latn",
+  "ctu_Latn",
+  "cub_Latn",
+  "cui_Latn",
+  "cuk_Latn",
+  "cul_Latn",
+  "cut_Latn",
+  "cux_Latn",
+  "cwe_Latn",
+  "cwt_Latn",
+  "cya_Latn",
+  "cym_Latn",
+  "czt_Latn",
+  "daa_Latn",
+  "dad_Latn",
+  "daf_Latn",
+  "dag_Latn",
+  "dah_Latn",
+  "dak_Latn",
+  "dan_Latn",
+  "dar_Cyrl",
+  "ddg_Latn",
+  "ded_Latn",
+  "des_Latn",
+  "deu_Latn",
+  "dga_Latn",
+  "dgc_Latn",
+  "dgi_Latn",
+  "dgr_Latn",
+  "dgz_Latn",
+  "dhm_Latn",
+  "dhv_Latn",
+  "dig_Latn",
+  "dik_Latn",
+  "diq_Latn",
+  "diu_Latn",
+  "div_Thaa",
+  "dje_Latn",
+  "djk_Latn",
+  "djr_Latn",
+  "dks_Latn",
+  "dng_Cyrl",
+  "dnj_Latn",
+  "dnw_Latn",
+  "dob_Latn",
+  "doi_Deva",
+  "dop_Latn",
+  "dos_Latn",
+  "drg_Latn",
+  "dru_Latn",
+  "dsb_Latn",
+  "dtp_Latn",
+  "dts_Latn",
+  "dua_Latn",
+  "duo_Latn",
+  "dur_Latn",
+  "dwr_Latn",
+  "dww_Latn",
+  "dyi_Latn",
+  "dyo_Latn",
+  "dyu_Latn",
+  "dzo_Tibt",
+  "ebk_Latn",
+  "efi_Latn",
+  "ekk_Latn",
+  "eko_Latn",
+  "ell_Grek",
+  "emi_Latn",
+  "eml_Latn",
+  "emp_Latn",
+  "enb_Latn",
+  "eng_Latn",
+  "enl_Latn",
+  "enm_Latn",
+  "enq_Latn",
+  "epo_Latn",
+  "eri_Latn",
+  "ese_Latn",
+  "esi_Latn",
+  "esk_Latn",
+  "ess_Latn",
+  "esu_Latn",
+  "etr_Latn",
+  "eus_Latn",
+  "eve_Cyrl",
+  "ewe_Latn",
+  "ewo_Latn",
+  "ext_Latn",
+  "eza_Latn",
+  "faa_Latn",
+  "fai_Latn",
+  "fal_Latn",
+  "fan_Latn",
+  "fao_Latn",
+  "far_Latn",
+  "fas_Arab",
+  "fat_Latn",
+  "ffm_Latn",
+  "fij_Latn",
+  "fil_Latn",
+  "fin_Latn",
+  "fit_Latn",
+  "fkv_Latn",
+  "fon_Latn",
+  "for_Latn",
+  "fra_Latn",
+  "frd_Latn",
+  "fro_Latn",
+  "frp_Latn",
+  "frr_Latn",
+  "fry_Latn",
+  "fub_Latn",
+  "fue_Latn",
+  "fuf_Latn",
+  "fuh_Latn",
+  "fur_Latn",
+  "fuv_Arab",
+  "fuv_Latn",
+  "gaa_Latn",
+  "gag_Cyrl",
+  "gag_Latn",
+  "gah_Latn",
+  "gai_Latn",
+  "gam_Latn",
+  "gaw_Latn",
+  "gaz_Latn",
+  "gbi_Latn",
+  "gbo_Latn",
+  "gcf_Latn",
+  "gcr_Latn",
+  "gde_Latn",
+  "gdg_Latn",
+  "gdn_Latn",
+  "gdr_Latn",
+  "geb_Latn",
+  "gfk_Latn",
+  "ghs_Latn",
+  "gil_Latn",
+  "gjn_Latn",
+  "gkn_Latn",
+  "gla_Latn",
+  "gle_Latn",
+  "glg_Latn",
+  "glk_Arab",
+  "glv_Latn",
+  "gmh_Latn",
+  "gmv_Ethi",
+  "gmv_Latn",
+  "gnb_Latn",
+  "gnd_Latn",
+  "gng_Latn",
+  "gnn_Latn",
+  "gnw_Latn",
+  "gof_Ethi",
+  "gof_Latn",
+  "gog_Latn",
+  "goh_Latn",
+  "gom_Deva",
+  "gom_Latn",
+  "gor_Latn",
+  "gos_Latn",
+  "got_Goth",
+  "gqr_Latn",
+  "grc_Grek",
+  "grt_Beng",
+  "gso_Latn",
+  "gsw_Latn",
+  "gub_Latn",
+  "guc_Latn",
+  "gug_Latn",
+  "guh_Latn",
+  "gui_Latn",
+  "guj_Gujr",
+  "guj_Latn",
+  "guk_Ethi",
+  "gul_Latn",
+  "gum_Latn",
+  "gun_Latn",
+  "guo_Latn",
+  "guq_Latn",
+  "gur_Latn",
+  "guw_Latn",
+  "gux_Latn",
+  "guz_Latn",
+  "gvc_Latn",
+  "gvf_Latn",
+  "gvl_Latn",
+  "gvn_Latn",
+  "gwi_Latn",
+  "gym_Latn",
+  "gyr_Latn",
+  "hac_Arab",
+  "hae_Latn",
+  "hag_Latn",
+  "hak_Hani",
+  "hak_Latn",
+  "hat_Latn",
+  "hau_Latn",
+  "hav_Latn",
+  "haw_Latn",
+  "hay_Latn",
+  "hbo_Hebr",
+  "hch_Latn",
+  "heb_Hebr",
+  "heg_Latn",
+  "heh_Latn",
+  "her_Latn",
+  "hif_Latn",
+  "hig_Latn",
+  "hil_Latn",
+  "hin_Deva",
+  "hin_Latn",
+  "hix_Latn",
+  "hla_Latn",
+  "hmo_Latn",
+  "hmr_Latn",
+  "hne_Deva",
+  "hnj_Latn",
+  "hnn_Latn",
+  "hns_Latn",
+  "hop_Latn",
+  "hot_Latn",
+  "hra_Latn",
+  "hrv_Latn",
+  "hrx_Latn",
+  "hsb_Latn",
+  "hto_Latn",
+  "hub_Latn",
+  "hui_Latn",
+  "hun_Latn",
+  "hus_Latn",
+  "huu_Latn",
+  "huv_Latn",
+  "hvn_Latn",
+  "hwc_Latn",
+  "hye_Armn",
+  "hyw_Armn",
+  "ian_Latn",
+  "iba_Latn",
+  "ibg_Latn",
+  "ibo_Latn",
+  "icr_Latn",
+  "ido_Latn",
+  "idu_Latn",
+  "ifb_Latn",
+  "ife_Latn",
+  "ifk_Latn",
+  "ifu_Latn",
+  "ify_Latn",
+  "ige_Latn",
+  "ign_Latn",
+  "ike_Cans",
+  "ikk_Latn",
+  "ikt_Latn",
+  "ikw_Latn",
+  "ile_Latn",
+  "ilo_Latn",
+  "imo_Latn",
+  "ina_Latn",
+  "inb_Latn",
+  "ind_Latn",
+  "inh_Cyrl",
+  "ino_Latn",
+  "iou_Latn",
+  "ipi_Latn",
+  "irk_Latn",
+  "isd_Latn",
+  "ish_Latn",
+  "isl_Latn",
+  "iso_Latn",
+  "ita_Latn",
+  "itv_Latn",
+  "ium_Latn",
+  "ivv_Latn",
+  "iws_Latn",
+  "ixl_Latn",
+  "izr_Latn",
+  "izz_Latn",
+  "jac_Latn",
+  "jae_Latn",
+  "jam_Latn",
+  "jav_Latn",
+  "jbo_Latn",
+  "jbu_Latn",
+  "jic_Latn",
+  "jiv_Latn",
+  "jpn_Jpan",
+  "jra_Latn",
+  "jvn_Latn",
+  "kaa_Cyrl",
+  "kaa_Latn",
+  "kab_Latn",
+  "kac_Latn",
+  "kal_Latn",
+  "kam_Latn",
+  "kan_Knda",
+  "kan_Latn",
+  "kao_Latn",
+  "kaq_Latn",
+  "kas_Arab",
+  "kas_Deva",
+  "kat_Geor",
+  "kaz_Cyrl",
+  "kbc_Latn",
+  "kbd_Cyrl",
+  "kbh_Latn",
+  "kbm_Latn",
+  "kbp_Latn",
+  "kbq_Latn",
+  "kbr_Latn",
+  "kby_Latn",
+  "kca_Cyrl",
+  "kcg_Latn",
+  "kck_Latn",
+  "kdc_Latn",
+  "kde_Latn",
+  "kdh_Latn",
+  "kdi_Latn",
+  "kdl_Latn",
+  "kea_Latn",
+  "kek_Latn",
+  "ken_Latn",
+  "keo_Latn",
+  "kew_Latn",
+  "kez_Latn",
+  "kff_Telu",
+  "kgf_Latn",
+  "kgk_Latn",
+  "kgp_Latn",
+  "kha_Latn",
+  "khk_Cyrl",
+  "khm_Khmr",
+  "khs_Latn",
+  "khz_Latn",
+  "kij_Latn",
+  "kik_Latn",
+  "kin_Latn",
+  "kir_Cyrl",
+  "kiu_Latn",
+  "kix_Latn",
+  "kjb_Latn",
+  "kje_Latn",
+  "kjh_Cyrl",
+  "kjs_Latn",
+  "kkc_Latn",
+  "kki_Latn",
+  "kkj_Latn",
+  "kkl_Latn",
+  "klt_Latn",
+  "klv_Latn",
+  "kmb_Latn",
+  "kmg_Latn",
+  "kmh_Latn",
+  "kmk_Latn",
+  "kmm_Latn",
+  "kmo_Latn",
+  "kmr_Cyrl",
+  "kmr_Latn",
+  "kms_Latn",
+  "kmu_Latn",
+  "kmy_Latn",
+  "knc_Arab",
+  "knc_Latn",
+  "kne_Latn",
+  "knf_Latn",
+  "kng_Latn",
+  "knj_Latn",
+  "knk_Latn",
+  "knv_Latn",
+  "koi_Cyrl",
+  "koo_Latn",
+  "kor_Hang",
+  "kos_Latn",
+  "kpe_Latn",
+  "kpf_Latn",
+  "kpg_Latn",
+  "kpj_Latn",
+  "kpr_Latn",
+  "kpv_Cyrl",
+  "kpw_Latn",
+  "kpx_Latn",
+  "kpz_Latn",
+  "kqc_Latn",
+  "kqe_Latn",
+  "kqf_Latn",
+  "kqn_Latn",
+  "kqp_Latn",
+  "kqw_Latn",
+  "krc_Cyrl",
+  "kri_Latn",
+  "krj_Latn",
+  "krl_Latn",
+  "kru_Deva",
+  "ksc_Latn",
+  "ksd_Latn",
+  "ksh_Latn",
+  "ksp_Latn",
+  "ksr_Latn",
+  "kss_Latn",
+  "ksw_Mymr",
+  "ktj_Latn",
+  "kto_Latn",
+  "ktu_Latn",
+  "ktz_Latn",
+  "kua_Latn",
+  "kud_Latn",
+  "kue_Latn",
+  "kum_Cyrl",
+  "kup_Latn",
+  "kus_Latn",
+  "kvg_Latn",
+  "kvn_Latn",
+  "kwd_Latn",
+  "kwf_Latn",
+  "kwi_Latn",
+  "kwj_Latn",
+  "kwn_Latn",
+  "kwy_Latn",
+  "kxc_Ethi",
+  "kxw_Latn",
+  "kyc_Latn",
+  "kyf_Latn",
+  "kyg_Latn",
+  "kyq_Latn",
+  "kyu_Kali",
+  "kyz_Latn",
+  "kze_Latn",
+  "kzj_Latn",
+  "lac_Latn",
+  "lad_Hebr",
+  "lad_Latn",
+  "lai_Latn",
+  "laj_Latn",
+  "lam_Latn",
+  "lao_Laoo",
+  "lat_Latn",
+  "lbb_Latn",
+  "lbe_Cyrl",
+  "lbj_Tibt",
+  "lbk_Latn",
+  "lcm_Latn",
+  "lcp_Thai",
+  "ldi_Latn",
+  "lee_Latn",
+  "lef_Latn",
+  "leh_Latn",
+  "leu_Latn",
+  "lew_Latn",
+  "lex_Latn",
+  "lez_Cyrl",
+  "lfn_Cyrl",
+  "lfn_Latn",
+  "lgg_Latn",
+  "lgl_Latn",
+  "lhu_Latn",
+  "lia_Latn",
+  "lid_Latn",
+  "lif_Deva",
+  "lif_Limb",
+  "lij_Latn",
+  "lim_Latn",
+  "lin_Latn",
+  "lip_Latn",
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+  "lit_Latn",
+  "liv_Latn",
+  "ljp_Latn",
+  "lki_Arab",
+  "lld_Latn",
+  "llg_Latn",
+  "lmk_Latn",
+  "lmo_Latn",
+  "lmp_Latn",
+  "lnd_Latn",
+  "log_Latn",
+  "loq_Latn",
+  "loz_Latn",
+  "lrc_Arab",
+  "lsi_Latn",
+  "lsm_Latn",
+  "ltg_Latn",
+  "ltz_Latn",
+  "lua_Latn",
+  "lub_Latn",
+  "luc_Latn",
+  "lue_Latn",
+  "lug_Latn",
+  "lun_Latn",
+  "luo_Latn",
+  "lus_Latn",
+  "lvs_Latn",
+  "lwg_Latn",
+  "lwo_Latn",
+  "lww_Latn",
+  "lzh_Hani",
+  "maa_Latn",
+  "mad_Latn",
+  "mag_Deva",
+  "mah_Latn",
+  "mai_Deva",
+  "maj_Latn",
+  "mak_Latn",
+  "mal_Latn",
+  "mal_Mlym",
+  "mam_Latn",
+  "maq_Latn",
+  "mar_Deva",
+  "mar_Latn",
+  "mas_Latn",
+  "mau_Latn",
+  "mav_Latn",
+  "maz_Latn",
+  "mbb_Latn",
+  "mbc_Latn",
+  "mbd_Latn",
+  "mbf_Latn",
+  "mbh_Latn",
+  "mbi_Latn",
+  "mbj_Latn",
+  "mbl_Latn",
+  "mbs_Latn",
+  "mbt_Latn",
+  "mca_Latn",
+  "mcb_Latn",
+  "mcd_Latn",
+  "mcf_Latn",
+  "mck_Latn",
+  "mco_Latn",
+  "mcp_Latn",
+  "mcq_Latn",
+  "mcu_Latn",
+  "mdf_Cyrl",
+  "mdy_Ethi",
+  "med_Latn",
+  "mee_Latn",
+  "mej_Latn",
+  "mek_Latn",
+  "men_Latn",
+  "meq_Latn",
+  "mer_Latn",
+  "meu_Latn",
+  "mfe_Latn",
+  "mfi_Latn",
+  "mfq_Latn",
+  "mfy_Latn",
+  "mfz_Latn",
+  "mgh_Latn",
+  "mgr_Latn",
+  "mhi_Latn",
+  "mhl_Latn",
+  "mhr_Cyrl",
+  "mhx_Latn",
+  "mib_Latn",
+  "mic_Latn",
+  "mie_Latn",
+  "mif_Latn",
+  "mig_Latn",
+  "mih_Latn",
+  "mil_Latn",
+  "mim_Latn",
+  "min_Latn",
+  "mio_Latn",
+  "mip_Latn",
+  "miq_Latn",
+  "mir_Latn",
+  "mit_Latn",
+  "miy_Latn",
+  "miz_Latn",
+  "mjc_Latn",
+  "mjw_Latn",
+  "mkd_Cyrl",
+  "mkl_Latn",
+  "mkn_Latn",
+  "mks_Latn",
+  "mlh_Latn",
+  "mlp_Latn",
+  "mlt_Latn",
+  "mlu_Latn",
+  "mmn_Latn",
+  "mmo_Latn",
+  "mmx_Latn",
+  "mna_Latn",
+  "mnb_Latn",
+  "mni_Beng",
+  "mni_Latn",
+  "mni_Mtei",
+  "mnk_Latn",
+  "mns_Cyrl",
+  "mnw_Mymr",
+  "mnx_Latn",
+  "mny_Latn",
+  "mog_Latn",
+  "moh_Latn",
+  "mop_Latn",
+  "mor_Latn",
+  "mos_Latn",
+  "mox_Latn",
+  "mph_Latn",
+  "mpm_Latn",
+  "mpp_Latn",
+  "mps_Latn",
+  "mpt_Latn",
+  "mpx_Latn",
+  "mqb_Latn",
+  "mqj_Latn",
+  "mqy_Latn",
+  "mri_Latn",
+  "mrj_Cyrl",
+  "mrq_Latn",
+  "mrw_Latn",
+  "msb_Latn",
+  "msk_Latn",
+  "msy_Latn",
+  "mta_Latn",
+  "mti_Latn",
+  "mto_Latn",
+  "mtp_Latn",
+  "muh_Latn",
+  "mui_Latn",
+  "mup_Deva",
+  "mur_Latn",
+  "mus_Latn",
+  "mux_Latn",
+  "muy_Latn",
+  "mva_Latn",
+  "mvn_Latn",
+  "mvp_Latn",
+  "mwc_Latn",
+  "mwl_Latn",
+  "mwn_Latn",
+  "mwp_Latn",
+  "mwq_Latn",
+  "mwv_Latn",
+  "mww_Latn",
+  "mxb_Latn",
+  "mxp_Latn",
+  "mxq_Latn",
+  "mxt_Latn",
+  "mxv_Latn",
+  "mya_Mymr",
+  "myk_Latn",
+  "myu_Latn",
+  "myv_Cyrl",
+  "myw_Latn",
+  "myx_Latn",
+  "myy_Latn",
+  "mza_Latn",
+  "mzh_Latn",
+  "mzk_Latn",
+  "mzn_Arab",
+  "mzw_Latn",
+  "mzz_Latn",
+  "naf_Latn",
+  "nah_Latn",
+  "nak_Latn",
+  "nan_Hani",
+  "nan_Latn",
+  "nap_Latn",
+  "naq_Latn",
+  "nas_Latn",
+  "nav_Latn",
+  "nba_Latn",
+  "nbe_Latn",
+  "nbl_Latn",
+  "nbq_Latn",
+  "nbu_Latn",
+  "nca_Latn",
+  "nch_Latn",
+  "ncj_Latn",
+  "ncl_Latn",
+  "ncq_Laoo",
+  "ncu_Latn",
+  "ncx_Latn",
+  "ndc_Latn",
+  "nde_Latn",
+  "ndh_Latn",
+  "ndo_Latn",
+  "nds_Latn",
+  "neb_Latn",
+  "new_Deva",
+  "nfa_Latn",
+  "nfr_Latn",
+  "ngl_Latn",
+  "ngp_Latn",
+  "ngu_Latn",
+  "nhe_Latn",
+  "nhg_Latn",
+  "nhi_Latn",
+  "nho_Latn",
+  "nhr_Latn",
+  "nhu_Latn",
+  "nhw_Latn",
+  "nhx_Latn",
+  "nhy_Latn",
+  "nia_Latn",
+  "nif_Latn",
+  "nii_Latn",
+  "nij_Latn",
+  "nim_Latn",
+  "nin_Latn",
+  "niu_Latn",
+  "niy_Latn",
+  "njb_Latn",
+  "njm_Latn",
+  "njn_Latn",
+  "njo_Latn",
+  "njz_Latn",
+  "nko_Latn",
+  "nld_Latn",
+  "nlg_Latn",
+  "nmf_Latn",
+  "nmo_Latn",
+  "nmz_Latn",
+  "nnb_Latn",
+  "nno_Latn",
+  "nnq_Latn",
+  "nnw_Latn",
+  "noa_Latn",
+  "nob_Latn",
+  "nod_Thai",
+  "nog_Cyrl",
+  "non_Latn",
+  "nop_Latn",
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+  "nou_Latn",
+  "nov_Latn",
+  "nph_Latn",
+  "npi_Deva",
+  "npi_Latn",
+  "npl_Latn",
+  "npy_Latn",
+  "nqo_Nkoo",
+  "nre_Latn",
+  "nrf_Latn",
+  "nrm_Latn",
+  "nse_Latn",
+  "nsm_Latn",
+  "nsn_Latn",
+  "nso_Latn",
+  "nss_Latn",
+  "nsu_Latn",
+  "ntp_Latn",
+  "ntr_Latn",
+  "ntu_Latn",
+  "nuj_Latn",
+  "nus_Latn",
+  "nuy_Latn",
+  "nvm_Latn",
+  "nwi_Latn",
+  "nya_Latn",
+  "nyf_Latn",
+  "nyn_Latn",
+  "nyo_Latn",
+  "nyu_Latn",
+  "nyy_Latn",
+  "nzi_Latn",
+  "nzm_Latn",
+  "obo_Latn",
+  "oci_Latn",
+  "ogo_Latn",
+  "ojb_Cans",
+  "ojb_Latn",
+  "oke_Latn",
+  "okv_Latn",
+  "old_Latn",
+  "olo_Latn",
+  "omw_Latn",
+  "ong_Latn",
+  "ons_Latn",
+  "opm_Latn",
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+  "ory_Orya",
+  "oss_Cyrl",
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+  "otm_Latn",
+  "oto_Latn",
+  "otq_Latn",
+  "ots_Latn",
+  "otw_Latn",
+  "ozm_Latn",
+  "pab_Latn",
+  "pad_Latn",
+  "pag_Latn",
+  "pah_Latn",
+  "pam_Latn",
+  "pan_Guru",
+  "pan_Latn",
+  "pao_Latn",
+  "pap_Latn",
+  "pau_Latn",
+  "pbb_Latn",
+  "pbt_Arab",
+  "pcd_Latn",
+  "pck_Latn",
+  "pcm_Latn",
+  "pdc_Latn",
+  "pdt_Latn",
+  "pem_Latn",
+  "pfl_Latn",
+  "pib_Latn",
+  "pio_Latn",
+  "pir_Latn",
+  "pis_Latn",
+  "pjt_Latn",
+  "plg_Latn",
+  "pls_Latn",
+  "plt_Latn",
+  "plu_Latn",
+  "plw_Latn",
+  "pma_Latn",
+  "pmf_Latn",
+  "pmq_Latn",
+  "pms_Latn",
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+  "pol_Latn",
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+  "pot_Latn",
+  "pov_Latn",
+  "poy_Latn",
+  "ppk_Latn",
+  "ppo_Latn",
+  "prf_Latn",
+  "prg_Latn",
+  "pri_Latn",
+  "ptp_Latn",
+  "ptu_Latn",
+  "pwg_Latn",
+  "pwn_Latn",
+  "pxm_Latn",
+  "qub_Latn",
+  "quc_Latn",
+  "quf_Latn",
+  "qug_Latn",
+  "quh_Latn",
+  "qup_Latn",
+  "quw_Latn",
+  "quy_Latn",
+  "quz_Latn",
+  "qva_Latn",
+  "qvc_Latn",
+  "qve_Latn",
+  "qvh_Latn",
+  "qvi_Latn",
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+  "qvn_Latn",
+  "qvs_Latn",
+  "qvw_Latn",
+  "qvz_Latn",
+  "qwh_Latn",
+  "qxh_Latn",
+  "qxl_Latn",
+  "qxn_Latn",
+  "qxo_Latn",
+  "qxr_Latn",
+  "rad_Latn",
+  "rai_Latn",
+  "rap_Latn",
+  "rar_Latn",
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+  "raw_Latn",
+  "rcf_Latn",
+  "rej_Latn",
+  "rgu_Latn",
+  "rhg_Latn",
+  "ria_Latn",
+  "rim_Latn",
+  "rkb_Latn",
+  "rki_Mymr",
+  "rmc_Latn",
+  "rml_Latn",
+  "rmn_Cyrl",
+  "rmn_Grek",
+  "rmn_Latn",
+  "rmo_Latn",
+  "rmq_Latn",
+  "rmy_Cyrl",
+  "rmy_Latn",
+  "rnd_Latn",
+  "rng_Latn",
+  "roh_Latn",
+  "ron_Cyrl",
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+  "rop_Latn",
+  "row_Latn",
+  "rro_Latn",
+  "rtm_Latn",
+  "rub_Latn",
+  "rue_Cyrl",
+  "ruf_Latn",
+  "rug_Latn",
+  "run_Latn",
+  "rup_Latn",
+  "rus_Cyrl",
+  "rwo_Latn",
+  "sab_Latn",
+  "sag_Latn",
+  "sah_Cyrl",
+  "san_Deva",
+  "san_Latn",
+  "sas_Latn",
+  "sat_Latn",
+  "sat_Olck",
+  "sba_Latn",
+  "sbe_Latn",
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+  "sby_Latn",
+  "sck_Deva",
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+  "sgb_Latn",
+  "sgc_Latn",
+  "sgh_Cyrl",
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+  "sgz_Latn",
+  "shi_Latn",
+  "shn_Mymr",
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+  "smj_Latn",
+  "smk_Latn",
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+  "soq_Latn",
+  "sot_Latn",
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+  "spa_Latn",
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+  "sps_Latn",
+  "spy_Latn",
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+  "srm_Latn",
+  "srn_Latn",
+  "srp_Cyrl",
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+  "srq_Latn",
+  "srr_Latn",
+  "ssd_Latn",
+  "ssg_Latn",
+  "ssw_Latn",
+  "ssx_Latn",
+  "stp_Latn",
+  "stq_Latn",
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+  "swh_Latn",
+  "swp_Latn",
+  "sxb_Latn",
+  "sxn_Latn",
+  "syb_Latn",
+  "syc_Syrc",
+  "syl_Beng",
+  "syl_Latn",
+  "szb_Latn",
+  "szl_Latn",
+  "szy_Latn",
+  "tab_Cyrl",
+  "tac_Latn",
+  "tah_Latn",
+  "taj_Deva",
+  "tam_Latn",
+  "tam_Taml",
+  "taq_Latn",
+  "taq_Tfng",
+  "tar_Latn",
+  "tat_Cyrl",
+  "tat_Latn",
+  "tav_Latn",
+  "taw_Latn",
+  "tay_Latn",
+  "tbc_Latn",
+  "tbg_Latn",
+  "tbo_Latn",
+  "tby_Latn",
+  "tbz_Latn",
+  "tca_Latn",
+  "tcf_Latn",
+  "tcs_Latn",
+  "tcy_Knda",
+  "tcz_Latn",
+  "tdt_Latn",
+  "ted_Latn",
+  "tee_Latn",
+  "tel_Latn",
+  "tel_Telu",
+  "tem_Latn",
+  "teo_Latn",
+  "ter_Latn",
+  "tet_Latn",
+  "tew_Latn",
+  "tfr_Latn",
+  "tgk_Cyrl",
+  "tgo_Latn",
+  "tgp_Latn",
+  "tha_Thai",
+  "thk_Latn",
+  "thl_Deva",
+  "tif_Latn",
+  "tih_Latn",
+  "tim_Latn",
+  "tir_Ethi",
+  "tiv_Latn",
+  "tiy_Latn",
+  "tkl_Latn",
+  "tkr_Cyrl",
+  "tku_Latn",
+  "tlf_Latn",
+  "tlh_Latn",
+  "tll_Latn",
+  "tly_Latn",
+  "tmd_Latn",
+  "tna_Latn",
+  "tnc_Latn",
+  "tnk_Latn",
+  "tnn_Latn",
+  "tnp_Latn",
+  "tob_Latn",
+  "toc_Latn",
+  "tod_Latn",
+  "tog_Latn",
+  "toh_Latn",
+  "toi_Latn",
+  "toj_Latn",
+  "tok_Latn",
+  "ton_Latn",
+  "too_Latn",
+  "top_Latn",
+  "tos_Latn",
+  "tpa_Latn",
+  "tpi_Latn",
+  "tpm_Latn",
+  "tpp_Latn",
+  "tpt_Latn",
+  "tpz_Latn",
+  "trc_Latn",
+  "trn_Latn",
+  "trp_Latn",
+  "trq_Latn",
+  "trv_Latn",
+  "tsc_Latn",
+  "tsg_Latn",
+  "tsn_Latn",
+  "tso_Latn",
+  "tsw_Latn",
+  "tsz_Latn",
+  "ttc_Latn",
+  "tte_Latn",
+  "ttj_Latn",
+  "tuc_Latn",
+  "tue_Latn",
+  "tuf_Latn",
+  "tui_Latn",
+  "tuk_Arab",
+  "tuk_Cyrl",
+  "tuk_Latn",
+  "tum_Latn",
+  "tuo_Latn",
+  "tur_Latn",
+  "tuv_Latn",
+  "tvk_Latn",
+  "tvl_Latn",
+  "twi_Latn",
+  "twu_Latn",
+  "txq_Latn",
+  "txu_Latn",
+  "tyv_Cyrl",
+  "tzh_Latn",
+  "tzj_Latn",
+  "tzm_Tfng",
+  "tzo_Latn",
+  "ubr_Latn",
+  "ubu_Latn",
+  "udm_Cyrl",
+  "udu_Latn",
+  "uig_Arab",
+  "uig_Cyrl",
+  "uig_Latn",
+  "ukr_Cyrl",
+  "umb_Latn",
+  "und_Bali",
+  "und_Bamu",
+  "und_Bopo",
+  "und_Cans",
+  "und_Cher",
+  "und_Cyrl",
+  "und_Deva",
+  "und_Egyp",
+  "und_Ethi",
+  "und_Geor",
+  "und_Goth",
+  "und_Grek",
+  "und_Hebr",
+  "und_Hluw",
+  "und_Hung",
+  "und_Kana",
+  "und_Khmr",
+  "und_Lana",
+  "und_Laoo",
+  "und_Lina",
+  "und_Mlym",
+  "und_Mong",
+  "und_Mymr",
+  "und_Newa",
+  "und_Orya",
+  "und_Shaw",
+  "und_Sylo",
+  "und_Syrc",
+  "und_Taml",
+  "und_Tang",
+  "und_Telu",
+  "und_Thaa",
+  "und_Thai",
+  "und_Tibt",
+  "und_Vaii",
+  "und_Xsux",
+  "und_Yiii",
+  "upv_Latn",
+  "ura_Latn",
+  "urb_Latn",
+  "urd_Arab",
+  "urd_Latn",
+  "urh_Latn",
+  "urk_Thai",
+  "urt_Latn",
+  "usa_Latn",
+  "usp_Latn",
+  "uth_Latn",
+  "uvh_Latn",
+  "uvl_Latn",
+  "uzn_Cyrl",
+  "uzn_Latn",
+  "uzs_Arab",
+  "vap_Latn",
+  "var_Latn",
+  "vec_Latn",
+  "ven_Latn",
+  "vep_Latn",
+  "vid_Latn",
+  "vie_Latn",
+  "viv_Latn",
+  "vls_Latn",
+  "vmw_Latn",
+  "vmy_Latn",
+  "vol_Latn",
+  "vro_Latn",
+  "vun_Latn",
+  "waj_Latn",
+  "wal_Latn",
+  "wap_Latn",
+  "war_Latn",
+  "way_Latn",
+  "wba_Latn",
+  "wbm_Latn",
+  "wbp_Latn",
+  "wed_Latn",
+  "wer_Latn",
+  "whk_Latn",
+  "wib_Latn",
+  "wim_Latn",
+  "wiu_Latn",
+  "wln_Latn",
+  "wls_Latn",
+  "wlv_Latn",
+  "wmt_Latn",
+  "wmw_Latn",
+  "wnc_Latn",
+  "wnu_Latn",
+  "wol_Latn",
+  "wos_Latn",
+  "wrk_Latn",
+  "wrs_Latn",
+  "wsk_Latn",
+  "wuu_Hani",
+  "wuv_Latn",
+  "xal_Cyrl",
+  "xav_Latn",
+  "xbi_Latn",
+  "xbr_Latn",
+  "xed_Latn",
+  "xho_Latn",
+  "xla_Latn",
+  "xmf_Geor",
+  "xmm_Latn",
+  "xmv_Latn",
+  "xnn_Latn",
+  "xog_Latn",
+  "xon_Latn",
+  "xrb_Latn",
+  "xsi_Latn",
+  "xsm_Latn",
+  "xsr_Deva",
+  "xsu_Latn",
+  "xtd_Latn",
+  "xtm_Latn",
+  "xtn_Latn",
+  "xuo_Latn",
+  "yaa_Latn",
+  "yad_Latn",
+  "yal_Latn",
+  "yan_Latn",
+  "yao_Latn",
+  "yap_Latn",
+  "yaq_Latn",
+  "ybb_Latn",
+  "yby_Latn",
+  "ycn_Latn",
+  "ydd_Hebr",
+  "yka_Latn",
+  "yle_Latn",
+  "yli_Latn",
+  "yml_Latn",
+  "yom_Latn",
+  "yon_Latn",
+  "yor_Latn",
+  "yrb_Latn",
+  "yre_Latn",
+  "yrk_Cyrl",
+  "yrl_Latn",
+  "yss_Latn",
+  "yua_Latn",
+  "yue_Hani",
+  "yuj_Latn",
+  "yut_Latn",
+  "yuw_Latn",
+  "yva_Latn",
+  "zaa_Latn",
+  "zab_Latn",
+  "zac_Latn",
+  "zad_Latn",
+  "zae_Latn",
+  "zai_Latn",
+  "zam_Latn",
+  "zao_Latn",
+  "zar_Latn",
+  "zas_Latn",
+  "zat_Latn",
+  "zav_Latn",
+  "zaw_Latn",
+  "zdj_Latn",
+  "zea_Latn",
+  "zgh_Tfng",
+  "zia_Latn",
+  "ziw_Latn",
+  "zne_Latn",
+  "zom_Latn",
+  "zos_Latn",
+  "zpa_Latn",
+  "zpc_Latn",
+  "zpg_Latn",
+  "zpi_Latn",
+  "zpl_Latn",
+  "zpm_Latn",
+  "zpo_Latn",
+  "zpt_Latn",
+  "zpu_Latn",
+  "zpv_Latn",
+  "zpz_Latn",
+  "zsm_Latn",
+  "zsr_Latn",
+  "ztq_Latn",
+  "zty_Latn",
+  "zul_Latn",
+  "zyb_Latn",
+  "zyp_Latn"
+ ],
+ "aliases": {
+  "ara_Arab": "arb_Arab",
+  "swa_Latn": "swh_Latn",
+  "sqi_Latn": "als_Latn",
+  "nep_Deva": "npi_Deva",
+  "orm_Latn": "gaz_Latn",
+  "ori_Orya": "ory_Orya",
+  "zho_Hani": "cmn_Hani",
+  "zho_Hans": "cmn_Hani",
+  "mlg_Latn": "plt_Latn",
+  "nor_Latn": "nob_Latn",
+  "pus_Arab": "pbt_Arab",
+  "prs_Arab": "fas_Arab",
+  "pes_Arab": "fas_Arab",
+  "tgl_Latn": "fil_Latn",
+  "kau_Latn": "knc_Latn",
+  "bal_Arab": "bcc_Arab",
+  "kok_Deva": "gom_Deva",
+  "kok_Latn": "gom_Latn",
+  "pan_Arab": "pnb_Arab",
+  "jpn_Hira": "jpn_Jpan",
+  "jpn_Kana": "jpn_Jpan",
+  "jpn_Hani": "jpn_Jpan"
+ },
+ "dropped": [
+  "din_Latn",
+  "ful_Latn",
+  "hbs_Latn",
+  "kln_Latn"
+ ],
+ "trained": [
+  "aai_Latn",
+  "aak_Latn",
+  "aau_Latn",
+  "aaz_Latn",
+  "aba_Latn",
+  "abk_Cyrl",
+  "abn_Latn",
+  "abq_Cyrl",
+  "abs_Latn",
+  "abt_Latn",
+  "abx_Latn",
+  "aby_Latn",
+  "abz_Latn",
+  "aca_Latn",
+  "acd_Latn",
+  "ace_Latn",
+  "acf_Latn",
+  "ach_Latn",
+  "acm_Arab",
+  "acn_Latn",
+  "acr_Latn",
+  "acu_Latn",
+  "ada_Latn",
+  "ade_Latn",
+  "adh_Latn",
+  "adi_Latn",
+  "adj_Latn",
+  "ady_Cyrl",
+  "adz_Latn",
+  "aeb_Arab",
+  "aeb_Latn",
+  "aer_Latn",
+  "aeu_Latn",
+  "aey_Latn",
+  "afr_Latn",
+  "agd_Latn",
+  "agg_Latn",
+  "agm_Latn",
+  "agn_Latn",
+  "agr_Latn",
+  "agt_Latn",
+  "agu_Latn",
+  "agx_Cyrl",
+  "ahk_Latn",
+  "aia_Latn",
+  "aii_Syrc",
+  "ain_Latn",
+  "ajg_Latn",
+  "aji_Latn",
+  "ajp_Arab",
+  "akb_Latn",
+  "ake_Latn",
+  "akh_Latn",
+  "akp_Latn",
+  "aln_Latn",
+  "alp_Latn",
+  "alq_Latn",
+  "als_Latn",
+  "alt_Cyrl",
+  "aly_Latn",
+  "alz_Latn",
+  "ame_Latn",
+  "amf_Latn",
+  "amh_Ethi",
+  "ami_Latn",
+  "amk_Latn",
+  "amm_Latn",
+  "amn_Latn",
+  "amp_Latn",
+  "amr_Latn",
+  "amu_Latn",
+  "amx_Latn",
+  "ang_Latn",
+  "ann_Latn",
+  "anp_Deva",
+  "anv_Latn",
+  "any_Latn",
+  "aoi_Latn",
+  "aoj_Latn",
+  "aom_Latn",
+  "aoz_Latn",
+  "apb_Latn",
+  "apc_Arab",
+  "ape_Latn",
+  "apn_Latn",
+  "apr_Latn",
+  "apt_Latn",
+  "apu_Latn",
+  "apw_Latn",
+  "apy_Latn",
+  "apz_Latn",
+  "arb_Arab",
+  "arb_Latn",
+  "are_Latn",
+  "arg_Latn",
+  "arl_Latn",
+  "arn_Latn",
+  "arp_Latn",
+  "arq_Arab",
+  "ars_Arab",
+  "ary_Arab",
+  "arz_Arab",
+  "asm_Beng",
+  "asm_Latn",
+  "aso_Latn",
+  "ast_Latn",
+  "ata_Latn",
+  "atb_Latn",
+  "atd_Latn",
+  "atg_Latn",
+  "ati_Latn",
+  "atj_Latn",
+  "att_Latn",
+  "auc_Latn",
+  "aui_Latn",
+  "auy_Latn",
+  "ava_Cyrl",
+  "avk_Latn",
+  "avt_Latn",
+  "avu_Latn",
+  "awa_Deva",
+  "awb_Latn",
+  "awx_Latn",
+  "ayp_Arab",
+  "ayr_Latn",
+  "azb_Arab",
+  "azg_Latn",
+  "azj_Cyrl",
+  "azj_Latn",
+  "azz_Latn",
+  "bak_Cyrl",
+  "bam_Latn",
+  "ban_Latn",
+  "bao_Latn",
+  "bar_Latn",
+  "bas_Latn",
+  "bba_Latn",
+  "bbb_Latn",
+  "bbc_Latn",
+  "bbr_Latn",
+  "bcc_Arab",
+  "bch_Latn",
+  "bci_Latn",
+  "bcl_Latn",
+  "bco_Latn",
+  "bcw_Latn",
+  "bdd_Latn",
+  "bdh_Latn",
+  "bdq_Latn",
+  "bea_Latn",
+  "bef_Latn",
+  "bel_Cyrl",
+  "bem_Latn",
+  "ben_Beng",
+  "ben_Latn",
+  "beq_Latn",
+  "bew_Latn",
+  "bfd_Latn",
+  "bgr_Latn",
+  "bgs_Latn",
+  "bgt_Latn",
+  "bgz_Latn",
+  "bhg_Latn",
+  "bhl_Latn",
+  "bho_Deva",
+  "bhw_Latn",
+  "bib_Latn",
+  "big_Latn",
+  "bim_Latn",
+  "bin_Latn",
+  "bis_Latn",
+  "biu_Latn",
+  "biv_Latn",
+  "bjn_Arab",
+  "bjn_Latn",
+  "bjp_Latn",
+  "bjr_Latn",
+  "bjv_Latn",
+  "bkd_Latn",
+  "bkq_Latn",
+  "bku_Latn",
+  "bla_Latn",
+  "blh_Latn",
+  "blk_Mymr",
+  "blt_Latn",
+  "blw_Latn",
+  "blz_Latn",
+  "bmh_Latn",
+  "bmk_Latn",
+  "bmq_Latn",
+  "bmr_Latn",
+  "bmu_Latn",
+  "bno_Latn",
+  "bnp_Latn",
+  "boa_Latn",
+  "bod_Tibt",
+  "boj_Latn",
+  "bon_Latn",
+  "bos_Latn",
+  "box_Latn",
+  "bpr_Latn",
+  "bps_Latn",
+  "bpy_Beng",
+  "bqc_Latn",
+  "bqp_Latn",
+  "bre_Latn",
+  "brh_Arab",
+  "bru_Latn",
+  "brx_Deva",
+  "bsn_Latn",
+  "bsp_Latn",
+  "bsq_Latn",
+  "bss_Latn",
+  "btd_Latn",
+  "bth_Latn",
+  "bts_Latn",
+  "btx_Latn",
+  "bud_Latn",
+  "bug_Latn",
+  "buk_Latn",
+  "bul_Cyrl",
+  "bum_Latn",
+  "bus_Latn",
+  "bvd_Latn",
+  "bvr_Latn",
+  "bvz_Latn",
+  "bwd_Latn",
+  "bwu_Latn",
+  "bxh_Latn",
+  "bxr_Cyrl",
+  "byr_Latn",
+  "byx_Latn",
+  "bzd_Latn",
+  "bzh_Latn",
+  "bzi_Thai",
+  "bzj_Latn",
+  "caa_Latn",
+  "cab_Latn",
+  "cac_Latn",
+  "caf_Latn",
+  "cag_Latn",
+  "cak_Latn",
+  "cao_Latn",
+  "cap_Latn",
+  "caq_Latn",
+  "car_Latn",
+  "cas_Latn",
+  "cat_Latn",
+  "cav_Latn",
+  "cax_Latn",
+  "cbc_Latn",
+  "cbi_Latn",
+  "cbk_Latn",
+  "cbr_Latn",
+  "cbs_Latn",
+  "cbt_Latn",
+  "cbu_Latn",
+  "cbv_Latn",
+  "cce_Latn",
+  "cco_Latn",
+  "ccp_Latn",
+  "ceb_Latn",
+  "ceg_Latn",
+  "cek_Latn",
+  "ces_Latn",
+  "cfm_Latn",
+  "cgc_Latn",
+  "cha_Latn",
+  "chd_Latn",
+  "che_Cyrl",
+  "chf_Latn",
+  "chk_Latn",
+  "cho_Latn",
+  "chq_Latn",
+  "chr_Cher",
+  "chu_Cyrl",
+  "chv_Cyrl",
+  "chw_Latn",
+  "chz_Latn",
+  "cjk_Latn",
+  "cjp_Latn",
+  "cjs_Cyrl",
+  "cjv_Latn",
+  "ckb_Arab",
+  "ckm_Latn",
+  "cko_Latn",
+  "ckt_Cyrl",
+  "clu_Latn",
+  "cme_Latn",
+  "cmn_Hani",
+  "cmo_Latn",
+  "cnh_Latn",
+  "cni_Latn",
+  "cnk_Latn",
+  "cnr_Latn",
+  "cnw_Latn",
+  "coe_Latn",
+  "cof_Latn",
+  "cok_Latn",
+  "con_Latn",
+  "cop_Copt",
+  "cor_Latn",
+  "cos_Latn",
+  "cot_Latn",
+  "cpb_Latn",
+  "cpc_Latn",
+  "cpu_Latn",
+  "cpy_Latn",
+  "crh_Cyrl",
+  "crh_Latn",
+  "crj_Cans",
+  "crk_Cans",
+  "crk_Latn",
+  "crl_Cans",
+  "crm_Cans",
+  "crn_Latn",
+  "crs_Latn",
+  "crx_Latn",
+  "csb_Latn",
+  "csw_Latn",
+  "csy_Latn",
+  "cta_Latn",
+  "ctd_Latn",
+  "cto_Latn",
+  "ctp_Latn",
+  "ctu_Latn",
+  "cub_Latn",
+  "cui_Latn",
+  "cuk_Latn",
+  "cul_Latn",
+  "cut_Latn",
+  "cux_Latn",
+  "cwe_Latn",
+  "cwt_Latn",
+  "cya_Latn",
+  "cym_Latn",
+  "czt_Latn",
+  "daa_Latn",
+  "dad_Latn",
+  "daf_Latn",
+  "dag_Latn",
+  "dah_Latn",
+  "dak_Latn",
+  "dan_Latn",
+  "dar_Cyrl",
+  "ddg_Latn",
+  "ded_Latn",
+  "des_Latn",
+  "deu_Latn",
+  "dga_Latn",
+  "dgc_Latn",
+  "dgi_Latn",
+  "dgr_Latn",
+  "dgz_Latn",
+  "dhm_Latn",
+  "dhv_Latn",
+  "dig_Latn",
+  "dik_Latn",
+  "diq_Latn",
+  "diu_Latn",
+  "div_Thaa",
+  "dje_Latn",
+  "djk_Latn",
+  "djr_Latn",
+  "dks_Latn",
+  "dng_Cyrl",
+  "dnj_Latn",
+  "dnw_Latn",
+  "dob_Latn",
+  "doi_Deva",
+  "dop_Latn",
+  "dos_Latn",
+  "drg_Latn",
+  "dru_Latn",
+  "dsb_Latn",
+  "dtp_Latn",
+  "dts_Latn",
+  "dua_Latn",
+  "duo_Latn",
+  "dur_Latn",
+  "dwr_Latn",
+  "dww_Latn",
+  "dyi_Latn",
+  "dyo_Latn",
+  "dyu_Latn",
+  "dzo_Tibt",
+  "ebk_Latn",
+  "efi_Latn",
+  "ekk_Latn",
+  "eko_Latn",
+  "ell_Grek",
+  "emi_Latn",
+  "eml_Latn",
+  "emp_Latn",
+  "enb_Latn",
+  "eng_Latn",
+  "enl_Latn",
+  "enm_Latn",
+  "enq_Latn",
+  "epo_Latn",
+  "eri_Latn",
+  "ese_Latn",
+  "esi_Latn",
+  "esk_Latn",
+  "ess_Latn",
+  "esu_Latn",
+  "etr_Latn",
+  "eus_Latn",
+  "eve_Cyrl",
+  "ewe_Latn",
+  "ewo_Latn",
+  "ext_Latn",
+  "eza_Latn",
+  "faa_Latn",
+  "fai_Latn",
+  "fal_Latn",
+  "fan_Latn",
+  "fao_Latn",
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+  "fas_Arab",
+  "fat_Latn",
+  "ffm_Latn",
+  "fij_Latn",
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+  "fin_Latn",
+  "fit_Latn",
+  "fkv_Latn",
+  "fon_Latn",
+  "for_Latn",
+  "fra_Latn",
+  "frd_Latn",
+  "fro_Latn",
+  "frp_Latn",
+  "frr_Latn",
+  "fry_Latn",
+  "fub_Latn",
+  "fue_Latn",
+  "fuf_Latn",
+  "fuh_Latn",
+  "fur_Latn",
+  "fuv_Arab",
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+  "gaa_Latn",
+  "gag_Cyrl",
+  "gag_Latn",
+  "gah_Latn",
+  "gai_Latn",
+  "gam_Latn",
+  "gaw_Latn",
+  "gaz_Latn",
+  "gbi_Latn",
+  "gbo_Latn",
+  "gcf_Latn",
+  "gcr_Latn",
+  "gde_Latn",
+  "gdg_Latn",
+  "gdn_Latn",
+  "gdr_Latn",
+  "geb_Latn",
+  "gfk_Latn",
+  "ghs_Latn",
+  "gil_Latn",
+  "gjn_Latn",
+  "gkn_Latn",
+  "gla_Latn",
+  "gle_Latn",
+  "glg_Latn",
+  "glk_Arab",
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+  "gmh_Latn",
+  "gmv_Ethi",
+  "gmv_Latn",
+  "gnb_Latn",
+  "gnd_Latn",
+  "gng_Latn",
+  "gnn_Latn",
+  "gnw_Latn",
+  "gof_Ethi",
+  "gof_Latn",
+  "gog_Latn",
+  "goh_Latn",
+  "gom_Deva",
+  "gom_Latn",
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+  "gos_Latn",
+  "got_Goth",
+  "gqr_Latn",
+  "grc_Grek",
+  "grt_Beng",
+  "gso_Latn",
+  "gsw_Latn",
+  "gub_Latn",
+  "guc_Latn",
+  "gug_Latn",
+  "guh_Latn",
+  "gui_Latn",
+  "guj_Gujr",
+  "guj_Latn",
+  "guk_Ethi",
+  "gul_Latn",
+  "gum_Latn",
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+  "gur_Latn",
+  "guw_Latn",
+  "gux_Latn",
+  "guz_Latn",
+  "gvc_Latn",
+  "gvf_Latn",
+  "gvl_Latn",
+  "gvn_Latn",
+  "gwi_Latn",
+  "gym_Latn",
+  "gyr_Latn",
+  "hac_Arab",
+  "hae_Latn",
+  "hag_Latn",
+  "hak_Hani",
+  "hak_Latn",
+  "hat_Latn",
+  "hau_Latn",
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+  "hbo_Hebr",
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+  "heb_Hebr",
+  "heg_Latn",
+  "heh_Latn",
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+  "hif_Latn",
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+  "hil_Latn",
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+  "hix_Latn",
+  "hla_Latn",
+  "hmo_Latn",
+  "hmr_Latn",
+  "hne_Deva",
+  "hnj_Latn",
+  "hnn_Latn",
+  "hns_Latn",
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+  "hra_Latn",
+  "hrv_Latn",
+  "hrx_Latn",
+  "hsb_Latn",
+  "hto_Latn",
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+  "hun_Latn",
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+  "huu_Latn",
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+  "hvn_Latn",
+  "hwc_Latn",
+  "hye_Armn",
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+  "ian_Latn",
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+  "ibg_Latn",
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+  "icr_Latn",
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+  "idu_Latn",
+  "ifb_Latn",
+  "ife_Latn",
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+  "ikw_Latn",
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+  "imo_Latn",
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+  "ino_Latn",
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+  "ish_Latn",
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+  "iso_Latn",
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+  "ium_Latn",
+  "ivv_Latn",
+  "iws_Latn",
+  "ixl_Latn",
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+  "jac_Latn",
+  "jae_Latn",
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+  "jic_Latn",
+  "jiv_Latn",
+  "jpn_Jpan",
+  "jra_Latn",
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+  "kaa_Cyrl",
+  "kaa_Latn",
+  "kab_Latn",
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+  "kbd_Cyrl",
+  "kbh_Latn",
+  "kbm_Latn",
+  "kbp_Latn",
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+  "kbr_Latn",
+  "kby_Latn",
+  "kca_Cyrl",
+  "kcg_Latn",
+  "kck_Latn",
+  "kdc_Latn",
+  "kde_Latn",
+  "kdh_Latn",
+  "kdi_Latn",
+  "kdl_Latn",
+  "kea_Latn",
+  "kek_Latn",
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+  "kff_Telu",
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+  "kha_Latn",
+  "khk_Cyrl",
+  "khm_Khmr",
+  "khs_Latn",
+  "khz_Latn",
+  "kij_Latn",
+  "kik_Latn",
+  "kin_Latn",
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+  "kjb_Latn",
+  "kje_Latn",
+  "kjh_Cyrl",
+  "kjs_Latn",
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+  "kki_Latn",
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+  "kkl_Latn",
+  "klt_Latn",
+  "klv_Latn",
+  "kmb_Latn",
+  "kmg_Latn",
+  "kmh_Latn",
+  "kmk_Latn",
+  "kmm_Latn",
+  "kmo_Latn",
+  "kmr_Cyrl",
+  "kmr_Latn",
+  "kms_Latn",
+  "kmu_Latn",
+  "kmy_Latn",
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+  "kne_Latn",
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+  "kng_Latn",
+  "knj_Latn",
+  "knk_Latn",
+  "knv_Latn",
+  "koi_Cyrl",
+  "koo_Latn",
+  "kor_Hang",
+  "kos_Latn",
+  "kpe_Latn",
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+  "kpg_Latn",
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+  "kpr_Latn",
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+  "kpx_Latn",
+  "kpz_Latn",
+  "kqc_Latn",
+  "kqe_Latn",
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+  "kqn_Latn",
+  "kqp_Latn",
+  "kqw_Latn",
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+  "kri_Latn",
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+  "pwn_Latn",
+  "pxm_Latn",
+  "qub_Latn",
+  "quc_Latn",
+  "quf_Latn",
+  "qug_Latn",
+  "quh_Latn",
+  "qup_Latn",
+  "quw_Latn",
+  "quy_Latn",
+  "quz_Latn",
+  "qva_Latn",
+  "qvc_Latn",
+  "qve_Latn",
+  "qvh_Latn",
+  "qvi_Latn",
+  "qvm_Latn",
+  "qvn_Latn",
+  "qvs_Latn",
+  "qvw_Latn",
+  "qvz_Latn",
+  "qwh_Latn",
+  "qxh_Latn",
+  "qxl_Latn",
+  "qxn_Latn",
+  "qxo_Latn",
+  "qxr_Latn",
+  "rad_Latn",
+  "rai_Latn",
+  "rap_Latn",
+  "rar_Latn",
+  "rav_Deva",
+  "raw_Latn",
+  "rcf_Latn",
+  "rej_Latn",
+  "rgu_Latn",
+  "rhg_Latn",
+  "ria_Latn",
+  "rim_Latn",
+  "rkb_Latn",
+  "rki_Mymr",
+  "rmc_Latn",
+  "rml_Latn",
+  "rmn_Cyrl",
+  "rmn_Grek",
+  "rmn_Latn",
+  "rmo_Latn",
+  "rmq_Latn",
+  "rmy_Cyrl",
+  "rmy_Latn",
+  "rnd_Latn",
+  "rng_Latn",
+  "roh_Latn",
+  "ron_Cyrl",
+  "ron_Latn",
+  "roo_Latn",
+  "rop_Latn",
+  "row_Latn",
+  "rro_Latn",
+  "rtm_Latn",
+  "rub_Latn",
+  "rue_Cyrl",
+  "ruf_Latn",
+  "rug_Latn",
+  "run_Latn",
+  "rup_Latn",
+  "rus_Cyrl",
+  "rwo_Latn",
+  "sab_Latn",
+  "sag_Latn",
+  "sah_Cyrl",
+  "san_Deva",
+  "san_Latn",
+  "sas_Latn",
+  "sat_Latn",
+  "sat_Olck",
+  "sba_Latn",
+  "sbe_Latn",
+  "sbl_Latn",
+  "sby_Latn",
+  "sck_Deva",
+  "scn_Latn",
+  "sco_Latn",
+  "sda_Latn",
+  "sdc_Latn",
+  "sdh_Arab",
+  "seh_Latn",
+  "ses_Latn",
+  "sey_Latn",
+  "sfw_Latn",
+  "sgb_Latn",
+  "sgc_Latn",
+  "sgh_Cyrl",
+  "sgs_Latn",
+  "sgw_Ethi",
+  "sgz_Latn",
+  "shi_Latn",
+  "shn_Mymr",
+  "shp_Latn",
+  "sid_Latn",
+  "sig_Latn",
+  "sil_Latn",
+  "sim_Latn",
+  "sin_Sinh",
+  "sja_Latn",
+  "skg_Latn",
+  "skr_Arab",
+  "slk_Latn",
+  "sll_Latn",
+  "slv_Latn",
+  "sma_Latn",
+  "sme_Latn",
+  "smj_Latn",
+  "smk_Latn",
+  "sml_Latn",
+  "smn_Latn",
+  "smo_Latn",
+  "sms_Latn",
+  "sna_Latn",
+  "snc_Latn",
+  "snd_Arab",
+  "snd_Deva",
+  "snd_Latn",
+  "snf_Latn",
+  "snn_Latn",
+  "snp_Latn",
+  "snw_Latn",
+  "sny_Latn",
+  "som_Latn",
+  "sop_Latn",
+  "soq_Latn",
+  "sot_Latn",
+  "soy_Latn",
+  "spa_Latn",
+  "spl_Latn",
+  "spm_Latn",
+  "spp_Latn",
+  "sps_Latn",
+  "spy_Latn",
+  "srd_Latn",
+  "srm_Latn",
+  "srn_Latn",
+  "srp_Cyrl",
+  "srp_Latn",
+  "srq_Latn",
+  "srr_Latn",
+  "ssd_Latn",
+  "ssg_Latn",
+  "ssw_Latn",
+  "ssx_Latn",
+  "stp_Latn",
+  "stq_Latn",
+  "sua_Latn",
+  "suc_Latn",
+  "sue_Latn",
+  "suk_Latn",
+  "sun_Latn",
+  "sur_Latn",
+  "sus_Arab",
+  "sus_Latn",
+  "suz_Deva",
+  "swc_Latn",
+  "swe_Latn",
+  "swg_Latn",
+  "swh_Latn",
+  "swp_Latn",
+  "sxb_Latn",
+  "sxn_Latn",
+  "syb_Latn",
+  "syc_Syrc",
+  "syl_Beng",
+  "syl_Latn",
+  "szb_Latn",
+  "szl_Latn",
+  "szy_Latn",
+  "tab_Cyrl",
+  "tac_Latn",
+  "tah_Latn",
+  "taj_Deva",
+  "tam_Latn",
+  "tam_Taml",
+  "taq_Latn",
+  "taq_Tfng",
+  "tar_Latn",
+  "tat_Cyrl",
+  "tat_Latn",
+  "tav_Latn",
+  "taw_Latn",
+  "tay_Latn",
+  "tbc_Latn",
+  "tbg_Latn",
+  "tbo_Latn",
+  "tby_Latn",
+  "tbz_Latn",
+  "tca_Latn",
+  "tcf_Latn",
+  "tcs_Latn",
+  "tcy_Knda",
+  "tcz_Latn",
+  "tdt_Latn",
+  "ted_Latn",
+  "tee_Latn",
+  "tel_Latn",
+  "tel_Telu",
+  "tem_Latn",
+  "teo_Latn",
+  "ter_Latn",
+  "tet_Latn",
+  "tew_Latn",
+  "tfr_Latn",
+  "tgk_Cyrl",
+  "tgo_Latn",
+  "tgp_Latn",
+  "tha_Thai",
+  "thk_Latn",
+  "thl_Deva",
+  "tif_Latn",
+  "tih_Latn",
+  "tim_Latn",
+  "tir_Ethi",
+  "tiv_Latn",
+  "tiy_Latn",
+  "tkl_Latn",
+  "tkr_Cyrl",
+  "tku_Latn",
+  "tlf_Latn",
+  "tlh_Latn",
+  "tll_Latn",
+  "tly_Latn",
+  "tmd_Latn",
+  "tna_Latn",
+  "tnc_Latn",
+  "tnk_Latn",
+  "tnn_Latn",
+  "tnp_Latn",
+  "tob_Latn",
+  "toc_Latn",
+  "tod_Latn",
+  "tog_Latn",
+  "toh_Latn",
+  "toi_Latn",
+  "toj_Latn",
+  "tok_Latn",
+  "ton_Latn",
+  "too_Latn",
+  "top_Latn",
+  "tos_Latn",
+  "tpa_Latn",
+  "tpi_Latn",
+  "tpm_Latn",
+  "tpp_Latn",
+  "tpt_Latn",
+  "tpz_Latn",
+  "trc_Latn",
+  "trn_Latn",
+  "trp_Latn",
+  "trq_Latn",
+  "trv_Latn",
+  "tsc_Latn",
+  "tsg_Latn",
+  "tsn_Latn",
+  "tso_Latn",
+  "tsw_Latn",
+  "tsz_Latn",
+  "ttc_Latn",
+  "tte_Latn",
+  "ttj_Latn",
+  "tuc_Latn",
+  "tue_Latn",
+  "tuf_Latn",
+  "tui_Latn",
+  "tuk_Arab",
+  "tuk_Cyrl",
+  "tuk_Latn",
+  "tum_Latn",
+  "tuo_Latn",
+  "tur_Latn",
+  "tuv_Latn",
+  "tvk_Latn",
+  "tvl_Latn",
+  "twi_Latn",
+  "twu_Latn",
+  "txq_Latn",
+  "txu_Latn",
+  "tyv_Cyrl",
+  "tzh_Latn",
+  "tzj_Latn",
+  "tzm_Tfng",
+  "tzo_Latn",
+  "ubr_Latn",
+  "ubu_Latn",
+  "udm_Cyrl",
+  "udu_Latn",
+  "uig_Arab",
+  "uig_Cyrl",
+  "uig_Latn",
+  "ukr_Cyrl",
+  "umb_Latn",
+  "und_Bali",
+  "und_Bamu",
+  "und_Bopo",
+  "und_Cans",
+  "und_Cher",
+  "und_Cyrl",
+  "und_Deva",
+  "und_Egyp",
+  "und_Ethi",
+  "und_Geor",
+  "und_Goth",
+  "und_Grek",
+  "und_Hebr",
+  "und_Hluw",
+  "und_Hung",
+  "und_Kana",
+  "und_Khmr",
+  "und_Lana",
+  "und_Laoo",
+  "und_Lina",
+  "und_Mlym",
+  "und_Mong",
+  "und_Mymr",
+  "und_Newa",
+  "und_Orya",
+  "und_Shaw",
+  "und_Sylo",
+  "und_Syrc",
+  "und_Taml",
+  "und_Tang",
+  "und_Telu",
+  "und_Thaa",
+  "und_Thai",
+  "und_Tibt",
+  "und_Vaii",
+  "und_Xsux",
+  "und_Yiii",
+  "upv_Latn",
+  "ura_Latn",
+  "urb_Latn",
+  "urd_Arab",
+  "urd_Latn",
+  "urh_Latn",
+  "urk_Thai",
+  "urt_Latn",
+  "usa_Latn",
+  "usp_Latn",
+  "uth_Latn",
+  "uvh_Latn",
+  "uvl_Latn",
+  "uzn_Cyrl",
+  "uzn_Latn",
+  "uzs_Arab",
+  "vap_Latn",
+  "var_Latn",
+  "vec_Latn",
+  "ven_Latn",
+  "vep_Latn",
+  "vid_Latn",
+  "vie_Latn",
+  "viv_Latn",
+  "vls_Latn",
+  "vmw_Latn",
+  "vmy_Latn",
+  "vol_Latn",
+  "vro_Latn",
+  "vun_Latn",
+  "waj_Latn",
+  "wal_Latn",
+  "wap_Latn",
+  "war_Latn",
+  "way_Latn",
+  "wba_Latn",
+  "wbm_Latn",
+  "wbp_Latn",
+  "wed_Latn",
+  "wer_Latn",
+  "whk_Latn",
+  "wib_Latn",
+  "wim_Latn",
+  "wiu_Latn",
+  "wln_Latn",
+  "wls_Latn",
+  "wlv_Latn",
+  "wmt_Latn",
+  "wmw_Latn",
+  "wnc_Latn",
+  "wnu_Latn",
+  "wol_Latn",
+  "wos_Latn",
+  "wrk_Latn",
+  "wrs_Latn",
+  "wsk_Latn",
+  "wuu_Hani",
+  "wuv_Latn",
+  "xal_Cyrl",
+  "xav_Latn",
+  "xbi_Latn",
+  "xbr_Latn",
+  "xed_Latn",
+  "xho_Latn",
+  "xla_Latn",
+  "xmf_Geor",
+  "xmm_Latn",
+  "xmv_Latn",
+  "xnn_Latn",
+  "xog_Latn",
+  "xon_Latn",
+  "xrb_Latn",
+  "xsi_Latn",
+  "xsm_Latn",
+  "xsr_Deva",
+  "xsu_Latn",
+  "xtd_Latn",
+  "xtm_Latn",
+  "xtn_Latn",
+  "xuo_Latn",
+  "yaa_Latn",
+  "yad_Latn",
+  "yal_Latn",
+  "yan_Latn",
+  "yao_Latn",
+  "yap_Latn",
+  "yaq_Latn",
+  "ybb_Latn",
+  "yby_Latn",
+  "ycn_Latn",
+  "ydd_Hebr",
+  "yka_Latn",
+  "yle_Latn",
+  "yli_Latn",
+  "yml_Latn",
+  "yom_Latn",
+  "yon_Latn",
+  "yor_Latn",
+  "yrb_Latn",
+  "yre_Latn",
+  "yrk_Cyrl",
+  "yrl_Latn",
+  "yss_Latn",
+  "yua_Latn",
+  "yue_Hani",
+  "yuj_Latn",
+  "yut_Latn",
+  "yuw_Latn",
+  "yva_Latn",
+  "zaa_Latn",
+  "zab_Latn",
+  "zac_Latn",
+  "zad_Latn",
+  "zae_Latn",
+  "zai_Latn",
+  "zam_Latn",
+  "zao_Latn",
+  "zar_Latn",
+  "zas_Latn",
+  "zat_Latn",
+  "zav_Latn",
+  "zaw_Latn",
+  "zdj_Latn",
+  "zea_Latn",
+  "zgh_Tfng",
+  "zia_Latn",
+  "ziw_Latn",
+  "zne_Latn",
+  "zom_Latn",
+  "zos_Latn",
+  "zpa_Latn",
+  "zpc_Latn",
+  "zpg_Latn",
+  "zpi_Latn",
+  "zpl_Latn",
+  "zpm_Latn",
+  "zpo_Latn",
+  "zpt_Latn",
+  "zpu_Latn",
+  "zpv_Latn",
+  "zpz_Latn",
+  "zsm_Latn",
+  "zsr_Latn",
+  "ztq_Latn",
+  "zty_Latn",
+  "zul_Latn",
+  "zyb_Latn",
+  "zyp_Latn"
+ ]
+}

millid.py

@@ -0,0 +1,1529 @@
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "torch>=2.4",
+#     "numpy",
+#     "pandas",
+#     "pyarrow",
+#     "huggingface_hub>=0.30",
+#     "safetensors",
+#     "fasttext-wheel",
+#     "wandb",
+#     "tyro>=0.8",
+# ]
+# ///
+"""MillID: MultiLingual Language IDentifier.
+
+A byte-level, encoder-only transformer that names the language of a passage of text, over
+the label set of baobabtech/millid-dataset (1,000+ language-script labels).
+
+One file, three uses:
+
+    # train on Hugging Face Jobs, evaluate against GlotLID, push to the Hub
+    hf jobs uv run --flavor h200 --timeout 4h --secrets HF_TOKEN \\
+        millid/millid.py train --dataset baobabtech/millid-dataset --push-to baobabtech/millid
+
+    # evaluate a trained model
+    python millid.py evaluate --model baobabtech/millid --dataset baobabtech/millid-dataset
+
+    # tune hyperparameters with a Weights & Biases sweep (agents run as Jobs too)
+    wandb sweep millid/sweep.yaml          # prints ENTITY/PROJECT/SWEEP_ID
+    hf jobs uv run --flavor h200 --secrets HF_TOKEN --secrets WANDB_API_KEY \\
+        millid/millid.py sweep-agent --sweep-id ENTITY/PROJECT/SWEEP_ID --count 6
+
+    # use it
+    from millid import MillID
+    model = MillID.from_pretrained("baobabtech/millid")
+    model.predict(["Lave men ou ak savon."], k=3)  # [[("hat_Latn", 0.98), ...]]
+
+One universal encoding
+----------------------
+Text: Unicode NFKC, whitespace collapsed, then UTF-8 bytes. Every script in every language is the
+same 256 symbols, so there is no tokenizer to train, no vocabulary to outgrow and no unknown
+token; a script the model has never seen is still bytes it can read.
+
+Labels: ISO 639-3 plus ISO 15924 script (`eng_Latn`, `amh_Ethi`), the dataset's labels.json
+fixing label i to labels[i]. A model trained with extra sources keeps those ids and appends its
+own labels after them.
+
+Architecture (ARCHITECTURE.md has the full design)
+------------
+    UTF-8 bytes, each embedded as byte + script + byte-in-character + hashed char 2/3/4-grams
+                -> depthwise convolution stem -> strided convolution (2 bytes per token)
+                -> N pre-norm blocks: RMSNorm, attention with RoPE, RMSNorm, SwiGLU
+                -> masked mean pooling -> language head (label + language + script vectors)
+
+Presets: tiny ~2M and small ~12M for trials, medium ~114M (default), large ~190M.
+
+The model is two parts, and the split is deliberate. `MillIDTextEncoder` turns bytes into one
+d_model vector; `LanguageHead` turns a vector into a language. MillID v2 adds images: an image
+encoder producing vectors in the same d_model space, fused with the text vector (or used alone
+for a scan with no text layer) before the same head. Checkpoint keys are `text.*` and `head.*`
+from v1 on, so v1 weights load into v2 unchanged.
+
+Configuration is CleanRL-style: each command is a typed dataclass (`TrainArgs`, ...) whose
+fields are the flags and whose field docstrings are the help, parsed by tyro. `python millid.py
+train --help` lists every setting; the resolved settings are saved with each run, in
+config.json and in W&B.
+
+Tracking: with WANDB_API_KEY set (or --wandb-project), every run logs its hyperparameters,
+training loss, learning rate, gradient norm and throughput, the validation curve, and the
+final per-set metrics and per-language tables to Weights & Biases.
+"""
+
+from __future__ import annotations
+
+import json
+import math
+import os
+import random
+import shutil
+import sys
+import time
+import unicodedata
+import zlib
+from dataclasses import asdict, dataclass, field
+from functools import lru_cache
+from pathlib import Path
+from typing import Literal
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+# --- the universal text encoding ---------------------------------------------------------
+#
+# A passage becomes four aligned sequences, one entry per UTF-8 byte:
+#
+#   ids      the byte, shifted by OFFSET; CLS first
+#   script   the Unicode script of the character the byte belongs to
+#   charpos  1 a one-byte character, 2 the first byte of a longer one, 3 a continuation byte
+#   ngrams   hashed character n-grams of 2, 3 and 4 characters ending at the byte's character
+#
+# Bytes make every language the same 256 symbols. The other three tell the model what bytes
+# alone hide: which writing system it is reading, where characters begin, and the character
+# n-grams that fastText and GlotLID identify languages by.
+
+PAD, CLS, EOS = 0, 1, 2
+OFFSET = 3  # byte b is token b + OFFSET
+VOCAB_SIZE = 256 + OFFSET
+SCRIPT_BUCKETS = 512
+SCRIPT_COMMON = 1  # digits, punctuation, symbols, spaces: shared by every script
+NGRAM_ORDERS = (2, 3, 4)
+
+
+def normalize(text: str) -> str:
+    """NFKC folds what PDF text layers and keyboards differ on (ligatures, full-width forms,
+    Arabic presentation forms) without changing the language of anything. Runs of whitespace
+    become one space."""
+    return " ".join(unicodedata.normalize("NFKC", str(text)).split())
+
+
+@lru_cache(maxsize=1 << 16)
+def char_script(ch: str) -> int:
+    """The script of a letter or mark, as an id: the first word of its Unicode name (LATIN,
+    ETHIOPIC, DEVANAGARI, CJK, HANGUL, ADLAM...), hashed. Python has no Unicode script property;
+    the name's first word is the same grouping for letters and marks, and hashing keeps the ids
+    stable when a newer Unicode adds scripts."""
+    if unicodedata.category(ch)[0] not in "LM":
+        return SCRIPT_COMMON
+    word = unicodedata.name(ch, "").split(" ", 1)[0]
+    if not word:
+        return SCRIPT_COMMON
+    return 2 + zlib.crc32(word.encode()) % (SCRIPT_BUCKETS - 2)
+
+
+_SCRIPT_TABLE_SIZE = 0x40000  # planes 0 to 3, every script in use; beyond, char_script per character
+_script_table: np.ndarray | None = None
+
+
+def script_table() -> np.ndarray:
+    """char_script for every code point below 0x40000, built once per process (about a second).
+    Built before the DataLoader forks, the workers share it."""
+    global _script_table
+    if _script_table is None:
+        uncached = char_script.__wrapped__
+        _script_table = np.fromiter((uncached(chr(c)) for c in range(_SCRIPT_TABLE_SIZE)), dtype=np.int64, count=_SCRIPT_TABLE_SIZE)
+    return _script_table
+
+
+# The n-gram hash: a polynomial over lowercased code points, read from the character back,
+# then splitmix64, so neighbouring n-grams land in unrelated buckets. Before the passage every
+# position holds _START, which no code point equals, so a word at the start of a passage has
+# n-grams of its own. numpy's uint64 arithmetic wraps, which is the modulus.
+_START = 0x110000
+_PRIME = np.uint64(0x100000001B3)
+_MIX1, _MIX2 = np.uint64(0xBF58476D1CE4E5B9), np.uint64(0x94D049BB133111EB)
+_ORDER_SEED = {n: np.uint64((0x9E3779B97F4A7C15 * n) & 0xFFFFFFFFFFFFFFFF) for n in NGRAM_ORDERS}
+
+
+def _splitmix(x: np.ndarray) -> np.ndarray:
+    x = (x ^ (x >> np.uint64(30))) * _MIX1
+    x = (x ^ (x >> np.uint64(27))) * _MIX2
+    return x ^ (x >> np.uint64(31))
+
+
+def _codepoints(text: str) -> np.ndarray:
+    # "replace": a lone surrogate (broken PDF text) becomes "?", one code point for one
+    return np.frombuffer(text.encode("utf-32-le", "replace"), dtype=np.uint32)
+
+
+_UTF8_LEAD = np.array([0, 0x00, 0xC0, 0xE0, 0xF0], dtype=np.int64)  # by sequence length
+
+
+def encode_batch(
+    texts: list[str], max_len: int = 256, ngram_buckets: int = 32768, rng: random.Random | None = None, pad_to: int | None = None
+) -> dict[str, torch.Tensor]:
+    """Passages as the model's input: the four per-byte sequences, CLS first, padded to the
+    longest (or to pad_to, for the fixed shapes torch.compile wants), and the mask. A passage
+    longer than max_len bytes is cut to a window of whole characters: its start at inference,
+    a random place in training.
+
+    The whole batch is one string, and every step one numpy operation over it, because the
+    training data workers run this for every batch: per passage it would be dozens of small
+    array operations, whose overhead is most of the cost."""
+    texts = [normalize(t) for t in texts]
+    b, budget = len(texts), max_len - 1
+    joined = "".join(texts)
+    cp = _codepoints(joined)
+    low = joined.lower()  # lower() may lengthen a character; then keep the first of its lowercase
+    low = _codepoints(low) if len(low) == len(joined) else _codepoints("".join(ch.lower()[0] for ch in joined))
+    lens = np.fromiter(map(len, texts), dtype=np.int64, count=b)
+    first = np.cumsum(lens) - lens  # each passage's first character in cp
+    after = first + lens
+    sizes = (1 + (cp >= 0x80) + (cp >= 0x800) + (cp >= 0x10000)).astype(np.int64)  # UTF-8 bytes per character
+    ends = np.concatenate([[0], np.cumsum(sizes)])  # ends[k]: the bytes before character k
+
+    # Each passage's window, characters lo..hi-1 of cp.
+    lo = first.copy()
+    if rng is not None:
+        long = np.flatnonzero(ends[after] - ends[first] > budget)
+        if len(long):
+            last = np.searchsorted(ends, ends[after[long]] - budget, side="left")  # the first start that fits the rest
+            u = np.array([rng.random() for _ in long])
+            lo[long] += (u * (last - first[long])).astype(np.int64)  # every start before `last` fills the window
+    hi = np.minimum(np.searchsorted(ends, ends[lo] + budget, side="right") - 1, after)
+    w = hi - lo
+    at = np.repeat(lo - (np.cumsum(w) - w), w) + np.arange(w.sum())  # every window character, in order
+    c = cp[at].astype(np.int64)
+
+    inside = c < _SCRIPT_TABLE_SIZE
+    script = script_table()[np.where(inside, c, 0)]
+    for k in np.flatnonzero(~inside):
+        script[k] = char_script(chr(c[k]))
+
+    # n-grams: the characters from this one back, _START before the start of its passage
+    into = at - np.repeat(first, w)
+    grams = np.empty((len(at), len(NGRAM_ORDERS)), dtype=np.int64)
+    h = np.zeros(len(at), dtype=np.uint64)
+    for n in range(1, max(NGRAM_ORDERS) + 1):
+        h = h * _PRIME + np.where(into >= n - 1, low[np.maximum(at - (n - 1), 0)], _START).astype(np.uint64)
+        if n in _ORDER_SEED:
+            grams[:, NGRAM_ORDERS.index(n)] = (_splitmix(h ^ _ORDER_SEED[n]) % np.uint64(ngram_buckets - 1)).astype(np.int64) + 1
+
+    # UTF-8, from the code points: byte k of an s-byte character
+    s = sizes[at]
+    cb, sb = np.repeat(c, s), np.repeat(s, s)
+    k = np.arange(len(cb)) - np.repeat(np.cumsum(s) - s, s)
+    shifted = cb >> (6 * (sb - 1 - k))
+    raw = np.where(k == 0, shifted | _UTF8_LEAD[sb], (shifted & 0x3F) | 0x80)
+    charpos = np.where(sb == 1, 1, np.where(k == 0, 2, 3))
+
+    nb = ends[hi] - ends[lo]  # bytes per passage
+    row = np.repeat(np.arange(b), nb)
+    col = 1 + np.arange(len(cb)) - np.repeat(np.cumsum(nb) - nb, nb)
+    width = max(1 + int(nb.max(initial=0)), pad_to or 0)
+    out = {name: np.zeros((b, width), dtype=np.int64) for name in ("ids", "script", "charpos")}
+    out["ngrams"] = np.zeros((b, width, len(NGRAM_ORDERS)), dtype=np.int64)
+    out["ids"][:, 0], out["script"][:, 0], out["charpos"][:, 0] = CLS, SCRIPT_COMMON, 1
+    out["ids"][row, col] = raw + OFFSET
+    out["script"][row, col] = np.repeat(script, s)
+    out["charpos"][row, col] = charpos
+    out["ngrams"][row, col] = np.repeat(grams, s, axis=0)
+    out = {name: torch.from_numpy(v) for name, v in out.items()}
+    out["mask"] = out["ids"] != PAD
+    return out
+
+
+@dataclass
+class Encoded:
+    """One passage as four aligned arrays, one entry per byte, CLS first."""
+
+    ids: np.ndarray  # [m]
+    script: np.ndarray  # [m]
+    charpos: np.ndarray  # [m]
+    ngrams: np.ndarray  # [m, len(NGRAM_ORDERS)]
+
+
+def encode(text: str, max_len: int = 256, ngram_buckets: int = 32768, rng: random.Random | None = None) -> Encoded:
+    """One passage, unpadded: encode_batch of one."""
+    x = encode_batch([text], max_len, ngram_buckets, rng)
+    m = int(x["mask"][0].sum())
+    return Encoded(*(x[name][0, :m].numpy() for name in ("ids", "script", "charpos", "ngrams")))
+
+
+def collate(items: list[Encoded]) -> dict[str, torch.Tensor]:
+    n, g = max(len(e.ids) for e in items), len(NGRAM_ORDERS)
+    ids = np.zeros((len(items), n), dtype=np.int64)
+    script = np.zeros_like(ids)
+    charpos = np.zeros_like(ids)
+    ngrams = np.zeros((len(items), n, g), dtype=np.int64)
+    for i, e in enumerate(items):
+        m = len(e.ids)
+        ids[i, :m], script[i, :m], charpos[i, :m], ngrams[i, :m] = e.ids, e.script, e.charpos, e.ngrams
+    out = {k: torch.from_numpy(v) for k, v in (("ids", ids), ("script", script), ("charpos", charpos), ("ngrams", ngrams))}
+    out["mask"] = out["ids"] != PAD
+    return out
+
+
+# --- the universal label encoding --------------------------------------------------------
+
+
+@dataclass
+class Labels:
+    labels: list[str]
+    aliases: dict[str, str] = field(default_factory=dict)
+    dropped: list[str] = field(default_factory=list)
+
+    def __post_init__(self) -> None:
+        self.index = {lab: i for i, lab in enumerate(self.labels)}
+
+    def __len__(self) -> int:
+        return len(self.labels)
+
+    def canonical(self, label: str) -> str | None:
+        if label in self.dropped:
+            return None
+        return self.aliases.get(label, label)
+
+    def extend(self, more) -> Labels:
+        """The same ids, with new labels appended after them."""
+        new = sorted({lab for lab in more if lab not in self.index})
+        return Labels(self.labels + new, self.aliases, self.dropped)
+
+    @classmethod
+    def load(cls, path: Path) -> Labels:
+        d = json.loads(Path(path).read_text(encoding="utf-8"))
+        return cls(d["labels"], d.get("aliases", {}), d.get("dropped", []))
+
+    def save(self, path: Path, trained: set[str] | None = None) -> None:
+        d = {
+            "scheme": "ISO 639-3 language + '_' + ISO 15924 script, e.g. eng_Latn, amh_Ethi",
+            "text_encoding": "Unicode NFKC, whitespace collapsed, then UTF-8 bytes",
+            "labels": self.labels,
+            "aliases": self.aliases,
+            "dropped": self.dropped,
+        }
+        if trained is not None:
+            d["trained"] = sorted(trained)
+        Path(path).write_text(json.dumps(d, indent=1, ensure_ascii=False), encoding="utf-8")
+
+
+# --- the model ---------------------------------------------------------------------------
+
+
+@dataclass
+class MillIDConfig:
+    num_labels: int = 0
+    max_len: int = 256
+    d_model: int = 768
+    n_layers: int = 12
+    n_heads: int = 12
+    dropout: float = 0.1
+    conv_kernel: int = 5
+    downsample: int = 2
+    ngram_buckets: int = 32768
+    script_buckets: int = SCRIPT_BUCKETS
+    rope_theta: float = 10_000.0
+    vocab_size: int = VOCAB_SIZE
+    modalities: tuple[str, ...] = ("text",)
+    model_type: str = "millid-byte-encoder"
+
+    @property
+    def d_ff(self) -> int:
+        """SwiGLU width: 8/3 d, rounded to 64, the same parameters as a 4d GELU layer."""
+        return max(64, round(8 * self.d_model / 3 / 64) * 64)
+
+
+PRESETS = {
+    "tiny": dict(d_model=192, n_layers=3, n_heads=3, downsample=1, ngram_buckets=4096),
+    "small": dict(d_model=384, n_layers=6, n_heads=6, downsample=2, ngram_buckets=16384),
+    "medium": dict(d_model=768, n_layers=12, n_heads=12, downsample=2, ngram_buckets=32768),
+    "large": dict(d_model=1024, n_layers=12, n_heads=16, downsample=2, ngram_buckets=32768),
+}
+
+
+def rope_tables(n: int, head_dim: int, theta: float) -> tuple[torch.Tensor, torch.Tensor]:
+    inv = 1.0 / (theta ** (torch.arange(0, head_dim, 2).float() / head_dim))
+    angles = torch.outer(torch.arange(n).float(), inv)
+    return angles.cos(), angles.sin()
+
+
+def apply_rope(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
+    """Rotate each pair of features by its position's angle (x: batch, heads, length, head_dim)."""
+    c = cos[: x.size(2)].to(x.dtype)[None, None]
+    s = sin[: x.size(2)].to(x.dtype)[None, None]
+    x1, x2 = x[..., 0::2], x[..., 1::2]
+    return torch.stack((x1 * c - x2 * s, x1 * s + x2 * c), dim=-1).flatten(-2)
+
+
+class Attention(nn.Module):
+    def __init__(self, cfg: MillIDConfig):
+        super().__init__()
+        self.h = cfg.n_heads
+        self.hd = cfg.d_model // cfg.n_heads
+        self.qkv = nn.Linear(cfg.d_model, 3 * cfg.d_model, bias=False)
+        self.out = nn.Linear(cfg.d_model, cfg.d_model, bias=False)
+        self.p = cfg.dropout
+
+    def forward(self, x, cos, sin, key_mask):
+        b, n, _ = x.shape
+        q, k, v = self.qkv(x).view(b, n, 3, self.h, self.hd).permute(2, 0, 3, 1, 4)
+        q, k = apply_rope(q, cos, sin), apply_rope(k, cos, sin)
+        y = F.scaled_dot_product_attention(
+            q, k, v, attn_mask=key_mask[:, None, None, :], dropout_p=self.p if self.training else 0.0
+        )
+        return self.out(y.transpose(1, 2).reshape(b, n, -1))
+
+
+class SwiGLU(nn.Module):
+    def __init__(self, cfg: MillIDConfig):
+        super().__init__()
+        self.w1 = nn.Linear(cfg.d_model, cfg.d_ff, bias=False)
+        self.w3 = nn.Linear(cfg.d_model, cfg.d_ff, bias=False)
+        self.w2 = nn.Linear(cfg.d_ff, cfg.d_model, bias=False)
+        self.drop = nn.Dropout(cfg.dropout)
+
+    def forward(self, x):
+        return self.w2(self.drop(F.silu(self.w1(x)) * self.w3(x)))
+
+
+class Block(nn.Module):
+    """Pre-norm: RMSNorm, attention with RoPE, residual; RMSNorm, SwiGLU, residual."""
+
+    def __init__(self, cfg: MillIDConfig):
+        super().__init__()
+        self.norm1 = nn.RMSNorm(cfg.d_model)
+        self.attn = Attention(cfg)
+        self.norm2 = nn.RMSNorm(cfg.d_model)
+        self.ffn = SwiGLU(cfg)
+        self.drop = nn.Dropout(cfg.dropout)
+
+    def forward(self, x, cos, sin, key_mask):
+        x = x + self.drop(self.attn(self.norm1(x), cos, sin, key_mask))
+        return x + self.drop(self.ffn(self.norm2(x)))
+
+
+class MillIDTextEncoder(nn.Module):
+    """A passage's four byte sequences to one d_model vector: the representation a future image
+    branch is fused with."""
+
+    def __init__(self, cfg: MillIDConfig):
+        super().__init__()
+        d = cfg.d_model
+        self.byte_emb = nn.Embedding(cfg.vocab_size, d, padding_idx=PAD)
+        self.script_emb = nn.Embedding(cfg.script_buckets, d, padding_idx=PAD)
+        self.charpos_emb = nn.Embedding(4, d, padding_idx=PAD)
+        self.ngram_emb = nn.Embedding(cfg.ngram_buckets, d, padding_idx=PAD)
+        # Depthwise over each feature, then mixed: n-grams of conv_kernel bytes, per position.
+        self.stem = nn.Sequential(
+            nn.Conv1d(d, d, cfg.conv_kernel, padding=cfg.conv_kernel // 2, groups=d),
+            nn.GELU(),
+            nn.Conv1d(d, d, 1),
+        )
+        # Then every `downsample` bytes become one token: the transformer reads half as many.
+        self.r = cfg.downsample
+        self.down = nn.Conv1d(d, d, self.r, stride=self.r) if self.r > 1 else None
+        self.blocks = nn.ModuleList(Block(cfg) for _ in range(cfg.n_layers))
+        self.norm = nn.RMSNorm(d)
+        cos, sin = rope_tables(cfg.max_len // self.r + 2, d // cfg.n_heads, cfg.rope_theta)
+        self.register_buffer("cos", cos, persistent=False)
+        self.register_buffer("sin", sin, persistent=False)
+
+    def tokens(self, batch: dict[str, torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor]:
+        """Every token's vector after the last layer, and which tokens are real."""
+        mask = batch["mask"]
+        x = (
+            self.byte_emb(batch["ids"])
+            + self.script_emb(batch["script"])
+            + self.charpos_emb(batch["charpos"])
+            + self.ngram_emb(batch["ngrams"]).sum(2)
+        )
+        m = mask.unsqueeze(-1).to(x.dtype)
+        x = (x + self.stem((x * m).transpose(1, 2)).transpose(1, 2)) * m
+        if self.down is not None:
+            pad = (-x.size(1)) % self.r
+            if pad:
+                x, mask = F.pad(x, (0, 0, 0, pad)), F.pad(mask, (0, pad))
+            x = self.down(x.transpose(1, 2)).transpose(1, 2)
+            mask = mask.view(mask.size(0), -1, self.r).any(-1)
+        for block in self.blocks:
+            x = block(x, self.cos, self.sin, mask)
+        return self.norm(x), mask
+
+    def forward(self, batch: dict[str, torch.Tensor]) -> torch.Tensor:
+        x, mask = self.tokens(batch)
+        m = mask.unsqueeze(-1).to(x.dtype)
+        return (x * m).sum(1) / m.sum(1).clamp(min=1.0)
+
+
+class LanguageHead(nn.Module):
+    """A vector to a score per language, shared by every modality.
+
+    Each language's vector is built from its parts: its own, its language's (shared by
+    `srp_Cyrl` and `srp_Latn`) and its script's (shared by every Ethiopic-script language). A
+    language with little data borrows from what it shares, and `language_vectors()` are the
+    learned language embeddings themselves.
+    """
+
+    def __init__(self, cfg: MillIDConfig, labels: list[str]):
+        super().__init__()
+        codes = sorted({lab.split("_", 1)[0] for lab in labels})
+        scripts = sorted({lab.split("_", 1)[1] if "_" in lab else "" for lab in labels})
+        code_id = {c: i for i, c in enumerate(codes)}
+        script_id = {sc: i for i, sc in enumerate(scripts)}
+        self.register_buffer("code_of", torch.tensor([code_id[lab.split("_", 1)[0]] for lab in labels]))
+        self.register_buffer(
+            "script_of", torch.tensor([script_id[lab.split("_", 1)[1] if "_" in lab else ""] for lab in labels])
+        )
+        d = cfg.d_model
+        self.label_emb = nn.Embedding(len(labels), d)
+        self.code_emb = nn.Embedding(len(codes), d)
+        self.script_emb = nn.Embedding(len(scripts), d)
+        self.bias = nn.Parameter(torch.zeros(len(labels)))
+        self.drop = nn.Dropout(cfg.dropout)
+
+    def language_vectors(self) -> torch.Tensor:
+        return self.label_emb.weight + self.code_emb(self.code_of) + self.script_emb(self.script_of)
+
+    def forward(self, z: torch.Tensor) -> torch.Tensor:
+        return F.linear(self.drop(z), self.language_vectors(), self.bias)
+
+
+class MillIDEncoder(nn.Module):
+    def __init__(self, cfg: MillIDConfig, labels: list[str]):
+        super().__init__()
+        self.cfg = cfg
+        self.text = MillIDTextEncoder(cfg)
+        self.head = LanguageHead(cfg, labels)
+        self.apply(self._init)
+        # Each residual branch's last projection starts small, so a deep stack starts close to
+        # the identity (GPT-2's scaling).
+        for block in self.text.blocks:
+            for w in (block.attn.out.weight, block.ffn.w2.weight):
+                nn.init.normal_(w, std=0.02 / math.sqrt(2 * cfg.n_layers))
+
+    @staticmethod
+    def _init(m: nn.Module) -> None:
+        if isinstance(m, nn.Linear):
+            nn.init.normal_(m.weight, std=0.02)
+            if m.bias is not None:
+                nn.init.zeros_(m.bias)
+        elif isinstance(m, nn.Embedding):
+            nn.init.normal_(m.weight, std=0.02)
+            if m.padding_idx is not None:
+                with torch.no_grad():
+                    m.weight[m.padding_idx].zero_()
+
+    def embed(self, batch: dict[str, torch.Tensor]) -> torch.Tensor:
+        """The passage as one vector, before the head: for similarity, clustering, fusion."""
+        return self.text(batch)
+
+    def forward(self, batch: dict[str, torch.Tensor]) -> torch.Tensor:
+        return self.head(self.text(batch))
+
+
+class MillID:
+    """A trained model, ready to answer: ranked (label, probability) per text."""
+
+    def __init__(self, model: MillIDEncoder, labels: Labels, trained: set[str] | None = None, device: str | None = None):
+        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
+        self.model = model.to(self.device).eval()
+        self.labels = labels
+        # Logits of labels the model never saw a passage of are not answers.
+        self.trained = trained or set(labels.labels)
+        keep = torch.tensor([lab in self.trained for lab in labels.labels], device=self.device)
+        self.bias = torch.where(keep, 0.0, float("-inf"))
+
+    @classmethod
+    def from_pretrained(cls, path_or_repo: str, device: str | None = None) -> MillID:
+        from safetensors.torch import load_file
+
+        path = Path(path_or_repo)
+        if not path.exists():
+            from huggingface_hub import snapshot_download
+
+            path = Path(snapshot_download(path_or_repo, allow_patterns=["config.json", "labels.json", "model.safetensors"]))
+        cfg = MillIDConfig(**json.loads((path / "config.json").read_text())["model"])
+        meta = json.loads((path / "labels.json").read_text(encoding="utf-8"))
+        labels = Labels(meta["labels"], meta.get("aliases", {}), meta.get("dropped", []))
+        model = MillIDEncoder(cfg, labels.labels)
+        model.load_state_dict(load_file(str(path / "model.safetensors")))
+        return cls(model, labels, set(meta.get("trained", meta["labels"])), device)
+
+    def batch(self, texts: list[str]) -> dict[str, torch.Tensor]:
+        cfg = self.model.cfg
+        b = encode_batch(texts, cfg.max_len, cfg.ngram_buckets)
+        return {k: v.to(self.device) for k, v in b.items()}
+
+    @torch.no_grad()
+    def predict(self, texts: list[str], k: int = 5, batch_size: int = 512) -> list[list[tuple[str, float]]]:
+        out = []
+        for i in range(0, len(texts), batch_size):
+            with torch.autocast(self.device, dtype=torch.bfloat16, enabled=self.device == "cuda"):
+                logits = self.model(self.batch(texts[i : i + batch_size])).float()
+            probs = F.softmax(logits + self.bias, dim=-1)
+            p, idx = probs.topk(k, dim=-1)
+            for pr, ix in zip(p.tolist(), idx.tolist(), strict=True):
+                out.append([(self.labels.labels[j], v) for j, v in zip(ix, pr, strict=True)])
+        return out
+
+    @torch.no_grad()
+    def embed(self, texts: list[str]) -> torch.Tensor:
+        """Passage vectors, B x d_model."""
+        return self.model.embed(self.batch(texts)).float().cpu()
+
+
+# --- metrics (the same definitions as scripts/metrics.py, tested there) -----------------
+
+
+def per_language(true: list[str], pred: list[str]):
+    import pandas as pd
+
+    t, p = pd.Series(list(true)), pd.Series(list(pred))
+    n = len(t)
+    out = pd.DataFrame({"support": t.value_counts()})
+    out["tp"] = t[t.values == p.values].value_counts().reindex(out.index, fill_value=0)
+    out["fp"] = p.value_counts().reindex(out.index, fill_value=0) - out["tp"]
+    out["fn"] = out["support"] - out["tp"]
+    out["tn"] = n - out["tp"] - out["fp"] - out["fn"]
+    with np.errstate(divide="ignore", invalid="ignore"):
+        out["precision"] = np.where(out["tp"] + out["fp"] > 0, out["tp"] / (out["tp"] + out["fp"]), 0.0)
+        out["recall"] = out["tp"] / out["support"]
+        pr = out["precision"] + out["recall"]
+        out["f1"] = np.where(pr > 0, 2 * out["precision"] * out["recall"] / pr, 0.0)
+        out["specificity"] = out["tn"] / (out["tn"] + out["fp"])
+        out["fp_per_million"] = out["fp"] / (n - out["support"]) * 1e6
+    out["sensitivity"] = out["recall"]
+    out.index.name = "label"
+    return out.sort_index()
+
+
+def summarize(table, true: list[str], ranked: list[list[str]]) -> dict:
+    n = max(1, len(true))
+    s = {f"top{k}_accuracy": sum(t in r[:k] for t, r in zip(true, ranked, strict=True)) / n for k in (1, 3, 5)}
+    w = table["support"] / table["support"].sum()
+    s.update(
+        passages=int(table["support"].sum()),
+        languages=int(len(table)),
+        macro_precision=float(table["precision"].mean()),
+        macro_recall=float(table["recall"].mean()),
+        macro_f1=float(table["f1"].mean()),
+        macro_specificity=float(table["specificity"].mean()),
+        weighted_f1=float((table["f1"] * w).sum()),
+        median_f1=float(table["f1"].median()),
+        languages_f1_at_least_0_9=int((table["f1"] >= 0.9).sum()),
+        languages_f1_below_0_5=int((table["f1"] < 0.5).sum()),
+        mean_fp_per_million=float(table["fp_per_million"].mean()),
+    )
+    s["micro_f1"] = s["top1_accuracy"]
+    return s
+
+
+# --- data --------------------------------------------------------------------------------
+
+
+def load_dataset_dir(dataset: str) -> Path:
+    """A local build directory, or the dataset repo downloaded (parquet and labels.json only)."""
+    if Path(dataset).exists():
+        return Path(dataset)
+    from huggingface_hub import snapshot_download
+
+    return Path(snapshot_download(dataset, repo_type="dataset", allow_patterns=["data/*", "udhr/*", "labels.json", "stats.json"]))
+
+
+def read_split(root: Path, name: str):
+    import pandas as pd
+
+    if name == "udhr":
+        return pd.read_parquet(root / "udhr" / "test.parquet")
+    return pd.read_parquet(root / "data" / f"{name}.parquet")
+
+
+class Sampler:
+    """Training passages, drawn so that small languages are seen often enough to learn.
+
+    A language is drawn with probability proportional to n^alpha, n being its passage count:
+    alpha 1 is the data as it is, alpha 0 every language equally; 0.3 in between. Then a
+    passage of it is drawn, and sometimes cut short, because a short snippet is where
+    language identification is hardest and what a document pipeline sees in table cells.
+    """
+
+    def __init__(self, texts: list[str], label_ids: np.ndarray, alpha: float, short_p: float, seed: int):
+        self.texts = texts
+        self.rng = random.Random(seed)
+        self.np_rng = np.random.default_rng(seed)
+        order = np.argsort(label_ids, kind="stable")
+        self.by_label = np.split(order, np.flatnonzero(np.diff(label_ids[order])) + 1)
+        self.label_of = np.array([label_ids[g[0]] for g in self.by_label])
+        sizes = np.array([len(g) for g in self.by_label], dtype=float)
+        p = sizes**alpha
+        self.p = p / p.sum()
+        self.short_p = short_p
+
+    def draw(self, n: int) -> tuple[list[str], np.ndarray]:
+        groups = self.np_rng.choice(len(self.by_label), size=n, p=self.p)
+        texts, labels = [], []
+        for g in groups:
+            idx = self.by_label[g][self.rng.randrange(len(self.by_label[g]))]
+            texts.append(self._maybe_short(self.texts[idx]))
+            labels.append(self.label_of[g])
+        return texts, np.array(labels)
+
+    def _maybe_short(self, text: str) -> str:
+        if self.rng.random() >= self.short_p or len(text) <= 40:
+            return text
+        n = self.rng.randint(20, 80)
+        start = self.rng.randrange(0, max(1, len(text) - n))
+        if " " in text:  # start and end on word boundaries where the script has them
+            left = text.rfind(" ", 0, start) + 1
+            right = text.find(" ", left + n)
+            return text[left : right if right > 0 else len(text)]
+        return text[start : start + n]
+
+
+# --- tracking ----------------------------------------------------------------------------
+
+
+class Tracker:
+    """Weights & Biases when there is a key or a project, and nothing otherwise, so the same run
+    works locally without an account. Inside a sweep agent, the agent's run is used."""
+
+    def __init__(self, args, config: dict, enabled: bool = True):
+        self.run = None
+        self.wandb = None
+        if not enabled or args.no_wandb or not (args.wandb_project or os.environ.get("WANDB_API_KEY")):
+            return
+        import wandb
+
+        self.wandb = wandb
+        clean = asdict(args)
+        if wandb.run is not None:
+            self.run = wandb.run
+            self.run.config.update({**config}, allow_val_change=True)
+            self.owned = False
+        else:
+            self.run = wandb.init(
+                project=args.wandb_project or "millid",
+                entity=args.wandb_entity,
+                name=args.wandb_run_name,
+                tags=args.wandb_tags or None,
+                config={**clean, **config},
+                job_type="train",
+            )
+            self.owned = True
+        self.run.define_metric("train/*", step_metric="step")
+        self.run.define_metric("val/*", step_metric="step")
+
+    def log(self, data: dict, step: int) -> None:
+        if self.run:
+            self.run.log({"step": step, **data})
+
+    def summary(self, data: dict) -> None:
+        if self.run:
+            self.run.summary.update(data)
+
+    def table(self, key: str, frame) -> None:
+        if self.run:
+            self.run.log({key: self.wandb.Table(dataframe=frame.reset_index())})
+
+    def finish(self) -> None:
+        if self.run and self.owned:
+            self.run.finish()
+
+
+# --- training ----------------------------------------------------------------------------
+
+
+class TrainStream(torch.utils.data.IterableDataset):
+    """Endless training batches, encoded in DataLoader workers so the GPU never waits for
+    Python. Each worker draws with its own seed."""
+
+    def __init__(self, texts, label_ids, args, cfg: MillIDConfig, batch_size: int, rank: int = 0, pad_to: int | None = None):
+        self.texts, self.label_ids = texts, label_ids
+        self.args, self.cfg = args, cfg
+        self.batch_size, self.rank, self.pad_to = batch_size, rank, pad_to
+
+    def __iter__(self):
+        info = torch.utils.data.get_worker_info()
+        # every worker of every GPU's process draws its own passages
+        seed = self.args.seed * 1000 + self.rank * 100 + (info.id if info else 0)
+        sampler = Sampler(self.texts, self.label_ids, self.args.alpha, self.args.short_p, seed)
+        rng = random.Random(seed)
+        while True:
+            texts, y = sampler.draw(self.batch_size)
+            batch = encode_batch(texts, self.cfg.max_len, self.cfg.ngram_buckets, rng, pad_to=self.pad_to)
+            batch["y"] = torch.from_numpy(y)
+            yield batch
+
+
+
+
+def stratified(frame, per_label: int, seed: int = 7):
+    return frame.sample(frac=1, random_state=seed).groupby("label").head(per_label)
+
+
+def evaluate_frame(model: MillID, frame, batch_size: int = 1024) -> tuple[dict, object, list]:
+    ranked = model.predict(frame["text"].tolist(), k=5, batch_size=batch_size)
+    pred = [r[0][0] for r in ranked]
+    table = per_language(frame["label"].tolist(), pred)
+    return summarize(table, frame["label"].tolist(), [[lab for lab, _ in r] for r in ranked]), table, ranked
+
+
+def _free_port() -> int:
+    import socket
+
+    with socket.socket() as s:
+        s.bind(("127.0.0.1", 0))
+        return s.getsockname()[1]
+
+
+def train(args: TrainArgs) -> None:
+    """Train on every GPU the machine has (or args.gpus of them): one process per GPU, the batch
+    split between them and the gradients averaged (DistributedDataParallel). Without CUDA,
+    args.gpus > 1 starts that many CPU processes, which is how the distributed path is tested."""
+    if torch.cuda.is_available():
+        n = torch.cuda.device_count()
+        world = n if args.gpus <= 0 else min(args.gpus, n)
+    else:
+        world = max(1, args.gpus)
+    if world == 1:
+        _train(0, 1, 0, args)
+        return
+    if args.batch_size % world:
+        sys.exit(f"batch size {args.batch_size} does not split over {world} processes")
+    load_dataset_dir(args.dataset)  # download once, before the processes start
+    import torch.multiprocessing as mp
+
+    mp.spawn(_train, args=(world, _free_port(), args), nprocs=world, join=True)
+
+
+def _train(rank: int, world: int, port: int, args: TrainArgs) -> None:
+    import pandas as pd
+    from safetensors.torch import save_file
+
+    cuda = torch.cuda.is_available()
+    distributed = world > 1
+    if distributed:
+        import torch.distributed as dist
+
+        os.environ["MASTER_ADDR"], os.environ["MASTER_PORT"] = "127.0.0.1", str(port)
+        if cuda:
+            torch.cuda.set_device(rank)
+        dist.init_process_group("nccl" if cuda else "gloo", rank=rank, world_size=world)
+    main = rank == 0
+    say = print if main else (lambda *a, **k: None)
+    torch.manual_seed(args.seed)
+    if args.threads:
+        torch.set_num_threads(args.threads)
+    device = f"cuda:{rank}" if cuda else "cpu"
+    device_type = "cuda" if cuda else "cpu"
+    root = load_dataset_dir(args.dataset)
+    labels = Labels.load(root / "labels.json")
+    tr = read_split(root, "train")
+    va = read_split(root, "validation")
+    if args.commercial_only:
+        tr = tr[~tr["license"].str.contains("NC", na=False)]
+        va = va[~va["license"].str.contains("NC", na=False)]
+    if args.extra:
+        extra = pd.concat([pd.read_parquet(p, columns=["text", "label"]) for p in args.extra], ignore_index=True)
+        extra["label"] = extra["label"].map(labels.canonical)
+        extra = extra.dropna(subset=["label"])
+        evals = set(va["text"].map(normalize)) | set(read_split(root, "test")["text"].map(normalize)) | set(read_split(root, "udhr")["text"].map(normalize))
+        extra = extra[~extra["text"].map(normalize).isin(evals)]
+        labels = labels.extend(extra["label"].unique())
+        tr = pd.concat([tr[["text", "label"]], extra], ignore_index=True)
+        say(f"extra training passages: {len(extra):,}", flush=True)
+    trained = set(tr["label"])
+    label_ids = tr["label"].map(labels.index).to_numpy()
+    say(f"{device_type} x{world}; train {len(tr):,} passages, {len(trained)} labels; validation {len(va):,}; vocabulary {len(labels)}", flush=True)
+
+    cfg = MillIDConfig(num_labels=len(labels), max_len=args.max_len, dropout=args.dropout, **PRESETS[args.preset])
+    net = MillIDEncoder(cfg, labels.labels).to(device)
+    model = net
+    if distributed:
+        from torch.nn.parallel import DistributedDataParallel
+
+        model = DistributedDataParallel(net, device_ids=[rank] if cuda else None)
+    step_net = torch.compile(model) if args.compile and cuda else model
+    params = sum(p.numel() for p in net.parameters())
+    say(f"MillID {args.preset}: {params / 1e6:.1f}M parameters, {cfg}", flush=True)
+    tracker = Tracker(
+        args,
+        {"model": asdict(cfg), "parameters": params, "train_passages": int(len(tr)), "gpus": world,
+         "trained_labels": len(trained), "vocabulary": len(labels), "validation_passages": int(len(va))},
+        enabled=main,
+    )  # fmt: skip
+
+    decay = [p for n, p in net.named_parameters() if p.dim() >= 2 and "emb" not in n]
+    no_decay = [p for n, p in net.named_parameters() if p.dim() < 2 or "emb" in n]
+    opt = torch.optim.AdamW(
+        [{"params": decay, "weight_decay": args.weight_decay}, {"params": no_decay, "weight_decay": 0.0}],
+        lr=args.lr, betas=(0.9, 0.98), eps=1e-8, fused=cuda,
+    )  # fmt: skip
+
+    def lr_at(step: int) -> float:
+        if step < args.warmup:
+            return args.lr * step / args.warmup
+        progress = (step - args.warmup) / max(1, args.steps - args.warmup)
+        return args.lr * (0.05 + 0.95 * 0.5 * (1 + math.cos(math.pi * progress)))
+
+    workers = args.workers
+    if workers < 0:
+        workers = 0 if os.name == "nt" else max(0, min(12, ((os.cpu_count() or 2) - 2) // world))
+    script_table()  # once, before the workers fork, so they share it
+    say(f"{workers} data workers per process, {args.batch_size // world} passages per process per step", flush=True)
+    pad_to = cfg.max_len if args.compile and cuda else None  # fixed shapes: compiled once
+    loader = torch.utils.data.DataLoader(
+        TrainStream(tr["text"].tolist(), label_ids, args, cfg, args.batch_size // world, rank, pad_to),
+        batch_size=None,
+        num_workers=workers,
+        pin_memory=cuda,
+        prefetch_factor=4 if workers else None,
+        persistent_workers=workers > 0,
+    )
+    stream = iter(loader)
+    va_small = stratified(va, args.eval_per_label)
+    out = Path(args.output_dir)
+    out.mkdir(parents=True, exist_ok=True)
+    log = open(out / "training_log.jsonl", "w", encoding="utf-8") if main else None
+    best, best_step, started, seen = -1.0, 0, time.monotonic(), 0
+    use_bf16 = cuda and torch.cuda.is_bf16_supported()
+
+    for step in range(1, args.steps + 1):
+        net.train()
+        batch = next(stream)
+        y = batch.pop("y").to(device, non_blocking=True)
+        batch = {k: v.to(device, non_blocking=True) for k, v in batch.items()}
+        for g in opt.param_groups:
+            g["lr"] = lr_at(step)
+        with torch.autocast(device_type, dtype=torch.bfloat16, enabled=use_bf16):
+            logits = step_net(batch)
+        loss = F.cross_entropy(logits.float(), y, label_smoothing=args.label_smoothing)
+        opt.zero_grad(set_to_none=True)
+        loss.backward()
+        grad_norm = torch.nn.utils.clip_grad_norm_(net.parameters(), 1.0)
+        opt.step()
+        seen += args.batch_size
+
+        if step % args.log_every == 0 and main:
+            rate = seen / (time.monotonic() - started)
+            print(f"step {step}/{args.steps} loss {loss.item():.4f} lr {lr_at(step):.2e} {rate:,.0f} passages/s", flush=True)
+            tracker.log(
+                {"train/loss": loss.item(), "train/lr": lr_at(step), "train/grad_norm": float(grad_norm),
+                 "train/passages_per_s": rate, "train/epochs": seen / len(tr)},
+                step,
+            )  # fmt: skip
+        if step % args.eval_every == 0 or step == args.steps:
+            # The first process evaluates and saves; the others wait for its verdict.
+            stop = False
+            if main:
+                s, _, _ = evaluate_frame(MillID(net, labels, trained, device), va_small)
+                entry = {"step": step, "loss": round(loss.item(), 5), **{k: round(s[k], 5) for k in ("macro_f1", "top1_accuracy", "top3_accuracy")}}
+                log.write(json.dumps(entry) + "\n")
+                log.flush()
+                print(f"  validation @ {step}: macro F1 {s['macro_f1']:.4f} top-1 {s['top1_accuracy']:.4f}", flush=True)
+                if s["macro_f1"] > best:
+                    best, best_step = s["macro_f1"], step
+                    save_file({k: v.detach().cpu().contiguous() for k, v in net.state_dict().items()}, str(out / "model.safetensors"))
+                elif step - best_step >= args.patience:
+                    print(f"no gain on validation for {args.patience} steps: stopping at {step}", flush=True)
+                    stop = True
+                tracker.log(
+                    {f"val/{k}": s[k] for k in ("macro_f1", "top1_accuracy", "top3_accuracy", "macro_precision", "macro_recall")}
+                    | {"val/best_macro_f1": best},
+                    step,
+                )
+            if distributed:
+                flag = torch.tensor([int(stop)], device=device)
+                dist.broadcast(flag, 0)
+                stop = bool(flag.item())
+            if stop:
+                break
+    if distributed:
+        dist.destroy_process_group()
+    if not main:
+        return
+    log.close()
+    tracker.summary({"best_step": best_step, "val/best_macro_f1": best})
+
+    labels.save(out / "labels.json", trained)
+    config = {
+        "model": asdict(cfg),
+        "training": asdict(args),
+        "best_step": best_step,
+        "parameters": params,
+        "gpus": world,
+        "train_passages": int(len(tr)),
+        "trained_labels": len(trained),
+        "seconds": round(time.monotonic() - started),
+    }
+    (out / "config.json").write_text(json.dumps(config, indent=2, default=str), encoding="utf-8")
+    shutil.copy(__file__, out / "millid.py")
+    doc = Path(__file__).with_name("ARCHITECTURE.md")
+    if doc.exists():  # beside the script locally; a Job ships only the script
+        shutil.copy(doc, out / "ARCHITECTURE.md")
+    print(f"best validation macro F1 {best:.4f} at step {best_step}", flush=True)
+
+    if args.final_eval == "none":
+        tracker.finish()
+        return
+    model = MillID.from_pretrained(str(out))
+    sets = ("validation",) if args.final_eval == "validation" else ("validation", "test", "udhr")
+    names = json.loads((root / "labels.json").read_text(encoding="utf-8")).get("names", {})
+    compare = [ext for ext, on in (("glotlid", args.compare_glotlid), ("fasttext", args.compare_fasttext)) if on]
+    results, boards = evaluate_all(model, root, out / "reports", compare, sets, names)
+    tracker.summary(
+        {f"eval/{set_name}/{m}/{k}": v for set_name, by in results.items() for m, sm in by.items()
+         for k, v in sm.items() if isinstance(v, (int, float))}
+    )  # fmt: skip
+    for set_name, by in results.items():
+        rows = [{"model": m, **{k: v for k, v in sm.items() if isinstance(v, (int, float))}} for m, sm in by.items()]
+        tracker.table(f"leaderboard/{set_name}/models", pd.DataFrame(rows).set_index("model"))
+    for set_name, board in boards.items():
+        tracker.table(f"leaderboard/{set_name}/languages", board.round(4))
+        if "winner" in board:
+            tracker.summary({f"eval/{set_name}/languages_{w.lower()}": int((board["winner"] == w).sum()) for w in ("MillID", "GlotLID", "tie")})
+    write_card(out, config, results, args, boards)
+    tracker.finish()
+    if args.push_to:
+        from huggingface_hub import HfApi
+
+        api = HfApi()
+        api.create_repo(args.push_to, private=not args.public, exist_ok=True)
+        api.upload_folder(repo_id=args.push_to, folder_path=str(out), commit_message=f"MillID {args.preset}, step {best_step}")
+        print(f"pushed https://huggingface.co/{args.push_to}", flush=True)
+
+
+# --- evaluation against GlotLID and fastText ---------------------------------------------
+#
+# Both are fastText models: GlotLID (cis-lmu, 2,000+ labels) and Meta's NLLB language identifier
+# (218 labels), published as facebook/fasttext-language-identification. Both write labels as
+# ISO 639-3 + script; their answers are mapped onto MillID's spelling of the same language
+# before scoring, so that a right answer spelled differently is not counted wrong.
+
+#: name: (Hub repo, file, environment variable for a copy on disk, display name, GB in memory)
+EXTERNAL = {
+    "glotlid": ("cis-lmu/glotlid", "model.bin", "GLOTLID_PATH", "GlotLID", 1.8),
+    "fasttext": ("facebook/fasttext-language-identification", "model.bin", "FASTTEXT_LID_PATH", "fastText (Meta, NLLB)", 1.3),
+}
+#: Spellings MillID's aliases do not cover: a macrolanguage for its one member in MillID
+#: (Estonian, Akan as Twi, Guarani as Paraguayan Guarani, Kikongo as Koongo).
+EXTERNAL_ALIASES = {"est_Latn": "ekk_Latn", "aka_Latn": "twi_Latn", "grn_Latn": "gug_Latn", "kon_Latn": "kng_Latn"}
+
+_lid = None
+_lid_aliases: dict[str, str] = {}
+
+
+def to_millid_label(label: str, aliases: dict[str, str]) -> str:
+    """An external model's label in MillID's spelling: Simplified and Traditional Han are both
+    Hani, then MillID's aliases, then EXTERNAL_ALIASES."""
+    code, _, script = label.partition("_")
+    if script in ("Hans", "Hant"):
+        label = f"{code}_Hani"
+    label = aliases.get(label, label)
+    return EXTERNAL_ALIASES.get(label, label)
+
+
+def _lid_init(path: str, aliases: dict[str, str]) -> None:
+    global _lid, _lid_aliases
+    import fasttext
+
+    _lid, _lid_aliases = fasttext.load_model(path), aliases
+
+
+def _lid_chunk(texts: list[str]) -> list[list[tuple[str, float]]]:
+    labels, probs = _lid.f.multilinePredict([normalize(t) for t in texts], 50, 0.0, "strict")
+    out = []
+    for ls, ps in zip(labels, probs, strict=True):
+        seen, pairs = set(), []
+        for lab, p in zip(ls, ps, strict=True):
+            lab = to_millid_label(lab.removeprefix("__label__"), _lid_aliases)
+            if lab not in seen:  # two of its labels can be one of MillID's: keep the better
+                seen.add(lab)
+                pairs.append((lab, float(p)))
+        out.append(pairs)
+    return out
+
+
+def _lid_labels() -> list[str]:
+    return sorted({to_millid_label(lab.removeprefix("__label__"), _lid_aliases) for lab in _lid.get_labels()})
+
+
+def _cpu_workers(gb_each: float) -> int:
+    """Processes the machine has cores and memory for, at most 8."""
+    cpus = os.cpu_count() or 2
+    try:
+        ram = os.sysconf("SC_PAGE_SIZE") * os.sysconf("SC_PHYS_PAGES") / 2**30
+    except (ValueError, OSError, AttributeError):
+        ram = 16.0
+    return max(1, min(8, cpus - 2, int(ram // (gb_each * 2))))
+
+
+class FastTextLID:
+    """A fastText language identifier's 50 best answers per passage, in MillID's label
+    spelling, computed in parallel processes (fastText holds the GIL, and each process loads the
+    model once). Both views come from the same answers: as it is, and restricted to MillID's
+    labels."""
+
+    def __init__(self, name: str = "glotlid", aliases: dict[str, str] | None = None, workers: int | None = None):
+        from huggingface_hub import hf_hub_download
+
+        repo, filename, env, self.display, gb = EXTERNAL[name]
+        self.name, self.aliases = name, aliases or {}
+        # A copy already on disk, rather than a gigabyte or two from the Hub again.
+        self.path = os.environ.get(env) or hf_hub_download(repo, filename)
+        self.workers = workers or _cpu_workers(gb_each=gb)
+        self._labels: set[str] | None = None
+
+    def labels(self) -> set[str]:
+        """Every label it can answer, in MillID's spelling."""
+        if self._labels is None:
+            _lid_init(self.path, self.aliases)
+            self._labels = set(_lid_labels())
+        return self._labels
+
+    def rank(self, texts: list[str], chunk: int = 10_000) -> list[list[tuple[str, float]]]:
+        parts = [texts[i : i + chunk] for i in range(0, len(texts), chunk)]
+        if self.workers <= 1 or len(parts) == 1:
+            _lid_init(self.path, self.aliases)
+            return [r for part in parts for r in _lid_chunk(part)]
+        from concurrent.futures import ProcessPoolExecutor
+
+        with ProcessPoolExecutor(self.workers, initializer=_lid_init, initargs=(self.path, self.aliases)) as pool:
+            return [r for result in pool.map(_lid_chunk, parts) for r in result]
+
+    @staticmethod
+    def view(ranked, allowed: set[str] | None = None, k: int = 5) -> list[list[tuple[str, float]]]:
+        out = []
+        for pairs in ranked:
+            if allowed is not None:
+                pairs = [(lab, p) for lab, p in pairs if lab in allowed]
+            out.append(pairs[:k] or [("unk", 0.0)])
+        return out
+
+
+GlotLID = FastTextLID  # the name earlier code and reports use
+
+
+def leaderboard(tables: dict, names: dict[str, str]):
+    """One row per language: MillID against each external model, as it is and restricted to
+    MillID's labels. `delta` is MillID's F1 minus restricted GlotLID's, `delta_fasttext` minus
+    restricted fastText's: the like-for-like comparisons."""
+    import pandas as pd
+
+    m = tables["millid"]
+    out = pd.DataFrame({"language": [names.get(lab, lab) for lab in m.index], "support": m["support"]}, index=m.index)
+    for model, t in tables.items():
+        for col in ("f1", "precision", "recall"):
+            out[f"{model}_{col}"] = t[col].reindex(m.index)
+    for ext, suffix, winner in (("glotlid", "", "GlotLID"), ("fasttext", "_fasttext", "fastText")):
+        if f"{ext}@millid" in tables:
+            d = out["millid_f1"] - out[f"{ext}@millid_f1"]
+            out[f"delta{suffix}"] = d
+            out[f"winner{suffix}"] = np.where(d > 0.01, "MillID", np.where(d < -0.01, winner, "tie"))
+    out.index.name = "label"
+    return out.sort_values("support", ascending=False)
+
+
+def _score(name: str, frame, rankings: dict, out: Path, names: dict) -> tuple[dict, object]:
+    d = out / name
+    d.mkdir(parents=True, exist_ok=True)
+    results, tables = {}, {}
+    for mname, ranked in rankings.items():
+        pred = [r[0][0] for r in ranked]
+        table = per_language(frame["label"].tolist(), pred)
+        sm = summarize(table, frame["label"].tolist(), [[lab for lab, _ in r] for r in ranked])
+        if "size" in frame:
+            f = frame.assign(pred=pred)
+            sm["top1_by_size"] = {k: float((g["label"] == g["pred"]).mean()) for k, g in f.groupby("size")}
+        table.round(6).to_csv(d / f"{mname}_per_language.csv")
+        (d / f"{mname}_summary.json").write_text(json.dumps(sm, indent=2), encoding="utf-8")
+        results[mname], tables[mname] = sm, table
+        print(f"{name:24} {mname:16} top-1 {sm['top1_accuracy']:.4f} top-3 {sm['top3_accuracy']:.4f} macro F1 {sm['macro_f1']:.4f}", flush=True)
+    board = None
+    if len(tables) > 1:
+        board = leaderboard(tables, names)
+        board.round(4).to_csv(d / "leaderboard.csv")
+    return results, board
+
+
+def evaluate_all(
+    model: MillID, root: Path, out: Path, compare=False, which=("validation", "test", "udhr"), names=None,
+    lids: dict[str, FastTextLID] | None = None, cache: dict | None = None,
+) -> tuple[dict, dict]:  # fmt: skip
+    """MillID on validation, test and UDHR, and the external models in `compare` (True means
+    GlotLID alone) on test and UDHR, each as it is and restricted to MillID's labels. Writes
+    per-language tables, summaries and a leaderboard per set under out/<set>/. Returns the
+    summaries and the leaderboards.
+
+    fastText knows 218 labels, so on sets of 1,600 it is mostly scored on languages it cannot
+    answer. `<set>-common` scores every model on the passages whose language all of them know:
+    the head-to-head on equal terms.
+
+    An external model's ranking of a set does not depend on the MillID it is compared with:
+    pass the same `lids` and `cache` to evaluate several models and each set is read once."""
+    names = names or {}
+    compare = ("glotlid",) if compare is True else tuple(compare or ())
+    lids = {} if lids is None else lids
+    cache = {} if cache is None else cache
+    for ext in compare:
+        if ext not in lids:
+            lids[ext] = FastTextLID(ext, model.labels.aliases)
+    frames = {}
+    if "validation" in which:
+        frames["validation"] = read_split(root, "validation")
+    if "test" in which:
+        frames["test"] = read_split(root, "test")
+    if "udhr" in which:
+        u = read_split(root, "udhr").reset_index(drop=True)
+        frames["udhr"] = u[u["label"].isin(model.trained)]
+        frames["udhr-all"] = u
+    common = set(model.trained)
+    if len(compare) > 1:
+        for ext in compare:
+            common &= lids[ext].labels()
+
+    results, boards = {}, {}
+    for name, frame in frames.items():
+        texts = frame["text"].tolist()
+        rankings = {"millid": model.predict(texts, k=5, batch_size=1024)}
+        if name != "validation":
+            for ext in compare:
+                if name in ("udhr", "udhr-all"):
+                    if (ext, "udhr") not in cache:
+                        cache[ext, "udhr"] = lids[ext].rank(frames["udhr-all"]["text"].tolist())
+                    full = cache[ext, "udhr"]
+                    ranked = [full[i] for i in frame.index]
+                else:
+                    if (ext, name) not in cache:
+                        cache[ext, name] = lids[ext].rank(texts)
+                    ranked = cache[ext, name]
+                rankings[ext] = FastTextLID.view(ranked)
+                rankings[f"{ext}@millid"] = FastTextLID.view(ranked, model.trained)
+        results[name], board = _score(name, frame, rankings, out, names)
+        if board is not None:
+            boards[name] = board
+        if len(compare) > 1 and name in ("test", "udhr"):
+            keep = frame["label"].isin(common).to_numpy()
+            sub = {m: [r for r, k in zip(ranked, keep, strict=True) if k] for m, ranked in rankings.items()}
+            results[f"{name}-common"], board = _score(f"{name}-common", frame[keep], sub, out, names)
+            boards[f"{name}-common"] = board
+    (out / "summary.json").write_text(json.dumps(results, indent=2), encoding="utf-8")
+    return results, boards
+
+
+def _board_md(board, set_name: str) -> str:
+    ext = [(name, col, w) for name, col, w in (("GlotLID", "glotlid", "winner"), ("fastText", "fasttext", "winner_fasttext")) if w in board]
+    if not ext:
+        return ""
+    counts = "; ".join(
+        f"against {name} restricted to MillID's labels, MillID is better on **{(board[w] == 'MillID').sum()}** languages, "
+        f"worse on **{(board[w] == name).sum()}** and within 0.01 on {(board[w] == 'tie').sum()}"
+        for name, _, w in ext
+    )
+    key = "delta" if "delta" in board else "delta_fasttext"
+
+    def table(frame) -> str:
+        head = "| Label | Language | Passages | MillID F1 | " + " | ".join(f"{n} F1 | {n} F1, restricted" for n, _, _ in ext) + " |"
+        lines = [head, "|---|---|---|---|" + "---|---|" * len(ext)]
+        for lab, r in frame.iterrows():
+            cells = " | ".join(f"{r[f'{c}_f1']:.3f} | {r[f'{c}@millid_f1']:.3f}" for _, c, _ in ext)
+            lines.append(f"| `{lab}` | {r['language']} | {int(r['support'])} | {r['millid_f1']:.3f} | {cells} |")
+        return "\n".join(lines)
+
+    return f"""### Leaderboard: {set_name}
+
+MillID's F1 against the others', language by language: {counts}. The full table, with precision and recall, is
+`reports/{set_name}/leaderboard.csv`.
+
+The 20 languages with the most passages:
+
+{table(board.head(20))}
+
+Where MillID gains most over {ext[0][0]}:
+
+{table(board.sort_values(key, ascending=False).head(15))}
+
+Where MillID loses most to {ext[0][0]}:
+
+{table(board.sort_values(key).head(15))}
+"""
+
+
+def write_card(out: Path, config: dict, results: dict, args, boards: dict | None = None) -> None:
+    boards = boards or {}
+    rows = ["| Set | Model | Top-1 | Top-3 | Top-5 | Macro P | Macro R | Macro F1 | Median F1 | Languages |", "|---|---|---|---|---|---|---|---|---|---|"]
+    for set_name, by_model in results.items():
+        for mname, sm in by_model.items():
+            rows.append(
+                f"| {set_name} | {mname} | {sm['top1_accuracy']:.4f} | {sm['top3_accuracy']:.4f} | {sm['top5_accuracy']:.4f} | "
+                f"{sm['macro_precision']:.4f} | {sm['macro_recall']:.4f} | {sm['macro_f1']:.4f} | {sm['median_f1']:.4f} | {sm['languages']} |"
+            )
+    size_rows = ["| Set | Model | short | sentence | paragraph |", "|---|---|---|---|---|"]
+    for set_name, by_model in results.items():
+        for mname, sm in by_model.items():
+            b = sm.get("top1_by_size") or {}
+            if b:
+                size_rows.append(f"| {set_name} | {mname} | " + " | ".join(f"{b.get(k, float('nan')):.4f}" for k in ("short", "sentence", "paragraph")) + " |")
+    m = config["model"]
+    repo = args.push_to or "baobabtech/millid"
+    card = f"""---
+library_name: pytorch
+pipeline_tag: text-classification
+tags:
+- language-identification
+datasets:
+- {args.dataset}
+---
+
+# MillID {args.preset}: MultiLingual Language IDentifier
+
+A byte-level, encoder-only transformer that names the language of a passage: **{config['trained_labels']} language-script
+labels** (ISO 639-3 + script, e.g. `eng_Latn`, `amh_Ethi`). {config['parameters'] / 1e6:.1f}M parameters, trained from
+scratch on [{args.dataset}](https://huggingface.co/datasets/{args.dataset}).
+
+```python
+from millid import MillID   # millid.py, in this repository
+
+model = MillID.from_pretrained("{repo}")
+model.predict(["Lave men ou ak savon anvan ou manje."], k=3)
+```
+
+## Architecture
+
+UTF-8 bytes (after Unicode NFKC), each embedded as byte + Unicode script + byte-in-character + hashed character
+2-, 3- and 4-grams ({m['ngram_buckets']:,} buckets) -> depthwise convolution stem (kernel {m['conv_kernel']}) -> strided
+convolution, {m['downsample']} bytes per token -> {m['n_layers']} pre-norm blocks (RMSNorm, attention with RoPE,
+{m['n_heads']} heads, SwiGLU; d={m['d_model']}) -> masked mean pooling -> language head, each language's vector the sum of
+its own, its language's and its script's. Up to {m['max_len']} bytes per passage. See `ARCHITECTURE.md`.
+
+## Evaluation against GlotLID and fastText
+
+`validation` chose the checkpoint; `test` is split by document from training; `udhr` is the Universal Declaration
+of Human Rights, labelled by its translators, over the languages MillID knows, and `udhr-all` over all of them.
+`glotlid` is [GlotLID](https://huggingface.co/cis-lmu/glotlid) as it is; `glotlid@millid` is GlotLID restricted to
+MillID's labels, the like-for-like comparison. GlotLID's own labels came from training on data that includes UDHR
+and labelled FinePDFs and FineWeb-2, so its scores on `udhr` and on those sources are optimistic. `fasttext` is
+Meta's [fastText language identifier](https://huggingface.co/facebook/fasttext-language-identification) (NLLB,
+218 labels), its answers mapped onto MillID's spelling (Simplified and Traditional Han as `Hani`, `pes`/`prs` as
+`fas`, `est` as `ekk`). It cannot answer most of the 1,600 languages here, so `test-common` and `udhr-common`
+score every model on the passages whose language all of them know: the head-to-head on equal terms.
+
+{chr(10).join(rows)}
+
+Top-1 accuracy by passage length:
+
+{chr(10).join(size_rows)}
+
+{chr(10).join(_board_md(boards[s], s) for s in ('test', 'udhr', 'test-common') if s in boards)}
+Per-language precision, recall (sensitivity), F1 and specificity for every model are in `reports/<set>/`.
+
+## Training
+
+{config['train_passages']:,} passages, best validation macro F1 at step {config['best_step']}, {config['seconds'] / 3600:.1f} hours.
+Languages sampled in proportion to n^{args.alpha}; {int(args.short_p * 100)}% of passages cut to a 20 to 80 character
+snippet; batch {args.batch_size}, AdamW at {args.lr}, cosine schedule, label smoothing {args.label_smoothing}.
+"""
+    (out / "README.md").write_text(card, encoding="utf-8")
+
+
+# --- command line ------------------------------------------------------------------------
+
+
+@dataclass
+class TrainArgs:
+    """Train MillID, evaluate it against GlotLID, and optionally push it to the Hub."""
+
+    dataset: str = "baobabtech/millid-dataset"
+    """Hub dataset id, or a local build directory with data/, udhr/ and labels.json"""
+    preset: Literal["tiny", "small", "medium", "large"] = "medium"
+    """model size: tiny ~2M and small ~12M for trials, medium ~114M, large ~190M parameters"""
+    max_len: int = 256
+    """bytes per passage, [CLS] included; a longer passage is cut to a window"""
+    steps: int = 60_000
+    """optimiser steps"""
+    batch_size: int = 512
+    """passages per step"""
+    lr: float = 3e-4
+    """peak learning rate, after warm-up"""
+    warmup: int = 2_000
+    """steps of linear warm-up, then cosine decay to 5% of the peak"""
+    weight_decay: float = 0.01
+    """AdamW weight decay, on weight matrices only"""
+    dropout: float = 0.1
+    """dropout in the encoder and before the head"""
+    label_smoothing: float = 0.1
+    """cross-entropy label smoothing"""
+    alpha: float = 0.3
+    """language sampling: a language is drawn in proportion to n^alpha (1: as the data is, 0: uniform)"""
+    short_p: float = 0.25
+    """share of training passages cut to a 20 to 80 character snippet"""
+    eval_every: int = 2_000
+    """steps between validation passes, which choose the checkpoint"""
+    eval_per_label: int = 20
+    """validation passages per language at each pass"""
+    patience: int = 12_000
+    """stop after this many steps without a better validation macro F1"""
+    commercial_only: bool = False
+    """leave out rows licensed for non-commercial use only"""
+    extra: list[str] = field(default_factory=list)
+    """more training parquet files (text, label): trained on, never evaluated on"""
+    compare_glotlid: bool = False
+    """score GlotLID too, as it is and restricted to MillID's labels"""
+    compare_fasttext: bool = False
+    """score Meta's fastText language identifier (NLLB, 218 labels) too"""
+    final_eval: Literal["all", "validation", "none"] = "all"
+    """which sets to score after training"""
+    output_dir: str = "millid-out"
+    """where the model, its card and reports are written"""
+    push_to: str | None = None
+    """Hub model repo to push to, e.g. baobabtech/millid"""
+    public: bool = False
+    """make the pushed repo public; private otherwise"""
+    seed: int = 7
+    """random seed"""
+    log_every: int = 100
+    """steps between training-loss lines and W&B points"""
+    threads: int = 0
+    """CPU threads for PyTorch; 0 leaves PyTorch's default"""
+    workers: int = -1
+    """DataLoader workers per training process; -1 picks from the cores available, 0 encodes in the training process"""
+    compile: bool = False
+    """torch.compile the model for training (GPU only); batches are then padded to max_len, so it compiles once"""
+    gpus: int = 0
+    """GPUs to train on, one process each, the batch split between them; 0 uses every GPU the machine has"""
+    wandb_project: str | None = os.environ.get("WANDB_PROJECT")
+    """Weights & Biases project; with WANDB_API_KEY set, 'millid' when not given"""
+    wandb_entity: str | None = os.environ.get("WANDB_ENTITY")
+    """Weights & Biases team or user"""
+    wandb_run_name: str | None = None
+    """run name in Weights & Biases"""
+    wandb_tags: list[str] = field(default_factory=list)
+    """run tags in Weights & Biases"""
+    no_wandb: bool = False
+    """never log to Weights & Biases, even with a key"""
+
+
+@dataclass
+class EvaluateArgs:
+    """Evaluate trained models on validation, test and UDHR, for instance again after the
+    dataset's evaluation splits were corrected."""
+
+    model: list[str]
+    """model directories or Hub repos; GlotLID and fastText read each set once for all of them"""
+    dataset: str = "baobabtech/millid-dataset"
+    """Hub dataset id or local build directory"""
+    compare_glotlid: bool = False
+    """score GlotLID too"""
+    compare_fasttext: bool = False
+    """score Meta's fastText language identifier (NLLB, 218 labels) too"""
+    output_dir: str = "millid-eval"
+    """where the reports are written, one directory per model"""
+    push: bool = False
+    """push each Hub model's new reports and model card back to its repo"""
+
+
+@dataclass
+class SweepArgs:
+    """Run Weights & Biases sweep trials in this process."""
+
+    sweep_id: str
+    """ENTITY/PROJECT/SWEEP_ID, from `wandb sweep millid/sweep.yaml`"""
+    count: int = 4
+    """trials this agent runs"""
+    dataset: str = "baobabtech/millid-dataset"
+    """used unless the sweep sets it"""
+
+
+@dataclass
+class PredictArgs:
+    """The language of each text."""
+
+    model: str
+    """model directory or Hub repo"""
+    texts: list[str] = field(default_factory=list)
+    """texts to identify"""
+    k: int = 3
+    """answers per text"""
+
+
+def evaluate_models(args: EvaluateArgs) -> None:
+    from types import SimpleNamespace
+
+    from huggingface_hub import HfApi, snapshot_download
+
+    root = load_dataset_dir(args.dataset)
+    names = json.loads((root / "labels.json").read_text(encoding="utf-8")).get("names", {})
+    compare = [ext for ext, on in (("glotlid", args.compare_glotlid), ("fasttext", args.compare_fasttext)) if on]
+    lids: dict = {}
+    cache: dict = {}
+    for ref in args.model:
+        local = Path(ref).exists()
+        src = Path(ref) if local else Path(snapshot_download(ref, allow_patterns=["*.json", "*.safetensors"]))
+        out = Path(args.output_dir) / ref.replace("/", "__")
+        print(f"== {ref}", flush=True)
+        model = MillID.from_pretrained(str(src))
+        results, boards = evaluate_all(model, root, out / "reports", compare, names=names, lids=lids, cache=cache)
+        config = json.loads((src / "config.json").read_text(encoding="utf-8"))
+        training = SimpleNamespace(**config["training"])
+        write_card(out, config, results, training, boards)
+        if args.push and not local:
+            HfApi().upload_folder(repo_id=ref, folder_path=str(out), commit_message=f"Evaluated again on {args.dataset}")
+            print(f"pushed https://huggingface.co/{ref}", flush=True)
+
+
+def sweep_agent(args: SweepArgs) -> None:
+    """Each trial takes its settings from the sweep and the TrainArgs defaults for the rest,
+    and reports val/best_macro_f1. No pushing, no GlotLID, validation only: a sweep chooses
+    settings, and the chosen ones are then trained in full."""
+    import wandb
+
+    def trial() -> None:
+        run = wandb.init()
+        a = TrainArgs(dataset=args.dataset)
+        for key, value in dict(run.config).items():
+            name = key.replace("-", "_")
+            if hasattr(a, name):
+                setattr(a, name, value)
+        a.push_to, a.compare_glotlid, a.final_eval = None, False, "validation"
+        a.output_dir = f"sweep/{run.id}"
+        train(a)
+        run.finish()
+
+    wandb.agent(args.sweep_id, function=trial, count=args.count)
+
+
+def main(argv: list[str] | None = None) -> None:
+    import tyro
+
+    args = tyro.extras.subcommand_cli_from_dict(
+        {"train": TrainArgs, "evaluate": EvaluateArgs, "sweep-agent": SweepArgs, "predict": PredictArgs},
+        args=argv,
+        prog="millid",
+    )
+    if isinstance(args, TrainArgs):
+        train(args)
+    elif isinstance(args, EvaluateArgs):
+        evaluate_models(args)
+    elif isinstance(args, SweepArgs):
+        sweep_agent(args)
+    else:
+        for text, answers in zip(args.texts, MillID.from_pretrained(args.model).predict(args.texts, args.k), strict=True):
+            print(text, "->", answers)
+
+
+if __name__ == "__main__":
+    os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
+    main(sys.argv[1:])

model.safetensors

@@ -0,0 +1,3 @@
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+oid sha256:8bb663d8c513daf3e4f95d5af02169c7f500ad7868ae3a9820baadc2abbedc8a
+size 11738164

reports/summary.json

@@ -0,0 +1,511 @@
+{
+  "validation": {
+    "millid": {
+      "top1_accuracy": 0.9577820504464577,
+      "top3_accuracy": 0.9888874405266245,
+      "top5_accuracy": 0.9932746203480415,
+      "passages": 245488,
+      "languages": 1626,
+      "macro_precision": 0.9538820330004751,
+      "macro_recall": 0.9590954075456577,
+      "macro_f1": 0.9550536411106632,
+      "macro_specificity": 0.9999740290832557,
+      "weighted_f1": 0.9579611590588513,
+      "median_f1": 0.9822485207100591,
+      "languages_f1_at_least_0_9": 1435,
+      "languages_f1_below_0_5": 8,
+      "mean_fp_per_million": 25.97091674438312,
+      "micro_f1": 0.9577820504464577,
+      "top1_by_size": {
+        "paragraph": 0.984960458955001,
+        "sentence": 0.9788390119887341,
+        "short": 0.9312356034595158
+      }
+    }
+  },
+  "test": {
+    "millid": {
+      "top1_accuracy": 0.9587247805328816,
+      "top3_accuracy": 0.9890484971341278,
+      "top5_accuracy": 0.9932401213781161,
+      "passages": 240241,
+      "languages": 1621,
+      "macro_precision": 0.9549770065582561,
+      "macro_recall": 0.9588167712047285,
+      "macro_f1": 0.9553762683202616,
+      "macro_specificity": 0.9999745397211366,
+      "weighted_f1": 0.9589133936122667,
+      "median_f1": 0.9824561403508771,
+      "languages_f1_at_least_0_9": 1446,
+      "languages_f1_below_0_5": 8,
+      "mean_fp_per_million": 25.46027886332467,
+      "micro_f1": 0.9587247805328816,
+      "top1_by_size": {
+        "paragraph": 0.9832807891792942,
+        "sentence": 0.9797006643418943,
+        "short": 0.933343407228316
+      }
+    },
+    "glotlid": {
+      "top1_accuracy": 0.977614145795264,
+      "top3_accuracy": 0.9977189572137978,
+      "top5_accuracy": 0.9983766301339072,
+      "passages": 240241,
+      "languages": 1621,
+      "macro_precision": 0.9782517709855841,
+      "macro_recall": 0.9751782136320077,
+      "macro_f1": 0.9748179587217549,
+      "macro_specificity": 0.9999867952157099,
+      "weighted_f1": 0.9776092036050326,
+      "median_f1": 1.0,
+      "languages_f1_at_least_0_9": 1488,
+      "languages_f1_below_0_5": 6,
+      "mean_fp_per_million": 13.204784290051938,
+      "micro_f1": 0.977614145795264,
+      "top1_by_size": {
+        "paragraph": 0.9910708837587715,
+        "sentence": 0.9852877542189529,
+        "short": 0.9663739311301133
+      }
+    },
+    "glotlid@millid": {
+      "top1_accuracy": 0.978292631149554,
+      "top3_accuracy": 0.9977897194900122,
+      "top5_accuracy": 0.9984557173837938,
+      "passages": 240241,
+      "languages": 1621,
+      "macro_precision": 0.9778492893480586,
+      "macro_recall": 0.9764998669631992,
+      "macro_f1": 0.9754780061637239,
+      "macro_specificity": 0.9999866076235475,
+      "weighted_f1": 0.9778914257911913,
+      "median_f1": 1.0,
+      "languages_f1_at_least_0_9": 1490,
+      "languages_f1_below_0_5": 5,
+      "mean_fp_per_million": 13.392376452534537,
+      "micro_f1": 0.978292631149554,
+      "top1_by_size": {
+        "paragraph": 0.9911725821214279,
+        "sentence": 0.98578410161122,
+        "short": 0.967431690103287
+      }
+    },
+    "fasttext": {
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+}

reports/test-common/fasttext@millid_per_language.csv

@@ -0,0 +1,214 @@
+label,support,tp,fp,fn,tn,precision,recall,f1,specificity,fp_per_million,sensitivity
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reports/test-common/fasttext@millid_summary.json

@@ -0,0 +1,22 @@
+{
+  "top1_accuracy": 0.9351982290199236,
+  "top3_accuracy": 0.9709584036410359,
+  "top5_accuracy": 0.9763301701317398,
+  "passages": 64597,
+  "languages": 213,
+  "macro_precision": 0.9353710554982398,
+  "macro_recall": 0.9200755454154029,
+  "macro_f1": 0.9173068426975766,
+  "macro_specificity": 0.9996942142238651,
+  "weighted_f1": 0.9361359929041825,
+  "median_f1": 0.9680365296803652,
+  "languages_f1_at_least_0_9": 166,
+  "languages_f1_below_0_5": 7,
+  "mean_fp_per_million": 305.78577613496856,
+  "micro_f1": 0.9351982290199236,
+  "top1_by_size": {
+    "paragraph": 0.9655103268498095,
+    "sentence": 0.958146778275258,
+    "short": 0.9124871606749817
+  }
+}

reports/test-common/fasttext_per_language.csv

@@ -0,0 +1,214 @@
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reports/test-common/fasttext_summary.json

@@ -0,0 +1,22 @@
+{
+  "top1_accuracy": 0.9338668978435531,
+  "top3_accuracy": 0.9706952335247767,
+  "top5_accuracy": 0.9761908447760732,
+  "passages": 64597,
+  "languages": 213,
+  "macro_precision": 0.9356570616737796,
+  "macro_recall": 0.9169210037343893,
+  "macro_f1": 0.9148824189777787,
+  "macro_specificity": 0.9996959024393602,
+  "weighted_f1": 0.9352000319739436,
+  "median_f1": 0.9680365296803652,
+  "languages_f1_at_least_0_9": 165,
+  "languages_f1_below_0_5": 8,
+  "mean_fp_per_million": 304.0975606397608,
+  "micro_f1": 0.9338668978435531,
+  "top1_by_size": {
+    "paragraph": 0.9651092841387607,
+    "sentence": 0.9562487833365778,
+    "short": 0.9112252384446075
+  }
+}

reports/test-common/glotlid@millid_per_language.csv

@@ -0,0 +1,214 @@
+label,support,tp,fp,fn,tn,precision,recall,f1,specificity,fp_per_million,sensitivity
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reports/test-common/glotlid@millid_summary.json

@@ -0,0 +1,22 @@
+{
+  "top1_accuracy": 0.9749369165750732,
+  "top3_accuracy": 0.9926931591250368,
+  "top5_accuracy": 0.994829481245259,
+  "passages": 64597,
+  "languages": 213,
+  "macro_precision": 0.9922871273519716,
+  "macro_recall": 0.9790381188248578,
+  "macro_f1": 0.9845885184031669,
+  "macro_specificity": 0.9999656167342897,
+  "weighted_f1": 0.9829375630604269,
+  "median_f1": 0.9983471074380166,
+  "languages_f1_at_least_0_9": 203,
+  "languages_f1_below_0_5": 0,
+  "mean_fp_per_million": 34.38326571019212,
+  "micro_f1": 0.9749369165750732,
+  "top1_by_size": {
+    "paragraph": 0.9948867054341287,
+    "sentence": 0.9885146972941405,
+    "short": 0.9609097578870139
+  }
+}

reports/test-common/glotlid_per_language.csv

@@ -0,0 +1,214 @@
+label,support,tp,fp,fn,tn,precision,recall,f1,specificity,fp_per_million,sensitivity
+abk_Cyrl,285,285,0,0,64312,1.0,1.0,1.0,1.0,0.0,1.0
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+afr_Latn,569,555,3,14,64025,0.994624,0.975395,0.984916,0.999953,46.854501,0.975395
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+taq_Latn,106,106,0,0,64491,1.0,1.0,1.0,1.0,0.0,1.0
+taq_Tfng,104,70,0,34,64493,1.0,0.673077,0.804598,1.0,0.0,0.673077
+tat_Cyrl,201,201,0,0,64396,1.0,1.0,1.0,1.0,0.0,1.0
+tel_Telu,555,553,0,2,64042,1.0,0.996396,0.998195,1.0,0.0,0.996396
+tgk_Cyrl,413,413,0,0,64184,1.0,1.0,1.0,1.0,0.0,1.0
+tha_Thai,431,414,0,17,64166,1.0,0.960557,0.979882,1.0,0.0,0.960557
+tir_Ethi,339,331,0,8,64258,1.0,0.976401,0.98806,1.0,0.0,0.976401
+ton_Latn,298,298,0,0,64299,1.0,1.0,1.0,1.0,0.0,1.0
+tpi_Latn,302,302,1,0,64294,0.9967,1.0,0.998347,0.999984,15.553309,1.0
+tsn_Latn,456,438,18,18,64123,0.960526,0.960526,0.960526,0.999719,280.631733,0.960526
+tso_Latn,581,532,0,49,64016,1.0,0.915663,0.955975,1.0,0.0,0.915663
+tuk_Latn,227,227,1,0,64369,0.995614,1.0,0.997802,0.999984,15.535187,1.0
+tum_Latn,265,264,1,1,64331,0.996226,0.996226,0.996226,0.999984,15.544364,0.996226
+tur_Latn,430,429,0,1,64167,1.0,0.997674,0.998836,1.0,0.0,0.997674
+twi_Latn,359,353,0,6,64238,1.0,0.983287,0.991573,1.0,0.0,0.983287
+tzm_Tfng,157,157,22,0,64418,0.877095,1.0,0.934524,0.999659,341.402855,1.0
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+urd_Arab,257,257,0,0,64340,1.0,1.0,1.0,1.0,0.0,1.0
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+vec_Latn,49,37,0,12,64548,1.0,0.755102,0.860465,1.0,0.0,0.755102
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+wol_Latn,214,214,2,0,64381,0.990741,1.0,0.995349,0.999969,31.064101,1.0
+xho_Latn,367,338,8,29,64222,0.976879,0.920981,0.948107,0.999875,124.55239,0.920981
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+yue_Hani,194,159,1,35,64402,0.99375,0.819588,0.898305,0.999984,15.527227,0.819588
+zsm_Latn,244,244,1,0,64352,0.995918,1.0,0.997955,0.999984,15.539291,1.0
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reports/test-common/glotlid_summary.json

@@ -0,0 +1,22 @@
+{
+  "top1_accuracy": 0.9738223137297398,
+  "top3_accuracy": 0.9924609501989257,
+  "top5_accuracy": 0.9946437141043701,
+  "passages": 64597,
+  "languages": 213,
+  "macro_precision": 0.9924377815827973,
+  "macro_recall": 0.9770740040297234,
+  "macro_f1": 0.9835053354686013,
+  "macro_specificity": 0.9999660547959016,
+  "weighted_f1": 0.9823407826125089,
+  "median_f1": 0.9982788296041308,
+  "languages_f1_at_least_0_9": 201,
+  "languages_f1_below_0_5": 0,
+  "mean_fp_per_million": 33.94520409829502,
+  "micro_f1": 0.9738223137297398,
+  "top1_by_size": {
+    "paragraph": 0.9946861840786043,
+    "sentence": 0.9876386996301343,
+    "short": 0.9593837123991196
+  }
+}

reports/test-common/leaderboard.csv

@@ -0,0 +1,214 @@
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+nno_Latn,Norwegian Nynorsk,427,0.9132,0.9275,0.8993,0.9845,1.0,0.9696,0.9845,1.0,0.9696,0.9542,0.9575,0.9508,0.9542,0.9575,0.9508,-0.0713,GlotLID,-0.041,fastText
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+ckb_Arab,Central Kurdish,421,0.9554,0.9974,0.9169,0.9769,1.0,0.9549,0.9769,1.0,0.9549,0.9952,1.0,0.9905,0.9952,1.0,0.9905,-0.0215,GlotLID,-0.0398,fastText
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+bod_Tibt,Tibetan,406,0.9354,0.9093,0.9631,0.9988,1.0,0.9975,0.9988,1.0,0.9975,0.8191,0.721,0.9483,0.8191,0.721,0.9483,-0.0634,GlotLID,0.1163,MillID
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+azb_Arab,South Azerbaijani,369,0.9586,0.9775,0.9404,0.9959,0.9973,0.9946,0.9959,0.9973,0.9946,0.9711,0.986,0.9566,0.9711,0.986,0.9566,-0.0374,GlotLID,-0.0126,fastText
+fin_Latn,Finnish,368,0.9291,0.9938,0.8723,1.0,1.0,1.0,1.0,1.0,1.0,0.9946,0.9973,0.9918,0.9946,0.9973,0.9918,-0.0709,GlotLID,-0.0655,fastText
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+xho_Latn,Xhosa,367,0.8953,0.9455,0.8501,0.9481,0.9769,0.921,0.9481,0.9769,0.921,0.9511,0.9485,0.9537,0.9511,0.9485,0.9537,-0.0528,GlotLID,-0.0558,fastText
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+ita_Latn,Italian,342,0.9216,0.9932,0.8596,0.9838,0.994,0.9737,0.9838,0.994,0.9737,0.949,0.9203,0.9795,0.949,0.9203,0.9795,-0.0621,GlotLID,-0.0274,fastText
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+kaz_Cyrl,Kazakh,340,0.9836,1.0,0.9676,1.0,1.0,1.0,1.0,1.0,1.0,0.9926,0.9941,0.9912,0.9926,0.9941,0.9912,-0.0164,GlotLID,-0.0091,tie
+tir_Ethi,Tigrinya,339,0.9824,0.9795,0.9853,0.9881,1.0,0.9764,0.9985,1.0,0.9971,0.9956,1.0,0.9912,0.9956,1.0,0.9912,-0.0162,GlotLID,-0.0132,fastText
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+hat_Latn,Haitian Creole,338,0.9865,1.0,0.9734,0.997,0.997,0.997,0.997,0.997,0.997,0.9774,0.9939,0.9615,0.9774,0.9939,0.9615,-0.0105,GlotLID,0.0091,tie
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+vie_Latn,Vietnamese,338,0.9519,1.0,0.9083,1.0,1.0,1.0,1.0,1.0,1.0,0.9836,0.994,0.9734,0.9836,0.994,0.9734,-0.0481,GlotLID,-0.0316,fastText
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+lao_Laoo,Lao,335,0.997,1.0,0.994,1.0,1.0,1.0,1.0,1.0,1.0,0.991,1.0,0.9821,0.991,1.0,0.9821,-0.003,tie,0.006,tie
+chv_Cyrl,Chuvash,335,0.9985,1.0,0.997,1.0,1.0,1.0,1.0,1.0,1.0,0.9926,0.9853,1.0,0.9926,0.9853,1.0,-0.0015,tie,0.0059,tie
+ind_Latn,Indonesian,335,0.8419,0.8849,0.803,0.9911,0.9824,1.0,0.9911,0.9824,1.0,0.8873,0.8497,0.9284,0.8873,0.8497,0.9284,-0.1492,GlotLID,-0.0454,fastText
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+oci_Latn,Occitan,333,0.9574,0.9692,0.9459,0.9911,0.9823,1.0,0.9911,0.9823,1.0,0.9619,0.9398,0.985,0.9619,0.9398,0.985,-0.0336,GlotLID,-0.0044,tie
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+lit_Latn,Lithuanian,330,0.9676,0.9843,0.9515,0.9955,0.991,1.0,0.9955,0.991,1.0,0.974,0.9815,0.9667,0.974,0.9815,0.9667,-0.0278,GlotLID,-0.0064,tie
+swe_Latn,Swedish,328,0.9588,0.9602,0.9573,0.9717,0.9504,0.9939,0.9717,0.9504,0.9939,0.9767,0.9968,0.9573,0.9767,0.9968,0.9573,-0.0129,GlotLID,-0.0179,fastText
+snd_Arab,Sindhi,325,0.9891,0.9969,0.9815,1.0,1.0,1.0,1.0,1.0,1.0,0.9848,0.973,0.9969,0.9833,0.9701,0.9969,-0.0109,GlotLID,0.0058,tie
+kat_Geor,Georgian,324,0.9954,0.9969,0.9938,1.0,1.0,1.0,1.0,1.0,1.0,0.9859,1.0,0.9722,0.9859,1.0,0.9722,-0.0046,tie,0.0094,tie
+jpn_Jpan,Japanese,323,0.9746,1.0,0.9505,0.9969,0.9938,1.0,0.9969,0.9938,1.0,0.8684,0.9695,0.7864,0.8684,0.9695,0.7864,-0.0223,GlotLID,0.1062,MillID
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+ukr_Cyrl,Ukrainian,322,0.9617,0.9901,0.9348,1.0,1.0,1.0,1.0,1.0,1.0,0.9783,0.9753,0.9814,0.9783,0.9753,0.9814,-0.0383,GlotLID,-0.0167,fastText
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+cat_Latn,Catalan,318,0.9497,0.9497,0.9497,0.9845,0.9695,1.0,0.9845,0.9695,1.0,0.9528,0.9528,0.9528,0.9528,0.9528,0.9528,-0.0348,GlotLID,-0.0031,tie
+ayr_Latn,Central Aymara,309,0.9886,0.9967,0.9806,1.0,1.0,1.0,1.0,1.0,1.0,0.9752,0.9966,0.9547,0.9752,0.9966,0.9547,-0.0114,GlotLID,0.0134,MillID
+yor_Latn,Yoruba,308,0.987,0.9902,0.9838,0.9984,0.9968,1.0,0.9984,0.9968,1.0,0.9683,0.9472,0.9903,0.9683,0.9472,0.9903,-0.0114,GlotLID,0.0187,MillID
+ewe_Latn,Ewe,308,0.9902,0.9967,0.9838,1.0,1.0,1.0,1.0,1.0,1.0,0.9852,0.9967,0.974,0.9852,0.9967,0.974,-0.0098,tie,0.005,tie
+roh_Latn,Romansh,307,0.9789,0.9773,0.9805,1.0,1.0,1.0,1.0,1.0,1.0,0.9742,0.9649,0.9837,0.9742,0.9649,0.9837,-0.0211,GlotLID,0.0047,tie
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+tpi_Latn,Tok Pisin,302,0.9934,0.9967,0.9901,0.9983,0.9967,1.0,0.9983,0.9967,1.0,0.9867,0.9933,0.9801,0.9867,0.9933,0.9801,-0.005,tie,0.0067,tie
+lvs_Latn,Standard Latvian,298,0.9603,0.9893,0.9329,0.995,0.99,1.0,0.995,0.99,1.0,0.9472,0.9619,0.9329,0.9472,0.9619,0.9329,-0.0347,GlotLID,0.0131,MillID
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+epo_Latn,Esperanto,294,0.976,0.9828,0.9694,0.9983,1.0,0.9966,0.9983,1.0,0.9966,0.9882,0.9799,0.9966,0.9882,0.9799,0.9966,-0.0223,GlotLID,-0.0122,fastText
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+run_Latn,Rundi,291,0.8832,0.8832,0.8832,0.9848,0.97,1.0,0.9848,0.97,1.0,0.8545,0.9347,0.7869,0.8545,0.9347,0.7869,-0.1016,GlotLID,0.0287,MillID
+mlt_Latn,Maltese,291,0.9948,0.9966,0.9931,0.9932,0.9864,1.0,0.9932,0.9864,1.0,0.9614,0.9821,0.9416,0.9614,0.9821,0.9416,0.0017,tie,0.0334,MillID
+bho_Deva,Bhojpuri,290,0.9556,0.9459,0.9655,0.9983,0.9966,1.0,0.9983,0.9966,1.0,0.8732,0.9198,0.831,0.8732,0.9198,0.831,-0.0426,GlotLID,0.0824,MillID
+bak_Cyrl,Bashkir,289,0.9825,0.9929,0.9723,1.0,1.0,1.0,1.0,1.0,1.0,0.9846,0.9762,0.9931,0.9846,0.9762,0.9931,-0.0175,GlotLID,-0.002,tie
+kmr_Latn,Northern Kurdish,289,0.9791,0.986,0.9723,1.0,1.0,1.0,1.0,1.0,1.0,0.9747,0.9507,1.0,0.9747,0.9507,1.0,-0.0209,GlotLID,0.0044,tie
+bul_Cyrl,Bulgarian,289,0.9636,0.9653,0.9619,1.0,1.0,1.0,1.0,1.0,1.0,0.9827,0.9827,0.9827,0.9827,0.9827,0.9827,-0.0364,GlotLID,-0.0191,fastText
+cmn_Hani,Chinese (Mandarin),288,0.9149,0.9348,0.8958,0.9663,0.9379,0.9965,0.9663,0.9379,0.9965,0.4884,0.9406,0.3299,0.4884,0.9406,0.3299,-0.0514,GlotLID,0.4265,MillID
+bam_Latn,Bambara,287,0.9521,0.936,0.9686,0.9983,0.9965,1.0,0.9983,0.9965,1.0,0.7923,0.6899,0.9303,0.7923,0.6899,0.9303,-0.0462,GlotLID,0.1598,MillID
+abk_Cyrl,Abkhazian,285,0.9965,1.0,0.993,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0035,tie,-0.0035,tie
+bxr_Cyrl,Russia Buriat,277,0.9726,0.9852,0.9603,1.0,1.0,1.0,1.0,1.0,1.0,0.9387,1.0,0.8845,0.9387,1.0,0.8845,-0.0274,GlotLID,0.0339,MillID
+isl_Latn,Icelandic,276,0.949,0.9921,0.9094,1.0,1.0,1.0,1.0,1.0,1.0,0.7886,0.6509,1.0,0.7886,0.6509,1.0,-0.051,GlotLID,0.1604,MillID
+lmo_Latn,Lombard,276,0.9149,0.9565,0.8768,0.9875,0.9753,1.0,0.9875,0.9753,1.0,0.9097,0.9065,0.913,0.9081,0.9032,0.913,-0.0725,GlotLID,0.0068,tie
+npi_Deva,Nepali,273,0.9649,0.9739,0.956,0.9908,0.9926,0.989,0.9927,0.9927,0.9927,0.9745,0.9675,0.9817,0.9745,0.9675,0.9817,-0.0278,GlotLID,-0.0097,tie
+kam_Latn,Kamba,272,0.9795,0.9925,0.9669,0.9716,1.0,0.9449,0.9736,1.0,0.9485,0.8625,0.9952,0.761,0.8625,0.9952,0.761,0.0059,tie,0.117,MillID
+quy_Latn,Ayacucho Quechua,271,0.9398,0.9918,0.893,0.9246,1.0,0.8598,0.9331,1.0,0.8745,0.9794,0.9924,0.9668,0.9794,0.9924,0.9668,0.0067,tie,-0.0396,fastText
+srp_Cyrl,Serbian,269,0.974,0.974,0.974,1.0,1.0,1.0,1.0,1.0,1.0,0.9834,0.9779,0.9888,0.9834,0.9779,0.9888,-0.026,GlotLID,-0.0094,tie
+nob_Latn,Norwegian Bokmål,265,0.8207,0.8043,0.8377,0.9612,0.942,0.9811,0.9594,0.9386,0.9811,0.8942,0.8796,0.9094,0.8942,0.8796,0.9094,-0.1387,GlotLID,-0.0735,fastText
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+tum_Latn,Tumbuka,265,0.9773,0.981,0.9736,0.9962,0.9962,0.9962,0.9962,0.9962,0.9962,0.9662,0.9625,0.9698,0.9662,0.9625,0.9698,-0.019,GlotLID,0.0111,MillID
+khk_Cyrl,Halh Mongolian,262,0.9774,0.9664,0.9885,1.0,1.0,1.0,1.0,1.0,1.0,0.9205,0.8925,0.9504,0.9205,0.8925,0.9504,-0.0226,GlotLID,0.0568,MillID
+nya_Latn,Nyanja,262,0.9518,0.9611,0.9427,0.9786,0.996,0.9618,0.9786,0.996,0.9618,0.9259,0.9747,0.8817,0.9259,0.9747,0.8817,-0.0268,GlotLID,0.026,MillID
+nld_Latn,Dutch,259,0.9363,0.9671,0.9073,0.9719,0.9453,1.0,0.9719,0.9453,1.0,0.9597,0.9542,0.9653,0.9597,0.9542,0.9653,-0.0356,GlotLID,-0.0234,fastText
+ltg_Latn,Latgalian,259,0.9656,0.9547,0.9768,0.9981,0.9962,1.0,0.9981,0.9962,1.0,0.9647,0.9801,0.9498,0.9647,0.9801,0.9498,-0.0324,GlotLID,0.0009,tie
+ibo_Latn,Igbo,257,0.9922,0.9961,0.9883,0.9981,0.9961,1.0,0.9981,0.9961,1.0,0.9922,0.9961,0.9883,0.9922,0.9961,0.9883,-0.0059,tie,0.0,tie
+bis_Latn,Bislama,257,0.9862,1.0,0.9728,1.0,1.0,1.0,1.0,1.0,1.0,0.9681,0.9918,0.9455,0.9681,0.9918,0.9455,-0.0138,GlotLID,0.0181,MillID
+urd_Arab,Urdu,257,0.9622,0.9837,0.9416,1.0,1.0,1.0,1.0,1.0,1.0,0.9345,0.8771,1.0,0.9345,0.8771,1.0,-0.0378,GlotLID,0.0277,MillID
+pol_Latn,Polish,254,0.9565,0.9603,0.9528,0.9844,0.9731,0.9961,0.9844,0.9731,0.9961,0.92,0.8547,0.9961,0.92,0.8547,0.9961,-0.0279,GlotLID,0.0365,MillID
+eus_Latn,Basque,254,0.9782,0.9841,0.9724,0.9922,0.9845,1.0,0.9922,0.9845,1.0,0.9862,0.9843,0.9882,0.9862,0.9843,0.9882,-0.014,GlotLID,-0.008,tie
+ilo_Latn,Iloko,252,0.9901,0.992,0.9881,1.0,1.0,1.0,1.0,1.0,1.0,0.9735,1.0,0.9484,0.9735,1.0,0.9484,-0.0099,tie,0.0165,MillID
+ltz_Latn,Luxembourgish,251,0.9742,0.9722,0.9761,1.0,1.0,1.0,1.0,1.0,1.0,0.9544,0.9127,1.0,0.9544,0.9127,1.0,-0.0258,GlotLID,0.0198,MillID
+pcm_Latn,Nigerian Pidgin,251,0.8228,0.9126,0.749,0.838,0.9151,0.7729,0.8559,0.914,0.8048,0.5015,1.0,0.3347,0.5407,1.0,0.3705,-0.0332,GlotLID,0.2821,MillID
+arn_Latn,Mapuche,250,0.992,0.996,0.988,1.0,1.0,1.0,1.0,1.0,1.0,0.9585,0.9957,0.924,0.9585,0.9957,0.924,-0.008,tie,0.0335,MillID
+plt_Latn,Malagasy (Plateau),248,0.9691,0.9916,0.9476,0.998,0.996,1.0,0.998,0.996,1.0,0.9818,0.9838,0.9798,0.9818,0.9838,0.9798,-0.0289,GlotLID,-0.0127,fastText
+lij_Latn,Ligurian,245,0.9379,0.9865,0.8939,0.998,0.9959,1.0,0.998,0.9959,1.0,0.8571,0.9643,0.7714,0.8571,0.9643,0.7714,-0.0601,GlotLID,0.0808,MillID
+ast_Latn,Asturian,244,0.9188,0.9598,0.8811,0.9702,0.9421,1.0,0.9702,0.9421,1.0,0.9333,0.9203,0.9467,0.9333,0.9203,0.9467,-0.0514,GlotLID,-0.0145,fastText
+zsm_Latn,Standard Malay,244,0.7891,0.8043,0.7746,0.998,0.9959,1.0,0.998,0.9959,1.0,0.9079,0.9274,0.8893,0.9079,0.9274,0.8893,-0.2088,GlotLID,-0.1188,fastText
+nia_Latn,Nias,242,0.9979,1.0,0.9959,1.0,1.0,1.0,1.0,1.0,1.0,0.9876,0.9876,0.9876,0.9876,0.9876,0.9876,-0.0021,tie,0.0103,MillID
+dan_Latn,Danish,242,0.8874,0.9318,0.8471,0.9499,0.9222,0.9793,0.9499,0.9222,0.9793,0.9623,0.9746,0.9504,0.9623,0.9746,0.9504,-0.0625,GlotLID,-0.0749,fastText
+kir_Cyrl,Kyrgyz,242,0.9733,0.9673,0.9793,1.0,1.0,1.0,1.0,1.0,1.0,0.9279,0.8782,0.9835,0.9279,0.8782,0.9835,-0.0267,GlotLID,0.0454,MillID
+tah_Latn,Tahitian,242,0.9833,0.9916,0.9752,1.0,1.0,1.0,1.0,1.0,1.0,0.9917,1.0,0.9835,0.9917,1.0,0.9835,-0.0167,GlotLID,-0.0083,tie
+san_Deva,Sanskrit,239,0.9895,0.9916,0.9874,0.9895,0.9958,0.9833,0.9895,0.9958,0.9833,0.9705,0.9787,0.9623,0.9705,0.9787,0.9623,0.0,tie,0.0191,MillID
+fao_Latn,Faroese,237,0.975,0.963,0.9873,1.0,1.0,1.0,1.0,1.0,1.0,0.4808,1.0,0.3165,0.4808,1.0,0.3165,-0.025,GlotLID,0.4942,MillID
+sag_Latn,Sango,237,0.9936,1.0,0.9873,1.0,1.0,1.0,1.0,1.0,1.0,0.985,1.0,0.9705,0.985,1.0,0.9705,-0.0064,tie,0.0086,tie
+mai_Deva,Maithili,234,0.9258,0.9763,0.8803,0.9957,0.9915,1.0,0.9957,0.9915,1.0,0.9196,0.8884,0.953,0.9196,0.8884,0.953,-0.0699,GlotLID,0.0063,tie
+sin_Sinh,Sinhala,232,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9978,1.0,0.9957,0.9978,1.0,0.9957,0.0,tie,0.0022,tie
+gug_Latn,Paraguayan Guaraní,228,0.9686,0.9908,0.9474,0.9956,0.9913,1.0,0.9956,0.9913,1.0,0.933,0.9854,0.886,0.933,0.9854,0.886,-0.027,GlotLID,0.0356,MillID
+tuk_Latn,Turkmen,227,0.9799,0.9955,0.9648,0.9978,0.9956,1.0,0.9978,0.9956,1.0,0.9821,1.0,0.9648,0.9821,1.0,0.9648,-0.0179,GlotLID,-0.0022,tie
+gla_Latn,Scottish Gaelic,223,0.9888,0.991,0.9865,0.9955,0.9911,1.0,0.9955,0.9911,1.0,0.9818,0.9954,0.9686,0.9818,0.9954,0.9686,-0.0068,tie,0.0069,tie
+bem_Latn,Bemba,222,0.9729,0.9773,0.9685,0.9841,0.9954,0.973,0.9818,0.9908,0.973,0.9643,0.9558,0.973,0.9643,0.9558,0.973,-0.009,tie,0.0086,tie
+fur_Latn,Friulian,221,0.9484,0.9854,0.914,0.9955,0.991,1.0,0.9955,0.991,1.0,0.9138,0.9423,0.8869,0.9074,0.9289,0.8869,-0.0471,GlotLID,0.0409,MillID
+kac_Latn,Kachin,216,0.9977,1.0,0.9954,1.0,1.0,1.0,1.0,1.0,1.0,0.986,0.9953,0.9769,0.986,0.9953,0.9769,-0.0023,tie,0.0117,MillID
+glg_Latn,Galician,215,0.9517,0.9409,0.9628,0.9663,0.9348,1.0,0.9663,0.9348,1.0,0.9316,0.8866,0.9814,0.9316,0.8866,0.9814,-0.0146,GlotLID,0.0202,MillID
+lin_Latn,Lingala,215,0.9789,0.9858,0.9721,0.9953,0.9953,0.9953,0.9953,0.9953,0.9953,0.9838,0.9815,0.986,0.9838,0.9815,0.986,-0.0164,GlotLID,-0.0048,tie
+wol_Latn,Wolof,214,0.969,0.9902,0.9486,0.9953,0.9907,1.0,0.9953,0.9907,1.0,0.9555,0.9577,0.9533,0.9579,0.9579,0.9579,-0.0264,GlotLID,0.011,MillID
+als_Latn,Albanian (Tosk),213,0.976,1.0,0.9531,1.0,1.0,1.0,1.0,1.0,1.0,0.9812,0.9812,0.9812,0.9812,0.9812,0.9812,-0.024,GlotLID,-0.0053,tie
+kin_Latn,Kinyarwanda,210,0.8491,0.8411,0.8571,0.9807,0.9951,0.9667,0.9807,0.9951,0.9667,0.8535,0.7701,0.9571,0.8535,0.7701,0.9571,-0.1316,GlotLID,-0.0044,tie
+alt_Cyrl,Southern Altai,208,0.9653,0.9949,0.9375,1.0,1.0,1.0,1.0,1.0,1.0,0.8966,1.0,0.8125,0.8966,1.0,0.8125,-0.0347,GlotLID,0.0688,MillID
+kea_Latn,Kabuverdianu,205,0.958,0.97,0.9463,0.9976,0.9951,1.0,0.9976,0.9951,1.0,0.8736,1.0,0.7756,0.8736,1.0,0.7756,-0.0395,GlotLID,0.0844,MillID
+heb_Hebr,Hebrew,203,0.9752,0.9801,0.9704,1.0,1.0,1.0,1.0,1.0,1.0,0.9975,0.9951,1.0,0.9975,0.9951,1.0,-0.0248,GlotLID,-0.0223,fastText
+gle_Latn,Irish,203,0.9776,0.9899,0.9655,1.0,1.0,1.0,1.0,1.0,1.0,0.9826,0.99,0.9754,0.9826,0.99,0.9754,-0.0224,GlotLID,-0.0051,tie
+ban_Latn,Balinese,202,0.9695,0.9948,0.9455,1.0,1.0,1.0,1.0,1.0,1.0,0.9223,0.9674,0.8812,0.9223,0.9674,0.8812,-0.0305,GlotLID,0.0473,MillID
+tat_Cyrl,Tatar,201,0.9776,0.98,0.9751,1.0,1.0,1.0,1.0,1.0,1.0,0.9685,0.9434,0.995,0.9685,0.9434,0.995,-0.0224,GlotLID,0.009,tie
+nus_Latn,Nuer,199,0.9925,0.99,0.995,1.0,1.0,1.0,1.0,1.0,1.0,0.9898,1.0,0.9799,0.9898,1.0,0.9799,-0.0075,tie,0.0026,tie
+bos_Latn,Bosnian,198,0.6852,0.764,0.6212,1.0,1.0,1.0,1.0,1.0,1.0,0.8967,0.8945,0.899,0.8967,0.8945,0.899,-0.3148,GlotLID,-0.2115,fastText
+hne_Deva,Chhattisgarhi,198,0.9381,0.9579,0.9192,0.9851,0.9706,1.0,0.9851,0.9706,1.0,0.9016,0.9255,0.8788,0.9016,0.9255,0.8788,-0.0469,GlotLID,0.0366,MillID
+lim_Latn,Limburgish,196,0.896,0.9385,0.8571,1.0,1.0,1.0,1.0,1.0,1.0,0.9556,0.9786,0.9337,0.9556,0.9786,0.9337,-0.104,GlotLID,-0.0596,fastText
+yue_Hani,Cantonese,194,0.8937,0.948,0.8454,0.8983,0.9938,0.8196,0.9014,0.9938,0.8247,0.2588,0.1493,0.9691,0.2588,0.1493,0.9691,-0.0077,tie,0.635,MillID
+war_Latn,Waray,193,0.9462,0.9832,0.9119,1.0,1.0,1.0,1.0,1.0,1.0,0.9511,1.0,0.9067,0.9511,1.0,0.9067,-0.0538,GlotLID,-0.0049,tie
+kik_Latn,Kikuyu,193,0.9922,1.0,0.9845,1.0,1.0,1.0,1.0,1.0,1.0,0.8478,1.0,0.7358,0.8478,1.0,0.7358,-0.0078,tie,0.1444,MillID
+crh_Latn,Crimean Tatar,189,0.9711,0.9635,0.9788,1.0,1.0,1.0,1.0,1.0,1.0,0.9786,0.9892,0.9683,0.9786,0.9892,0.9683,-0.0289,GlotLID,-0.0075,tie
+cym_Latn,Welsh,189,0.9893,1.0,0.9788,1.0,1.0,1.0,1.0,1.0,1.0,0.9867,0.9946,0.9788,0.9867,0.9946,0.9788,-0.0107,GlotLID,0.0026,tie
+khm_Khmr,Khmer,187,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+azj_Latn,North Azerbaijani,187,0.9731,0.9784,0.9679,0.9973,0.9947,1.0,0.9973,0.9947,1.0,0.9787,0.9735,0.984,0.9787,0.9735,0.984,-0.0242,GlotLID,-0.0056,tie
+ces_Latn,Czech,183,0.9389,0.9548,0.9235,1.0,1.0,1.0,1.0,1.0,1.0,0.9683,0.9385,1.0,0.9683,0.9385,1.0,-0.0611,GlotLID,-0.0294,fastText
+fon_Latn,Fon,180,0.9833,0.9833,0.9833,1.0,1.0,1.0,1.0,1.0,1.0,0.9535,1.0,0.9111,0.9535,1.0,0.9111,-0.0167,GlotLID,0.0298,MillID
+hye_Armn,Armenian,178,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.977,1.0,0.9551,0.977,1.0,0.9551,0.0,tie,0.023,MillID
+shn_Mymr,Shan,174,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9854,1.0,0.9713,0.9854,1.0,0.9713,0.0,tie,0.0146,MillID
+scn_Latn,Sicilian,172,0.9767,0.9767,0.9767,1.0,1.0,1.0,1.0,1.0,1.0,0.9243,0.8636,0.9942,0.9243,0.8636,0.9942,-0.0233,GlotLID,0.0524,MillID
+gom_Deva,Goan Konkani,170,0.948,0.9318,0.9647,0.9798,0.9605,1.0,0.9798,0.9605,1.0,0.9524,0.9091,1.0,0.9524,0.9091,1.0,-0.0319,GlotLID,-0.0044,tie
+hrv_Latn,Croatian,168,0.6588,0.6512,0.6667,0.9941,0.9882,1.0,0.9941,0.9882,1.0,0.8563,0.8439,0.869,0.8563,0.8439,0.869,-0.3353,GlotLID,-0.1975,fastText
+kab_Latn,Kabyle,165,0.9354,0.95,0.9212,1.0,1.0,1.0,1.0,1.0,1.0,0.9558,0.931,0.9818,0.9558,0.931,0.9818,-0.0646,GlotLID,-0.0204,fastText
+fuv_Latn,Nigerian Fulfulde,164,0.9073,0.9928,0.8354,0.8767,1.0,0.7805,0.8919,1.0,0.8049,0.8587,0.763,0.9817,0.8587,0.763,0.9817,0.0154,MillID,0.0486,MillID
+arb_Latn,Standard Arabic,163,0.9419,0.9932,0.8957,0.906,1.0,0.8282,0.9133,1.0,0.8405,0.8522,0.9688,0.7607,0.8522,0.9688,0.7607,0.0286,MillID,0.0897,MillID
+mri_Latn,Māori,162,0.9969,1.0,0.9938,1.0,1.0,1.0,1.0,1.0,1.0,0.6639,0.5016,0.9815,0.6639,0.5016,0.9815,-0.0031,tie,0.333,MillID
+udm_Cyrl,Udmurt,159,0.9779,0.981,0.9748,1.0,1.0,1.0,1.0,1.0,1.0,0.8244,0.9583,0.7233,0.8244,0.9583,0.7233,-0.0221,GlotLID,0.1535,MillID
+krc_Cyrl,Karachay-Balkar,159,0.9748,0.9748,0.9748,1.0,1.0,1.0,1.0,1.0,1.0,0.8778,0.7861,0.9937,0.8778,0.7861,0.9937,-0.0252,GlotLID,0.0971,MillID
+jav_Latn,Javanese,158,0.9551,0.9675,0.943,0.9937,0.9875,1.0,0.9937,0.9875,1.0,0.9313,0.8814,0.9873,0.9313,0.8814,0.9873,-0.0386,GlotLID,0.0238,MillID
+tzm_Tfng,Central Atlas Tamazight,157,0.6973,0.875,0.5796,0.9345,0.8771,1.0,0.9345,0.8771,1.0,0.7512,0.6015,1.0,0.7512,0.6015,1.0,-0.2372,GlotLID,-0.0539,fastText
+bel_Cyrl,Belarusian,157,0.9841,0.981,0.9873,1.0,1.0,1.0,1.0,1.0,1.0,0.9937,0.9874,1.0,0.9937,0.9874,1.0,-0.0159,GlotLID,-0.0095,tie
+bjn_Latn,Banjar,153,0.9369,0.9527,0.9216,0.9967,0.9935,1.0,0.9967,0.9935,1.0,0.8688,0.8323,0.9085,0.8688,0.8323,0.9085,-0.0599,GlotLID,0.0681,MillID
+oss_Cyrl,Ossetic,152,0.9868,0.9933,0.9803,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0132,GlotLID,-0.0132,fastText
+sun_Latn,Sundanese,143,0.9677,0.9926,0.9441,1.0,1.0,1.0,1.0,1.0,1.0,0.9467,0.9045,0.993,0.9467,0.9045,0.993,-0.0323,GlotLID,0.0211,MillID
+srd_Latn,Sardinian,139,0.9751,0.9648,0.9856,1.0,1.0,1.0,1.0,1.0,1.0,0.9363,0.9766,0.8993,0.9363,0.9766,0.8993,-0.0249,GlotLID,0.0388,MillID
+fil_Latn,Filipino,137,0.9466,0.992,0.9051,0.9815,0.9925,0.9708,0.9815,0.9925,0.9708,0.9926,1.0,0.9854,0.9926,1.0,0.9854,-0.035,GlotLID,-0.0461,fastText
+ady_Cyrl,Adyghe,135,0.9963,1.0,0.9926,1.0,1.0,1.0,1.0,1.0,1.0,0.9814,0.9851,0.9778,0.9814,0.9851,0.9778,-0.0037,tie,0.0149,MillID
+diq_Latn,Dimli (individual language),134,0.9339,0.9756,0.8955,1.0,1.0,1.0,1.0,1.0,1.0,0.9333,0.9835,0.8881,0.9333,0.9835,0.8881,-0.0661,GlotLID,0.0005,tie
+ewo_Latn,Ewondo,134,0.9591,0.9556,0.9627,0.8787,1.0,0.7836,0.8833,1.0,0.791,0.8596,1.0,0.7537,0.8596,1.0,0.7537,0.0758,MillID,0.0995,MillID
+mos_Latn,Mossi,133,0.9774,0.9774,0.9774,1.0,1.0,1.0,1.0,1.0,1.0,0.9811,0.9848,0.9774,0.9811,0.9848,0.9774,-0.0226,GlotLID,-0.0037,tie
+xmf_Geor,Mingrelian,132,0.9848,0.9848,0.9848,1.0,1.0,1.0,1.0,1.0,1.0,0.963,0.942,0.9848,0.963,0.942,0.9848,-0.0152,GlotLID,0.0219,MillID
+kbp_Latn,Kabiyè,130,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9766,0.9921,0.9615,0.9766,0.9921,0.9615,0.0,tie,0.0234,MillID
+pag_Latn,Pangasinan,122,0.9573,1.0,0.918,0.9959,0.9919,1.0,0.9959,0.9919,1.0,0.959,0.959,0.959,0.959,0.959,0.959,-0.0387,GlotLID,-0.0018,tie
+sat_Olck,Santali,122,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+kmb_Latn,Kimbundu,119,0.9833,0.9752,0.9916,0.9917,0.9835,1.0,0.9917,0.9835,1.0,0.9633,0.9365,0.9916,0.9633,0.9365,0.9916,-0.0083,tie,0.0201,MillID
+kas_Arab,Kashmiri,118,0.9957,1.0,0.9915,1.0,1.0,1.0,1.0,1.0,1.0,0.9784,1.0,0.9576,0.9784,1.0,0.9576,-0.0043,tie,0.0174,MillID
+bjn_Arab,Banjar,116,0.9915,0.9831,1.0,0.8342,1.0,0.7155,0.9869,1.0,0.9741,0.4533,1.0,0.2931,0.9596,1.0,0.9224,0.0046,tie,0.0318,MillID
+ace_Latn,Acehnese,113,0.9776,0.9909,0.9646,1.0,1.0,1.0,1.0,1.0,1.0,0.8943,0.8271,0.9735,0.88,0.8029,0.9735,-0.0224,GlotLID,0.0976,MillID
+nav_Latn,Navajo,113,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.968,1.0,0.9381,0.968,1.0,0.9381,0.0,tie,0.032,MillID
+taq_Latn,Tamasheq,106,0.9016,1.0,0.8208,1.0,1.0,1.0,1.0,1.0,1.0,0.8377,0.9412,0.7547,0.8377,0.9412,0.7547,-0.0984,GlotLID,0.0639,MillID
+taq_Tfng,Tamasheq,104,0.654,0.6449,0.6635,0.8046,1.0,0.6731,0.8046,1.0,0.6731,0.0,0.0,0.0,0.0,0.0,0.0,-0.1506,GlotLID,0.654,MillID
+umb_Latn,Umbundu,100,0.98,0.98,0.98,0.995,0.9901,1.0,0.995,0.9901,1.0,0.8972,0.8421,0.96,0.8972,0.8421,0.96,-0.015,GlotLID,0.0828,MillID
+mag_Deva,Magahi,97,0.89,0.8641,0.9175,1.0,1.0,1.0,1.0,1.0,1.0,0.7723,0.7429,0.8041,0.7723,0.7429,0.8041,-0.11,GlotLID,0.1177,MillID
+awa_Deva,Awadhi,91,0.8977,0.9294,0.8681,0.963,0.9286,1.0,0.963,0.9286,1.0,0.7571,0.7791,0.7363,0.7571,0.7791,0.7363,-0.0652,GlotLID,0.1407,MillID
+bug_Latn,Buginese,78,0.9806,0.987,0.9744,0.9811,0.963,1.0,0.9811,0.963,1.0,0.9375,0.9146,0.9615,0.9375,0.9146,0.9615,-0.0005,tie,0.0431,MillID
+knc_Arab,Central Kanuri,68,0.5217,1.0,0.3529,0.8571,1.0,0.75,0.8595,0.9811,0.7647,0.029,1.0,0.0147,0.029,1.0,0.0147,-0.3378,GlotLID,0.4928,MillID
+min_Latn,Minangkabau,55,0.8947,0.8644,0.9273,0.991,0.9821,1.0,0.991,0.9821,1.0,0.3333,1.0,0.2,0.3333,1.0,0.2,-0.0963,GlotLID,0.5614,MillID
+vec_Latn,Venetian,49,0.7664,0.7069,0.8367,0.8605,1.0,0.7551,0.8605,1.0,0.7551,0.6202,0.5,0.8163,0.6202,0.5,0.8163,-0.0941,GlotLID,0.1462,MillID
+kas_Deva,Kashmiri,47,0.557,0.6875,0.4681,0.7027,0.963,0.5532,0.7027,0.963,0.5532,0.5867,0.7857,0.4681,0.5867,0.7857,0.4681,-0.1457,GlotLID,-0.0297,fastText
+cjk_Latn,Chokwe,24,0.8571,1.0,0.75,1.0,1.0,1.0,1.0,1.0,1.0,0.807,0.697,0.9583,0.807,0.697,0.9583,-0.1429,GlotLID,0.0501,MillID

reports/test-common/millid_per_language.csv

@@ -0,0 +1,214 @@
+label,support,tp,fp,fn,tn,precision,recall,f1,specificity,fp_per_million,sensitivity
+abk_Cyrl,285,283,0,2,64312,1.0,0.992982,0.996479,1.0,0.0,0.992982
+ace_Latn,113,109,1,4,64483,0.990909,0.964602,0.977578,0.999984,15.507723,0.964602
+ady_Cyrl,135,134,0,1,64462,1.0,0.992593,0.996283,1.0,0.0,0.992593
+afr_Latn,569,548,11,21,64017,0.980322,0.963093,0.971631,0.999828,171.799838,0.963093
+als_Latn,213,203,0,10,64384,1.0,0.953052,0.975962,1.0,0.0,0.953052
+alt_Cyrl,208,195,1,13,64388,0.994898,0.9375,0.965347,0.999984,15.530603,0.9375
+amh_Ethi,458,448,5,10,64134,0.988962,0.978166,0.983535,0.999922,77.95569,0.978166
+arb_Arab,745,575,0,170,63852,1.0,0.771812,0.871212,1.0,0.0,0.771812
+arb_Latn,163,146,1,17,64433,0.993197,0.895706,0.941935,0.999984,15.519757,0.895706
+arn_Latn,250,247,1,3,64346,0.995968,0.988,0.991968,0.999984,15.54074,0.988
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reports/test-common/millid_summary.json

@@ -0,0 +1,22 @@
+{
+  "top1_accuracy": 0.9395947180209607,
+  "top3_accuracy": 0.9856649689614069,
+  "top5_accuracy": 0.9908664489062959,
+  "passages": 64597,
+  "languages": 213,
+  "macro_precision": 0.970922775379856,
+  "macro_recall": 0.9353270477650256,
+  "macro_f1": 0.951259698841108,
+  "macro_specificity": 0.9998952436560236,
+  "weighted_f1": 0.9570662852696624,
+  "median_f1": 0.9726495726495726,
+  "languages_f1_at_least_0_9": 188,
+  "languages_f1_below_0_5": 0,
+  "mean_fp_per_million": 104.75634397647721,
+  "micro_f1": 0.9395947180209607,
+  "top1_by_size": {
+    "paragraph": 0.9725285742931622,
+    "sentence": 0.9684640840957758,
+    "short": 0.9125458547322084
+  }
+}

reports/test/fasttext@millid_per_language.csv

@@ -0,0 +1,1622 @@
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reports/test/fasttext@millid_summary.json

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+{
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+  "micro_f1": 0.2514599922577745,
+  "top1_by_size": {
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+    "sentence": 0.25056634510143305,
+    "short": 0.27637730884784273
+  }
+}

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reports/test/fasttext_summary.json

@@ -0,0 +1,22 @@
+{
+  "top1_accuracy": 0.25110201838986684,
+  "top3_accuracy": 0.2610045745730329,
+  "top5_accuracy": 0.262482257399861,
+  "passages": 240241,
+  "languages": 1621,
+  "macro_precision": 0.05202750834127428,
+  "macro_recall": 0.12048375928156996,
+  "macro_f1": 0.06567764337804372,
+  "macro_specificity": 0.9995388522643763,
+  "weighted_f1": 0.14972995387859817,
+  "median_f1": 0.0,
+  "languages_f1_at_least_0_9": 25,
+  "languages_f1_below_0_5": 1518,
+  "mean_fp_per_million": 461.14773562356646,
+  "micro_f1": 0.25110201838986684,
+  "top1_by_size": {
+    "paragraph": 0.19578968778602665,
+    "sentence": 0.2500699977091659,
+    "short": 0.2759950934205614
+  }
+}

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reports/test/glotlid@millid_summary.json

@@ -0,0 +1,22 @@
+{
+  "top1_accuracy": 0.978292631149554,
+  "top3_accuracy": 0.9977897194900122,
+  "top5_accuracy": 0.9984557173837938,
+  "passages": 240241,
+  "languages": 1621,
+  "macro_precision": 0.9778492893480586,
+  "macro_recall": 0.9764998669631992,
+  "macro_f1": 0.9754780061637239,
+  "macro_specificity": 0.9999866076235475,
+  "weighted_f1": 0.9778914257911913,
+  "median_f1": 1.0,
+  "languages_f1_at_least_0_9": 1490,
+  "languages_f1_below_0_5": 5,
+  "mean_fp_per_million": 13.392376452534537,
+  "micro_f1": 0.978292631149554,
+  "top1_by_size": {
+    "paragraph": 0.9911725821214279,
+    "sentence": 0.98578410161122,
+    "short": 0.967431690103287
+  }
+}

reports/test/glotlid_per_language.csv

@@ -0,0 +1,1622 @@
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reports/test/glotlid_summary.json

@@ -0,0 +1,22 @@
+{
+  "top1_accuracy": 0.977614145795264,
+  "top3_accuracy": 0.9977189572137978,
+  "top5_accuracy": 0.9983766301339072,
+  "passages": 240241,
+  "languages": 1621,
+  "macro_precision": 0.9782517709855841,
+  "macro_recall": 0.9751782136320077,
+  "macro_f1": 0.9748179587217549,
+  "macro_specificity": 0.9999867952157099,
+  "weighted_f1": 0.9776092036050326,
+  "median_f1": 1.0,
+  "languages_f1_at_least_0_9": 1488,
+  "languages_f1_below_0_5": 6,
+  "mean_fp_per_million": 13.204784290051938,
+  "micro_f1": 0.977614145795264,
+  "top1_by_size": {
+    "paragraph": 0.9910708837587715,
+    "sentence": 0.9852877542189529,
+    "short": 0.9663739311301133
+  }
+}

reports/test/millid_summary.json

@@ -0,0 +1,22 @@
+{
+  "top1_accuracy": 0.9587247805328816,
+  "top3_accuracy": 0.9890484971341278,
+  "top5_accuracy": 0.9932401213781161,
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+  "macro_precision": 0.9549770065582561,
+  "macro_recall": 0.9588167712047285,
+  "macro_f1": 0.9553762683202616,
+  "macro_specificity": 0.9999745397211366,
+  "weighted_f1": 0.9589133936122667,
+  "median_f1": 0.9824561403508771,
+  "languages_f1_at_least_0_9": 1446,
+  "languages_f1_below_0_5": 8,
+  "mean_fp_per_million": 25.46027886332467,
+  "micro_f1": 0.9587247805328816,
+  "top1_by_size": {
+    "paragraph": 0.9832807891792942,
+    "sentence": 0.9797006643418943,
+    "short": 0.933343407228316
+  }
+}

reports/udhr-all/fasttext@millid_per_language.csv

@@ -0,0 +1,430 @@
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reports/udhr-all/fasttext@millid_summary.json

@@ -0,0 +1,17 @@
+{
+  "top1_accuracy": 0.39644499149259677,
+  "top3_accuracy": 0.4159577163957572,
+  "top5_accuracy": 0.42004851029938817,
+  "passages": 27623,
+  "languages": 429,
+  "macro_precision": 0.2478791005299098,
+  "macro_recall": 0.3741155533322177,
+  "macro_f1": 0.27967804305179117,
+  "macro_specificity": 0.998819804723614,
+  "weighted_f1": 0.3054055953686982,
+  "median_f1": 0.0,
+  "languages_f1_at_least_0_9": 55,
+  "languages_f1_below_0_5": 299,
+  "mean_fp_per_million": 1180.1952763859204,
+  "micro_f1": 0.39644499149259677
+}

reports/udhr-all/fasttext_per_language.csv

@@ -0,0 +1,430 @@
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reports/udhr-all/fasttext_summary.json

@@ -0,0 +1,17 @@
+{
+  "top1_accuracy": 0.3961191760489447,
+  "top3_accuracy": 0.4159577163957572,
+  "top5_accuracy": 0.4199761068674655,
+  "passages": 27623,
+  "languages": 429,
+  "macro_precision": 0.24791077144530446,
+  "macro_recall": 0.3737409279575923,
+  "macro_f1": 0.279501714390051,
+  "macro_specificity": 0.9988210740543896,
+  "weighted_f1": 0.30525958931365826,
+  "median_f1": 0.0,
+  "languages_f1_at_least_0_9": 54,
+  "languages_f1_below_0_5": 299,
+  "mean_fp_per_million": 1178.9259456102943,
+  "micro_f1": 0.3961191760489447
+}

reports/udhr-all/glotlid@millid_per_language.csv

@@ -0,0 +1,430 @@
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+sid_Latn,57,57,11,0,27555,0.838235,1.0,0.912,0.999601,399.042298,1.0
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reports/udhr-all/glotlid@millid_summary.json

@@ -0,0 +1,17 @@
+{
+  "top1_accuracy": 0.7378271730080006,
+  "top3_accuracy": 0.7960395322738297,
+  "top5_accuracy": 0.8079498968251095,
+  "passages": 27623,
+  "languages": 429,
+  "macro_precision": 0.7425329775774382,
+  "macro_recall": 0.7380568437901042,
+  "macro_f1": 0.724506747472521,
+  "macro_specificity": 0.999809579502659,
+  "weighted_f1": 0.7305913692421664,
+  "median_f1": 0.9747899159663865,
+  "languages_f1_at_least_0_9": 272,
+  "languages_f1_below_0_5": 105,
+  "mean_fp_per_million": 190.42049734090864,
+  "micro_f1": 0.7378271730080006
+}

reports/udhr-all/glotlid_per_language.csv

@@ -0,0 +1,430 @@
+label,support,tp,fp,fn,tn,precision,recall,f1,specificity,fp_per_million,sensitivity
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+yor_Latn,57,57,2,0,27564,0.966102,1.0,0.982759,0.999927,72.553145,1.0
+yrk_Cyrl,55,52,1,3,27567,0.981132,0.945455,0.962963,0.999964,36.273941,0.945455
+yua_Latn,59,59,0,0,27564,1.0,1.0,1.0,1.0,0.0,1.0
+yue_Hani,49,20,0,29,27574,1.0,0.408163,0.57971,1.0,0.0,0.408163
+zam_Latn,60,0,5,60,27558,0.0,0.0,0.0,0.999819,181.402605,0.0
+zdj_Latn,50,48,1,2,27572,0.979592,0.96,0.969697,0.999964,36.267363,0.96
+zgh_Tfng,60,18,0,42,27563,1.0,0.3,0.461538,1.0,0.0,0.3
+zlm_Arab,24,0,0,24,27599,0.0,0.0,0.0,1.0,0.0,0.0
+zlm_Latn,58,0,0,58,27565,0.0,0.0,0.0,1.0,0.0,0.0
+zro_Latn,56,0,0,56,27567,0.0,0.0,0.0,1.0,0.0,0.0
+ztu_Latn,58,0,0,58,27565,0.0,0.0,0.0,1.0,0.0,0.0
+zul_Latn,58,57,2,1,27563,0.966102,0.982759,0.974359,0.999927,72.555777,0.982759
+zyb_Latn,57,57,2,0,27564,0.966102,1.0,0.982759,0.999927,72.553145,1.0

reports/udhr-all/glotlid_summary.json

@@ -0,0 +1,17 @@
+{
+  "top1_accuracy": 0.750244361582739,
+  "top3_accuracy": 0.8144662056981501,
+  "top5_accuracy": 0.8281866560474966,
+  "passages": 27623,
+  "languages": 429,
+  "macro_precision": 0.7694575973118034,
+  "macro_recall": 0.7529055089081532,
+  "macro_f1": 0.7454327849166208,
+  "macro_specificity": 0.9998410395298513,
+  "weighted_f1": 0.7485822543592835,
+  "median_f1": 0.9795918367346939,
+  "languages_f1_at_least_0_9": 277,
+  "languages_f1_below_0_5": 97,
+  "mean_fp_per_million": 158.96047014874904,
+  "micro_f1": 0.750244361582739
+}

reports/udhr-all/leaderboard.csv

@@ -0,0 +1,430 @@
+label,language,support,millid_f1,millid_precision,millid_recall,glotlid_f1,glotlid_precision,glotlid_recall,glotlid@millid_f1,glotlid@millid_precision,glotlid@millid_recall,fasttext_f1,fasttext_precision,fasttext_recall,fasttext@millid_f1,fasttext@millid_precision,fasttext@millid_recall,delta,winner,delta_fasttext,winner_fasttext
+roh_Latn,Romansh,399,0.9925,0.995,0.99,0.9987,0.9975,1.0,0.9987,0.9975,1.0,0.941,0.8886,1.0,0.941,0.8886,1.0,-0.0063,tie,0.0514,MillID
+oci_Latn,Occitan,398,0.2747,0.9412,0.1608,0.3755,1.0,0.2312,0.3755,1.0,0.2312,0.4276,0.4856,0.3819,0.4276,0.4856,0.3819,-0.1008,GlotLID,-0.1529,fastText
+cmn_Hani,Chinese (Mandarin),319,0.7876,0.6681,0.9592,0.7446,0.6261,0.9185,0.7446,0.6261,0.9185,0.4125,0.4612,0.373,0.4125,0.4612,0.373,0.043,MillID,0.3752,MillID
+krl_Latn,Karelian,183,0.0,0.0,0.0,0.0109,1.0,0.0055,0.0109,1.0,0.0055,0.0,0.0,0.0,0.0,0.0,0.0,-0.0109,GlotLID,0.0,tie
+qug_Latn,Chimborazo Highland Quichua,182,0.0109,1.0,0.0055,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0109,MillID,0.0109,MillID
+acu_Latn,Achuar-Shiwiar,180,0.8254,0.963,0.7222,0.8535,1.0,0.7444,0.8535,1.0,0.7444,0.0,0.0,0.0,0.0,0.0,0.0,-0.0281,GlotLID,0.8254,MillID
+hus_Latn,Huastec,175,0.9766,1.0,0.9543,0.9855,1.0,0.9714,0.9855,1.0,0.9714,0.0,0.0,0.0,0.0,0.0,0.0,-0.0089,tie,0.9766,MillID
+hau_Latn,Hausa,155,0.984,0.9747,0.9935,0.9968,0.9936,1.0,0.9968,0.9936,1.0,0.854,0.7452,1.0,0.854,0.7452,1.0,-0.0128,GlotLID,0.13,MillID
+auc_Latn,Waorani,154,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+kwi_Latn,Awa-Cuaiquer,145,0.8803,1.0,0.7862,0.8769,0.9913,0.7862,0.8769,0.9913,0.7862,0.0,0.0,0.0,0.0,0.0,0.0,0.0034,tie,0.8803,MillID
+lot_Latn,lot_Latn,140,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+jpn_Jpan,Japanese,140,1.0,1.0,1.0,0.9894,0.979,1.0,0.9859,0.9722,1.0,0.9024,0.8535,0.9571,0.9024,0.8535,0.9571,0.0141,MillID,0.0976,MillID
+snn_Latn,Siona,136,0.6305,0.9552,0.4706,0.5137,1.0,0.3456,0.5137,1.0,0.3456,0.0,0.0,0.0,0.0,0.0,0.0,0.1169,MillID,0.6305,MillID
+lat_Latn,Latin,118,0.9915,0.9915,0.9915,0.9872,0.9915,0.9831,0.9872,0.9915,0.9831,0.0,0.0,0.0,0.0,0.0,0.0,0.0043,tie,0.9915,MillID
+ktu_Latn,Kituba (Democratic Republic of Congo),118,0.7653,0.6667,0.8983,0.6803,0.6587,0.7034,0.6803,0.6587,0.7034,0.0,0.0,0.0,0.0,0.0,0.0,0.085,MillID,0.7653,MillID
+hat_Latn,Haitian Creole,117,0.9915,0.9915,0.9915,0.9785,0.9828,0.9744,0.9785,0.9828,0.9744,0.6988,0.5395,0.9915,0.6988,0.5395,0.9915,0.0129,MillID,0.2927,MillID
+nya_Latn,Nyanja,116,0.9913,1.0,0.9828,1.0,1.0,1.0,1.0,1.0,1.0,0.7891,0.6517,1.0,0.7891,0.6517,1.0,-0.0087,tie,0.2022,MillID
+twi_Latn,Twi,116,0.9748,0.9508,1.0,0.9957,0.9915,1.0,0.9957,0.9915,1.0,0.3694,0.2266,1.0,0.3665,0.2244,1.0,-0.0209,GlotLID,0.6083,MillID
+kmb_Latn,Kimbundu,116,0.9702,0.958,0.9828,1.0,1.0,1.0,1.0,1.0,1.0,0.8605,0.7817,0.9569,0.8605,0.7817,0.9569,-0.0298,GlotLID,0.1097,MillID
+rmn_Latn,Balkan Romani,116,0.678,0.9836,0.5172,0.8571,1.0,0.75,0.8571,1.0,0.75,0.0,0.0,0.0,0.0,0.0,0.0,-0.1792,GlotLID,0.678,MillID
+kng_Latn,Koongo,115,0.0758,0.2941,0.0435,0.1928,0.3137,0.1391,0.1928,0.3137,0.1391,0.4514,0.3529,0.6261,0.4514,0.3529,0.6261,-0.117,GlotLID,-0.3757,fastText
+pov_Latn,Upper Guinea Crioulo,115,0.8205,1.0,0.6957,0.8325,1.0,0.713,0.8325,1.0,0.713,0.0,0.0,0.0,0.0,0.0,0.0,-0.012,GlotLID,0.8205,MillID
+chr_Cher,Cherokee,115,0.6705,1.0,0.5043,0.6628,1.0,0.4957,0.6628,1.0,0.4957,0.0,0.0,0.0,0.0,0.0,0.0,0.0077,tie,0.6705,MillID
+lvs_Latn,Standard Latvian,115,0.9868,1.0,0.9739,1.0,1.0,1.0,1.0,1.0,1.0,0.9914,0.9829,1.0,0.9914,0.9829,1.0,-0.0132,GlotLID,-0.0046,tie
+ell_Grek,Greek,115,0.6705,1.0,0.5043,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.3295,GlotLID,-0.3295,fastText
+por_Latn,Portuguese,115,0.9554,0.9817,0.9304,0.9871,0.9746,1.0,0.9237,0.8582,1.0,0.7797,0.6389,1.0,0.7797,0.6389,1.0,0.0317,MillID,0.1757,MillID
+fas_Arab,Persian,114,0.9912,1.0,0.9825,1.0,1.0,1.0,1.0,1.0,1.0,0.8976,0.8143,1.0,0.8976,0.8143,1.0,-0.0088,tie,0.0935,MillID
+tso_Latn,Tsonga,114,0.8557,0.9885,0.7544,0.7807,1.0,0.6404,0.7807,1.0,0.6404,0.659,0.4914,1.0,0.659,0.4914,1.0,0.075,MillID,0.1968,MillID
+tha_Thai,Thai,114,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9956,0.9913,1.0,0.9956,0.9913,1.0,0.0,tie,0.0044,tie
+cjk_Latn,Chokwe,114,0.867,0.9888,0.7719,1.0,1.0,1.0,1.0,1.0,1.0,0.5301,0.3654,0.9649,0.5301,0.3654,0.9649,-0.133,GlotLID,0.3369,MillID
+lin_Latn,Lingala,110,0.991,0.9821,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.982,0.9732,0.9909,0.982,0.9732,0.9909,-0.009,tie,0.009,tie
+urd_Arab,Urdu,108,0.9907,1.0,0.9815,0.986,0.9907,0.9815,0.986,0.9907,0.9815,0.6353,0.4655,1.0,0.6353,0.4655,1.0,0.0046,tie,0.3554,MillID
+fuv_Latn,Nigerian Fulfulde,108,0.6364,0.8235,0.5185,0.6813,0.8378,0.5741,0.6848,0.8289,0.5833,0.3582,0.2182,1.0,0.3582,0.2182,1.0,-0.0484,GlotLID,0.2782,MillID
+umb_Latn,Umbundu,107,0.9662,1.0,0.9346,0.9906,1.0,0.9813,0.9906,1.0,0.9813,0.9004,0.8387,0.972,0.9004,0.8387,0.972,-0.0244,GlotLID,0.0658,MillID
+mal_Mlym,Malayalam,100,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+ron_Latn,Romanian,97,0.9949,0.9898,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.8727,0.7805,0.9897,0.8727,0.7805,0.9897,-0.0051,tie,0.1221,MillID
+ven_Latn,Venda,89,0.9944,0.9889,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0056,tie,0.9944,MillID
+spa_Latn,Spanish,78,0.9677,0.974,0.9615,0.9809,0.9747,0.9872,0.9625,0.939,0.9872,0.5493,0.3786,1.0,0.5493,0.3786,1.0,0.0052,tie,0.4184,MillID
+hye_Armn,Armenian,70,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9855,1.0,0.9714,0.9855,1.0,0.9714,0.0,tie,0.0145,MillID
+tam_Taml,Tamil,68,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+tiv_Latn,Tiv,67,1.0,1.0,1.0,1.0,1.0,1.0,0.9853,0.971,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0147,MillID,1.0,MillID
+deu_Latn,German,67,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9571,0.9178,1.0,0.9571,0.9178,1.0,0.0,tie,0.0429,MillID
+ibb_Latn,ibb_Latn,67,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+sun_Latn,Sundanese,66,0.9924,1.0,0.9848,0.9924,1.0,0.9848,0.9924,1.0,0.9848,0.7263,0.5752,0.9848,0.7263,0.5752,0.9848,0.0,tie,0.2661,MillID
+pam_Latn,Pampanga,66,0.9778,0.9565,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0222,GlotLID,0.9778,MillID
+njo_Latn,Ao Naga,64,1.0,1.0,1.0,0.9844,0.9844,0.9844,0.9844,0.9844,0.9844,0.0,0.0,0.0,0.0,0.0,0.0,0.0156,MillID,1.0,MillID
+knc_Latn,Central Kanuri,64,0.9697,0.9412,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.3891,0.2415,1.0,0.3891,0.2415,1.0,-0.0303,GlotLID,0.5806,MillID
+skr_Arab,Saraiki,63,0.8673,0.98,0.7778,0.5,1.0,0.3333,0.5,1.0,0.3333,0.0,0.0,0.0,0.0,0.0,0.0,0.3673,MillID,0.8673,MillID
+smo_Latn,Samoan,63,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.7871,0.663,0.9683,0.7871,0.663,0.9683,0.0,tie,0.2129,MillID
+mcf_Latn,Matsés,62,1.0,1.0,1.0,0.992,0.9841,1.0,0.992,0.9841,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.008,tie,1.0,MillID
+shp_Latn,Shipibo-Conibo,62,0.4691,1.0,0.3065,0.9123,1.0,0.8387,0.9123,1.0,0.8387,0.0,0.0,0.0,0.0,0.0,0.0,-0.4431,GlotLID,0.4691,MillID
+cbi_Latn,Chachi,62,0.9688,0.9394,1.0,0.9839,0.9839,0.9839,0.976,0.9683,0.9839,0.0,0.0,0.0,0.0,0.0,0.0,-0.0072,tie,0.9688,MillID
+nob_Latn,Norwegian Bokmål,62,0.9134,0.8923,0.9355,0.9764,0.9538,1.0,0.9764,0.9538,1.0,0.9841,0.9688,1.0,0.9841,0.9688,1.0,-0.063,GlotLID,-0.0707,fastText
+gla_Latn,Scottish Gaelic,62,0.9919,1.0,0.9839,0.9836,1.0,0.9677,0.9836,1.0,0.9677,0.9508,0.9667,0.9355,0.9508,0.9667,0.9355,0.0083,tie,0.0411,MillID
+swe_Latn,Swedish,61,0.9917,1.0,0.9836,1.0,1.0,1.0,0.9919,0.9839,1.0,0.9839,0.9683,1.0,0.9839,0.9683,1.0,-0.0001,tie,0.0079,tie
+sey_Latn,Secoya,61,0.5799,0.4537,0.8033,0.5138,0.3567,0.918,0.5249,0.3625,0.9508,0.0,0.0,0.0,0.0,0.0,0.0,0.055,MillID,0.5799,MillID
+fin_Latn,Finnish,61,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.2742,0.1589,1.0,0.2742,0.1589,1.0,0.0,tie,0.7258,MillID
+pnb_Arab,Western Panjabi,61,0.8824,0.8,0.9836,0.7394,0.5865,1.0,0.7394,0.5865,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.143,MillID,0.8824,MillID
+amr_Latn,Amarakaeri,61,0.976,0.9531,1.0,0.976,0.9531,1.0,0.976,0.9531,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.976,MillID
+ace_Latn,Acehnese,61,1.0,1.0,1.0,0.9917,1.0,0.9836,0.9917,1.0,0.9836,0.48,0.3175,0.9836,0.48,0.3175,0.9836,0.0083,tie,0.52,MillID
+dan_Latn,Danish,61,0.9431,0.9355,0.9508,0.9833,1.0,0.9672,0.9833,1.0,0.9672,0.96,0.9375,0.9836,0.96,0.9375,0.9836,-0.0402,GlotLID,-0.0169,fastText
+boa_Latn,Bora,61,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+vai_Vaii,vai_Vaii,60,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+wol_Latn,Wolof,60,0.8511,0.7407,1.0,0.9836,0.9677,1.0,0.9836,0.9677,1.0,0.208,0.1161,1.0,0.208,0.1161,1.0,-0.1325,GlotLID,0.6431,MillID
+fil_Latn,Filipino,60,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.553,0.3822,1.0,0.553,0.3822,1.0,0.0,tie,0.447,MillID
+mad_Latn,Madurese,60,0.6556,0.4917,0.9833,0.9508,0.9355,0.9667,0.9355,0.9062,0.9667,0.0,0.0,0.0,0.0,0.0,0.0,-0.2799,GlotLID,0.6556,MillID
+war_Latn,Waray,60,0.9836,0.9677,1.0,0.9917,0.9836,1.0,0.9917,0.9836,1.0,0.9365,0.8939,0.9833,0.9365,0.8939,0.9833,-0.0081,tie,0.0471,MillID
+ban_Latn,Balinese,60,0.9661,0.9828,0.95,1.0,1.0,1.0,1.0,1.0,1.0,0.5381,0.3681,1.0,0.5381,0.3681,1.0,-0.0339,GlotLID,0.428,MillID
+tca_Latn,Ticuna,60,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+ton_Latn,Tongan,60,1.0,1.0,1.0,0.9917,0.9836,1.0,0.9917,0.9836,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0083,tie,1.0,MillID
+zam_Latn,Miahuatlán Zapotec,60,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+zgh_Tfng,Standard Moroccan Tamazight,60,0.75,1.0,0.6,0.4615,1.0,0.3,0.4615,1.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.2885,MillID,0.75,MillID
+tbz_Latn,Ditammari,60,0.0,0.0,0.0,0.3784,1.0,0.2333,0.3784,1.0,0.2333,0.0,0.0,0.0,0.0,0.0,0.0,-0.3784,GlotLID,0.0,tie
+cab_Latn,Garifuna,60,1.0,1.0,1.0,0.9831,1.0,0.9667,0.9831,1.0,0.9667,0.0,0.0,0.0,0.0,0.0,0.0,0.0169,MillID,1.0,MillID
+cic_Latn,cic_Latn,60,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+cbr_Latn,Cashibo-Cacataibo,60,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+bod_Tibt,Tibetan,60,0.9748,0.9831,0.9667,0.9009,0.9804,0.8333,0.9009,0.9804,0.8333,0.8992,0.8406,0.9667,0.8992,0.8406,0.9667,0.0739,MillID,0.0756,MillID
+bug_Latn,Buginese,60,0.9298,0.9815,0.8833,0.9917,0.9836,1.0,0.9917,0.9836,1.0,0.566,0.3947,1.0,0.566,0.3947,1.0,-0.0619,GlotLID,0.3638,MillID
+sna_Latn,Shona,60,0.9756,0.9524,1.0,0.9917,0.9836,1.0,0.9917,0.9836,1.0,0.5455,0.3826,0.95,0.5455,0.3826,0.95,-0.0161,GlotLID,0.4302,MillID
+jav_Java,jav_Java,60,0.0,0.0,0.0,0.9744,1.0,0.95,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+afr_Latn,Afrikaans,60,0.9916,1.0,0.9833,1.0,1.0,1.0,1.0,1.0,1.0,0.944,0.9077,0.9833,0.944,0.9077,0.9833,-0.0084,tie,0.0476,MillID
+arl_Latn,Arabela,60,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+udu_Latn,Uduk,60,0.9836,0.9677,1.0,0.9836,0.9677,1.0,0.9302,0.8696,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0534,MillID,0.9836,MillID
+vie_Latn,Vietnamese,60,0.9917,0.9836,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9302,0.8696,1.0,0.9302,0.8696,1.0,-0.0083,tie,0.0615,MillID
+mcd_Latn,Sharanahua,60,1.0,1.0,1.0,0.9836,0.9677,1.0,0.9836,0.9677,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0164,MillID,1.0,MillID
+jav_Latn,Javanese,60,0.9833,0.9833,0.9833,0.9916,1.0,0.9833,0.9916,1.0,0.9833,0.4739,0.3122,0.9833,0.4739,0.3122,0.9833,-0.0083,tie,0.5094,MillID
+jiv_Latn,Shuar,60,0.8,0.6824,0.9667,0.7973,0.6705,0.9833,0.7973,0.6705,0.9833,0.0,0.0,0.0,0.0,0.0,0.0,0.0027,tie,0.8,MillID
+gjn_Latn,Gonja,60,0.9833,0.9833,0.9833,0.928,0.8923,0.9667,0.928,0.8923,0.9667,0.0,0.0,0.0,0.0,0.0,0.0,0.0553,MillID,0.9833,MillID
+gaz_Latn,Oromo (West Central),60,0.912,0.8769,0.95,0.9752,0.9672,0.9833,0.9752,0.9672,0.9833,0.4762,0.3125,1.0,0.4762,0.3125,1.0,-0.0632,GlotLID,0.4358,MillID
+quy_Latn,Ayacucho Quechua,60,0.6554,0.4957,0.9667,0.7712,0.6344,0.9833,0.7712,0.6344,0.9833,0.0909,0.0477,0.9833,0.0909,0.0477,0.9833,-0.1159,GlotLID,0.5645,MillID
+qvm_Latn,Margos-Yarowilca-Lauricocha Quechua,60,0.3562,1.0,0.2167,0.5,1.0,0.3333,0.5,1.0,0.3333,0.0,0.0,0.0,0.0,0.0,0.0,-0.1438,GlotLID,0.3562,MillID
+ctd_Latn,Tedim Chin,60,0.6047,1.0,0.4333,0.6957,1.0,0.5333,0.7234,1.0,0.5667,0.0,0.0,0.0,0.0,0.0,0.0,-0.1188,GlotLID,0.6047,MillID
+cot_Latn,Caquinte,60,0.9916,1.0,0.9833,0.9916,1.0,0.9833,0.9916,1.0,0.9833,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.9916,MillID
+cym_Latn,Welsh,60,0.9917,0.9836,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.8,0.6667,1.0,0.8,0.6667,1.0,-0.0083,tie,0.1917,MillID
+cpu_Latn,Pichis Ashéninka,60,0.7292,0.9722,0.5833,0.9744,1.0,0.95,0.9744,1.0,0.95,0.0,0.0,0.0,0.0,0.0,0.0,-0.2452,GlotLID,0.7292,MillID
+ekk_Latn,Estonian,60,0.9917,0.9836,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.3343,0.2007,1.0,0.3343,0.2007,1.0,-0.0083,tie,0.6575,MillID
+ind_Latn,Indonesian,60,0.7308,0.8636,0.6333,0.8947,0.9444,0.85,0.8947,0.9444,0.85,0.5279,0.3796,0.8667,0.5279,0.3796,0.8667,-0.164,GlotLID,0.2029,MillID
+tuk_Latn,Turkmen,59,0.9828,1.0,0.9661,1.0,1.0,1.0,0.9916,0.9833,1.0,0.9672,0.9365,1.0,0.9672,0.9365,1.0,-0.0088,tie,0.0155,MillID
+tsz_Latn,Purepecha,59,0.8175,0.7179,0.9492,0.8889,0.8358,0.9492,0.8976,0.8382,0.9661,0.0,0.0,0.0,0.0,0.0,0.0,-0.0801,GlotLID,0.8175,MillID
+yua_Latn,Yucateco,59,0.9833,0.9672,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0167,GlotLID,0.9833,MillID
+tur_Latn,Turkish,59,0.9516,0.9077,1.0,0.7712,0.6277,1.0,0.7662,0.6211,1.0,0.4797,0.3155,1.0,0.4797,0.3155,1.0,0.1854,MillID,0.4719,MillID
+ssw_Latn,Swati,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.8252,0.7024,1.0,0.8252,0.7024,1.0,0.0,tie,0.1748,MillID
+srp_Latn,Serbian,59,0.3486,0.38,0.322,0.45,0.8571,0.3051,0.45,0.8571,0.3051,0.0,0.0,0.0,0.0,0.0,0.0,-0.1014,GlotLID,0.3486,MillID
+bos_Cyrl,bos_Cyrl,59,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+ceb_Latn,Cebuano,59,0.9916,0.9833,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.6114,0.4403,1.0,0.6114,0.4403,1.0,-0.0084,tie,0.3802,MillID
+bre_Latn,Breton,59,0.9831,0.9831,0.9831,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.0,0.0,0.0,0.0,0.0,0.0,-0.0084,tie,0.9831,MillID
+ccp_Cakm,ccp_Cakm,59,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+chj_Latn,chj_Latn,59,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+bis_Latn,Bislama,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.7516,0.602,1.0,0.7516,0.602,1.0,0.0,tie,0.2484,MillID
+top_Latn,Papantla Totonac,59,0.958,0.95,0.9661,0.9916,0.9833,1.0,0.9916,0.9833,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0336,GlotLID,0.958,MillID
+tly_Latn,Talysh,59,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.9915,MillID
+tob_Latn,Toba,59,0.9833,0.9672,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0167,GlotLID,0.9833,MillID
+slv_Latn,Slovenian,59,0.9661,0.9661,0.9661,0.9752,0.9516,1.0,0.9752,0.9516,1.0,0.7662,0.6211,1.0,0.7662,0.6211,1.0,-0.0091,tie,0.1999,MillID
+src_Latn,src_Latn,59,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+srp_Cyrl,Serbian,59,0.6629,0.5,0.9831,0.6629,0.5,0.9831,0.6629,0.5,0.9831,0.6629,0.5,0.9831,0.6629,0.5,0.9831,0.0,tie,0.0,tie
+sin_Sinh,Sinhala,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+bos_Latn,Bosnian,59,0.2,0.1831,0.2203,0.3121,0.2368,0.4576,0.3121,0.2368,0.4576,0.1951,0.1228,0.4746,0.1951,0.1228,0.4746,-0.1121,GlotLID,0.0049,tie
+hun_Latn,Hungarian,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.2914,0.1705,1.0,0.2914,0.1705,1.0,0.0,tie,0.7086,MillID
+oki_Latn,oki_Latn,59,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+orh_Latn,orh_Latn,59,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+ote_Latn,Mezquital Otomi,59,0.1408,0.4167,0.0847,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1408,MillID,0.1408,MillID
+gaa_Latn,Ga,59,0.9672,0.9365,1.0,0.9752,0.9516,1.0,0.9672,0.9365,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.9672,MillID
+gld_Cyrl,gld_Cyrl,59,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+fij_Latn,Fijian,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9752,0.9516,1.0,0.9752,0.9516,1.0,0.0,tie,0.0248,MillID
+eus_Latn,Basque,59,0.9147,0.8429,1.0,0.9916,0.9833,1.0,0.9916,0.9833,1.0,0.8788,0.7945,0.9831,0.8788,0.7945,0.9831,-0.0769,GlotLID,0.0359,MillID
+ewe_Latn,Ewe,59,0.9291,0.8676,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.4813,0.3187,0.9831,0.4813,0.3187,0.9831,-0.0709,GlotLID,0.4478,MillID
+pbu_Arab,pbu_Arab,59,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+not_Latn,Nomatsiguenga,59,0.9831,0.9831,0.9831,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.0,0.0,0.0,0.0,0.0,0.0,-0.0084,tie,0.9831,MillID
+fur_Latn,Friulian,59,0.9667,0.9508,0.9831,1.0,1.0,1.0,1.0,1.0,1.0,0.6082,0.437,1.0,0.6051,0.4338,1.0,-0.0333,GlotLID,0.3615,MillID
+gag_Latn,Gagauz,59,0.9744,0.9828,0.9661,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.0,0.0,0.0,0.0,0.0,0.0,-0.0171,GlotLID,0.9744,MillID
+qwh_Latn,Huaylas Ancash Quechua,59,0.494,0.3832,0.6949,0.6545,0.7059,0.6102,0.6545,0.7059,0.6102,0.0,0.0,0.0,0.0,0.0,0.0,-0.1606,GlotLID,0.494,MillID
+cnr_Latn,Serbian (Montenegro),59,0.0,0.0,0.0,0.0303,0.1429,0.0169,0.0303,0.1429,0.0169,0.0,0.0,0.0,0.0,0.0,0.0,-0.0303,GlotLID,0.0,tie
+crh_Latn,Crimean Tatar,59,0.9833,0.9672,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.8082,0.6782,1.0,0.8082,0.6782,1.0,-0.0167,GlotLID,0.1751,MillID
+dga_Latn,Southern Dagaare,59,0.9833,0.9672,1.0,0.9915,1.0,0.9831,0.9916,0.9833,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0083,tie,0.9833,MillID
+dzo_Tibt,Dzongkha,59,0.9748,0.9667,0.9831,0.9134,0.8529,0.9831,0.9134,0.8529,0.9831,0.8571,0.9057,0.8136,0.8571,0.9057,0.8136,0.0614,MillID,0.1176,MillID
+cjs_Cyrl,Shor,59,0.9474,0.9818,0.9153,0.9369,1.0,0.8814,0.9369,1.0,0.8814,0.0,0.0,0.0,0.0,0.0,0.0,0.0104,MillID,0.9474,MillID
+cos_Latn,Corsican,59,0.9916,0.9833,1.0,0.9831,0.9831,0.9831,0.9831,0.9831,0.9831,0.0,0.0,0.0,0.0,0.0,0.0,0.0085,tie,0.9916,MillID
+cof_Latn,Colorado,59,1.0,1.0,1.0,0.9273,1.0,0.8644,0.9369,1.0,0.8814,0.0,0.0,0.0,0.0,0.0,0.0,0.0631,MillID,1.0,MillID
+qva_Latn,Ambo-Pasco Quechua,59,0.0,0.0,0.0,0.0323,0.3333,0.0169,0.0323,0.3333,0.0169,0.0,0.0,0.0,0.0,0.0,0.0,-0.0323,GlotLID,0.0,tie
+sag_Latn,Sango,59,0.9831,0.9831,0.9831,0.5783,1.0,0.4068,0.5783,1.0,0.4068,0.1795,0.3684,0.1186,0.1795,0.3684,0.1186,0.4047,MillID,0.8036,MillID
+ppl_Latn,ppl_Latn,59,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+rar_Latn,Rarotongan,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+pol_Latn,Polish,59,0.9833,0.9672,1.0,1.0,1.0,1.0,0.5463,0.3758,1.0,0.6946,0.537,0.9831,0.6946,0.537,0.9831,0.437,MillID,0.2887,MillID
+nzi_Latn,Nzima,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+hsb_Latn,Upper Sorbian,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+hns_Latn,Caribbean Hindustani,59,0.9273,1.0,0.8644,0.9369,1.0,0.8814,0.9369,1.0,0.8814,0.0,0.0,0.0,0.0,0.0,0.0,-0.0097,tie,0.9273,MillID
+hms_Latn,hms_Latn,59,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+hin_Deva,Hindi,59,0.7234,0.9714,0.5763,0.9573,0.9655,0.9492,0.9573,0.9655,0.9492,0.9752,0.9516,1.0,0.9752,0.9516,1.0,-0.2339,GlotLID,-0.2518,fastText
+ltz_Latn,Luxembourgish,59,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.6304,0.464,0.9831,0.6304,0.464,0.9831,0.0,tie,0.361,MillID
+mar_Deva,Marathi,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+lld_Latn,Ladin,59,0.9009,0.9615,0.8475,0.7312,1.0,0.5763,0.7234,0.9714,0.5763,0.0,0.0,0.0,0.0,0.0,0.0,0.1775,MillID,0.9009,MillID
+gsw_Latn,Swiss German,59,0.8462,0.9778,0.7458,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.0,0.0,0.0,0.0,0.0,0.0,-0.1453,GlotLID,0.8462,MillID
+maz_Latn,Central Mazahua,59,0.7389,0.5918,0.9831,0.9153,0.9153,0.9153,0.9076,0.9,0.9153,0.0,0.0,0.0,0.0,0.0,0.0,-0.1687,GlotLID,0.7389,MillID
+mor_Latn,Moro,59,1.0,1.0,1.0,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.0,0.0,0.0,0.0,0.0,0.0,0.0085,tie,1.0,MillID
+mlt_Latn,Maltese,59,0.9916,0.9833,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.8676,0.7662,1.0,0.8676,0.7662,1.0,-0.0084,tie,0.1239,MillID
+nba_Latn,Nyemba,59,0.0328,0.5,0.0169,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0328,MillID,0.0328,MillID
+nav_Latn,Navajo,59,0.9916,0.9833,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.5488,0.3782,1.0,0.5488,0.3782,1.0,-0.0084,tie,0.4428,MillID
+men_Latn,Mende,59,0.9593,0.9219,1.0,0.9916,0.9833,1.0,0.9833,0.9672,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.024,GlotLID,0.9593,MillID
+mic_Latn,Mi'kmaw,59,0.9748,0.9667,0.9831,0.4267,1.0,0.2712,0.4267,1.0,0.2712,0.0,0.0,0.0,0.0,0.0,0.0,0.5481,MillID,0.9748,MillID
+min_Latn,Minangkabau,59,0.9916,0.9833,1.0,0.9516,0.9077,1.0,0.9516,0.9077,1.0,0.127,1.0,0.0678,0.127,1.0,0.0678,0.04,MillID,0.8646,MillID
+kbp_Latn,Kabiyè,59,0.6821,0.5175,1.0,1.0,1.0,1.0,0.9916,0.9833,1.0,0.2915,0.1711,0.9831,0.2915,0.1711,0.9831,-0.3095,GlotLID,0.3906,MillID
+kin_Latn,Kinyarwanda,59,0.7961,0.9318,0.6949,0.8868,1.0,0.7966,0.8868,1.0,0.7966,0.8095,0.7612,0.8644,0.8095,0.7612,0.8644,-0.0907,GlotLID,-0.0134,fastText
+khm_Khmr,Khmer,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+kqn_Latn,Kaonde,59,0.9748,0.9667,0.9831,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0252,GlotLID,0.9748,MillID
+kdh_Latn,Tem,59,0.9916,0.9833,1.0,0.8654,1.0,0.7627,0.9915,1.0,0.9831,0.0,0.0,0.0,0.0,0.0,0.0,0.0001,tie,0.9916,MillID
+wln_Latn,Walloon,59,0.6118,0.4685,0.8814,0.655,0.5,0.9492,0.655,0.5,0.9492,0.0,0.0,0.0,0.0,0.0,0.0,-0.0432,GlotLID,0.6118,MillID
+kbr_Latn,Kafa,59,1.0,1.0,1.0,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.0,0.0,0.0,0.0,0.0,0.0,0.0085,tie,1.0,MillID
+ydd_Hebr,Eastern Yiddish,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+xsm_Latn,Kasem,59,0.816,0.7727,0.8644,0.9355,0.8923,0.9831,0.716,0.5631,0.9831,0.0,0.0,0.0,0.0,0.0,0.0,0.1,MillID,0.816,MillID
+ayr_Latn,Central Aymara,59,0.9833,0.9672,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.7117,0.5577,0.9831,0.7117,0.5577,0.9831,-0.0167,GlotLID,0.2717,MillID
+uig_Latn,Uyghur,59,0.7152,0.5566,1.0,0.8194,0.6941,1.0,0.7919,0.6556,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0768,GlotLID,0.7152,MillID
+bcl_Latn,Central Bikol,59,0.9915,1.0,0.9831,0.9916,0.9833,1.0,0.9916,0.9833,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0001,tie,0.9915,MillID
+ame_Latn,Yanesha',59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+ada_Latn,Adangme,59,0.9752,0.9516,1.0,0.9365,0.8806,1.0,0.9365,0.8806,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0387,MillID,0.9752,MillID
+ajg_Latn,Aja (Benin),59,0.6519,0.4836,1.0,0.6667,0.5,1.0,0.6629,0.4958,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.011,GlotLID,0.6519,MillID
+als_Latn,Albanian (Tosk),59,0.9915,1.0,0.9831,0.9833,0.9672,1.0,0.9833,0.9672,1.0,0.596,0.4245,1.0,0.596,0.4245,1.0,0.0081,tie,0.3955,MillID
+azj_Latn,North Azerbaijani,58,0.9915,0.9831,1.0,0.9915,0.9831,1.0,0.9915,0.9831,1.0,0.5888,0.4173,1.0,0.5888,0.4173,1.0,0.0,tie,0.4026,MillID
+ast_Latn,Asturian,58,0.9649,0.9821,0.9483,1.0,1.0,1.0,1.0,1.0,1.0,0.7389,0.5859,1.0,0.7389,0.5859,1.0,-0.0351,GlotLID,0.2261,MillID
+arn_Latn,Mapuche,58,0.9655,0.9655,0.9655,0.8952,1.0,0.8103,0.9057,1.0,0.8276,0.7887,0.6667,0.9655,0.7887,0.6667,0.9655,0.0599,MillID,0.1768,MillID
+arb_Arab,Arabic (Modern Standard),58,0.8846,1.0,0.7931,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.1154,GlotLID,-0.1154,fastText
+ami_Latn,Amis,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+tzo_Latn,Tzotzil,58,0.9643,1.0,0.931,0.9828,0.9828,0.9828,0.9828,0.9828,0.9828,0.0,0.0,0.0,0.0,0.0,0.0,-0.0185,GlotLID,0.9643,MillID
+uig_Arab,Uyghur,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+ukr_Cyrl,Ukrainian,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9587,0.9206,1.0,0.9587,0.9206,1.0,0.0,tie,0.0413,MillID
+ben_Beng,Bangla,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+bel_Cyrl,Belarusian,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.8345,0.716,1.0,0.8345,0.716,1.0,0.0,tie,0.1655,MillID
+bci_Latn,Baoulé,58,0.9915,0.9831,1.0,1.0,1.0,1.0,0.9915,0.9831,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.9915,MillID
+bba_Latn,Baatonum,58,0.7451,0.6,0.9828,0.9744,0.9661,0.9828,0.9206,0.8529,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.1755,GlotLID,0.7451,MillID
+bax_Latn,bax_Latn,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+vec_Latn,Venetian,58,0.9667,0.9355,1.0,0.9831,0.9667,1.0,0.9831,0.9667,1.0,0.8,0.6667,1.0,0.8,0.6667,1.0,-0.0164,GlotLID,0.1667,MillID
+uzn_Latn,Northern Uzbek,58,0.9355,0.8788,1.0,0.9667,0.9355,1.0,0.9667,0.9355,1.0,0.3483,0.2109,1.0,0.3483,0.2109,1.0,-0.0312,GlotLID,0.5871,MillID
+vep_Latn,Veps,58,0.9825,1.0,0.9655,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0175,GlotLID,0.9825,MillID
+uzn_Cyrl,Northern Uzbek,58,0.9913,1.0,0.9828,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0087,tie,0.9913,MillID
+kal_Latn,Kalaallisut,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.755,0.6129,0.9828,0.755,0.6129,0.9828,0.0,tie,0.245,MillID
+kan_Knda,Kannada,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+kat_Geor,Georgian,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+kaz_Cyrl,Kazakh,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.4444,0.2857,1.0,0.4444,0.2857,1.0,0.0,tie,0.5556,MillID
+kea_Latn,Kabuverdianu,58,0.5729,0.4043,0.9828,0.8056,0.6744,1.0,0.6517,0.4833,1.0,0.1143,0.0789,0.2069,0.1137,0.0784,0.2069,-0.0788,GlotLID,0.4591,MillID
+kha_Latn,Khasi,58,0.9508,0.9062,1.0,0.9915,0.9831,1.0,0.9915,0.9831,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0406,GlotLID,0.9508,MillID
+kir_Cyrl,Kyrgyz,58,0.9915,0.9831,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.6042,0.4328,1.0,0.6042,0.4328,1.0,-0.0085,tie,0.3873,MillID
+ina_Latn,Interlingua,58,0.8382,0.7308,0.9828,0.8593,0.7532,1.0,0.8593,0.7532,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.021,GlotLID,0.8382,MillID
+ilo_Latn,Iloko,58,0.9831,0.9667,1.0,0.9913,1.0,0.9828,0.9913,1.0,0.9828,0.8467,0.7342,1.0,0.8467,0.7342,1.0,-0.0083,tie,0.1363,MillID
+yap_Latn,Yapese,58,0.9913,1.0,0.9828,0.9915,0.9831,1.0,0.9915,0.9831,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0001,tie,0.9913,MillID
+xho_Latn,Xhosa,58,0.9402,0.9322,0.9483,1.0,1.0,1.0,1.0,1.0,1.0,0.703,0.5421,1.0,0.703,0.5421,1.0,-0.0598,GlotLID,0.2371,MillID
+vmw_Latn,Makhuwa,58,0.9825,1.0,0.9655,0.9739,0.9825,0.9655,0.9828,0.9828,0.9828,0.0,0.0,0.0,0.0,0.0,0.0,-0.0003,tie,0.9825,MillID
+kab_Latn,Kabyle,58,0.9259,1.0,0.8621,0.9913,1.0,0.9828,0.9913,1.0,0.9828,0.9355,0.8788,1.0,0.928,0.8657,1.0,-0.0654,GlotLID,-0.0021,tie
+kaa_Cyrl,Kara-Kalpak,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+ita_Latn,Italian,58,0.8571,0.76,0.9828,0.9062,0.8286,1.0,0.9062,0.8286,1.0,0.5421,0.3718,1.0,0.5421,0.3718,1.0,-0.0491,GlotLID,0.3151,MillID
+isl_Latn,Icelandic,58,0.8738,1.0,0.7759,1.0,1.0,1.0,1.0,1.0,1.0,0.8286,0.7073,1.0,0.8286,0.7073,1.0,-0.1262,GlotLID,0.0452,MillID
+glg_Latn,Galician,58,0.9828,0.9828,0.9828,1.0,1.0,1.0,1.0,1.0,1.0,0.7891,0.6517,1.0,0.7891,0.6517,1.0,-0.0172,GlotLID,0.1936,MillID
+glv_Latn,Manx,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+guj_Gujr,Gujarati,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+guc_Latn,Wayuu,58,0.928,0.8657,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.072,GlotLID,0.928,MillID
+haw_Latn,Hawaiian,58,1.0,1.0,1.0,0.9735,1.0,0.9483,0.9735,1.0,0.9483,0.0,0.0,0.0,0.0,0.0,0.0,0.0265,MillID,1.0,MillID
+guu_Latn,guu_Latn,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+nds_Latn,Low German,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+hrv_Latn,Croatian,58,0.5,0.3772,0.7414,0.7273,0.5833,0.9655,0.7273,0.5833,0.9655,0.4596,0.3051,0.931,0.4596,0.3051,0.931,-0.2273,GlotLID,0.0404,MillID
+hnj_Latn,Hmong Njua,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+hil_Latn,Hiligaynon,58,0.9913,1.0,0.9828,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0087,tie,0.9913,MillID
+mfq_Latn,Moba,58,1.0,1.0,1.0,0.9748,0.9508,1.0,0.9748,0.9508,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0252,MillID,1.0,MillID
+mkd_Cyrl,Macedonian,58,0.9915,0.9831,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0085,tie,-0.0085,tie
+lue_Latn,Luvale,58,0.9667,0.9355,1.0,0.9913,1.0,0.9828,0.9913,1.0,0.9828,0.0,0.0,0.0,0.0,0.0,0.0,-0.0246,GlotLID,0.9667,MillID
+lus_Latn,Mizo,58,0.9913,1.0,0.9828,1.0,1.0,1.0,0.9748,0.9508,1.0,0.3494,0.2117,1.0,0.3494,0.2117,1.0,0.0165,MillID,0.6419,MillID
+mai_Deva,Maithili,58,0.9355,0.8788,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9915,0.9831,1.0,0.9915,0.9831,1.0,-0.0645,GlotLID,-0.056,fastText
+mah_Latn,Marshallese,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+mam_Latn,Mam,58,0.8846,1.0,0.7931,0.9369,0.9811,0.8966,0.9369,0.9811,0.8966,0.0,0.0,0.0,0.0,0.0,0.0,-0.0523,GlotLID,0.8846,MillID
+mxi_Latn,mxi_Latn,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+lob_Latn,lob_Latn,58,0.0,0.0,0.0,0.873,0.8088,0.9483,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+lua_Latn,Luba-Lulua,58,0.7097,0.9429,0.569,0.7391,1.0,0.5862,0.7391,1.0,0.5862,0.6347,0.4862,0.9138,0.6347,0.4862,0.9138,-0.0295,GlotLID,0.0749,MillID
+lit_Latn,Lithuanian,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.8112,0.6824,1.0,0.8112,0.6824,1.0,0.0,tie,0.1888,MillID
+lij_Latn,Ligurian,58,0.9748,0.9508,1.0,0.8406,0.725,1.0,0.8406,0.725,1.0,0.4286,0.274,0.9828,0.4286,0.274,0.9828,0.1342,MillID,0.5462,MillID
+loz_Latn,Lozi,58,0.9915,0.9831,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0085,tie,0.9915,MillID
+lad_Latn,Ladino,58,0.9643,1.0,0.931,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0357,GlotLID,0.9643,MillID
+kor_Hang,Korean,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.8722,0.7733,1.0,0.8722,0.7733,1.0,0.0,tie,0.1278,MillID
+kqs_Latn,kqs_Latn,58,0.0,0.0,0.0,0.8041,1.0,0.6724,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+kri_Latn,Krio,58,0.9915,0.9831,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0085,tie,0.9915,MillID
+kmr_Latn,Northern Kurdish,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.703,0.5421,1.0,0.703,0.5421,1.0,0.0,tie,0.297,MillID
+niu_Latn,Niuean,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+nhn_Latn,nhn_Latn,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+ibo_Latn,Igbo,58,0.9748,0.9508,1.0,0.9915,0.9831,1.0,0.9915,0.9831,1.0,0.6629,0.4957,1.0,0.6629,0.4957,1.0,-0.0167,GlotLID,0.3119,MillID
+huu_Latn,Murui Huitoto,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+heb_Hebr,Hebrew,58,0.9825,1.0,0.9655,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0175,GlotLID,-0.0175,fastText
+hea_Latn,hea_Latn,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+mzi_Latn,mzi_Latn,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+mxv_Latn,Metlatónoc Mixtec,58,0.129,1.0,0.069,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.129,MillID,0.129,MillID
+nbl_Latn,South Ndebele,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+ndo_Latn,Ndonga,58,0.95,0.9194,0.9828,0.958,0.9344,0.9828,0.958,0.9344,0.9828,0.0,0.0,0.0,0.0,0.0,0.0,-0.008,tie,0.95,MillID
+ddn_Latn,ddn_Latn,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+qvh_Latn,Huamalíes-Dos de Mayo Huánuco Quechua,58,0.4675,0.9474,0.3103,0.1515,0.625,0.0862,0.1515,0.625,0.0862,0.0,0.0,0.0,0.0,0.0,0.0,0.316,MillID,0.4675,MillID
+bul_Cyrl,Bulgarian,58,0.9744,0.9661,0.9828,1.0,1.0,1.0,1.0,1.0,1.0,0.9915,0.9831,1.0,0.9915,0.9831,1.0,-0.0256,GlotLID,-0.0171,fastText
+snk_Latn,snk_Latn,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+suk_Latn,Sukuma,58,0.6994,0.5429,0.9828,0.7261,0.5758,0.9828,0.7261,0.5758,0.9828,0.0,0.0,0.0,0.0,0.0,0.0,-0.0267,GlotLID,0.6994,MillID
+srr_Latn,Serer,58,0.9508,0.9062,1.0,0.9508,0.9062,1.0,0.9508,0.9062,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.9508,MillID
+tpi_Latn,Tok Pisin,58,0.9831,0.9667,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9748,0.9508,1.0,0.9748,0.9508,1.0,-0.0169,GlotLID,0.0083,tie
+tsn_Latn,Tswana,58,0.9915,0.9831,1.0,0.9915,0.9831,1.0,0.9915,0.9831,1.0,0.8788,0.7838,1.0,0.8788,0.7838,1.0,0.0,tie,0.1127,MillID
+tyv_Cyrl,Tuvinian,58,1.0,1.0,1.0,0.9915,0.9831,1.0,0.9915,0.9831,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0085,tie,1.0,MillID
+tir_Ethi,Tigrinya,58,0.9915,0.9831,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9915,0.9831,1.0,0.9915,0.9831,1.0,-0.0085,tie,0.0,tie
+tel_Telu,Telugu,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+zul_Latn,Zulu,58,0.9027,0.9273,0.8793,0.9744,0.9661,0.9828,0.9744,0.9661,0.9828,0.601,0.4296,1.0,0.601,0.4296,1.0,-0.0717,GlotLID,0.3016,MillID
+ztu_Latn,ztu_Latn,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+zlm_Latn,zlm_Latn,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+tet_Latn,Tetum,58,0.6377,0.55,0.7586,0.1667,0.4286,0.1034,0.1644,0.4,0.1034,0.0,0.0,0.0,0.0,0.0,0.0,0.4733,MillID,0.6377,MillID
+bum_Latn,Bulu,58,0.2059,0.7,0.1207,0.0968,0.75,0.0517,0.0896,0.3333,0.0517,0.0,0.0,0.0,0.0,0.0,0.0,0.1163,MillID,0.2059,MillID
+cat_Latn,Catalan,58,0.9748,0.9508,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.5714,0.4,1.0,0.5714,0.4,1.0,-0.0252,GlotLID,0.4034,MillID
+ces_Latn,Czech,58,0.9739,0.9825,0.9655,1.0,1.0,1.0,1.0,1.0,1.0,0.8788,0.7838,1.0,0.8788,0.7838,1.0,-0.0261,GlotLID,0.0951,MillID
+swb_Latn,swb_Latn,58,0.0,0.0,0.0,0.8906,0.8143,0.9828,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+sus_Latn,Susu,58,0.9298,0.9464,0.9138,0.928,0.8657,1.0,0.928,0.8657,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0018,tie,0.9298,MillID
+swh_Latn,Swahili,58,0.8522,0.8596,0.8448,0.9573,0.9492,0.9655,0.9412,0.918,0.9655,0.3268,0.1953,1.0,0.3268,0.1953,1.0,-0.089,GlotLID,0.5254,MillID
+chk_Latn,Chuukese,58,0.9915,0.9831,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0085,tie,0.9915,MillID
+dyu_Latn,Dyula,58,0.0,0.0,0.0,0.4186,0.6429,0.3103,0.3025,0.2951,0.3103,0.0123,0.0096,0.0172,0.0123,0.0096,0.0172,-0.3025,GlotLID,-0.0123,fastText
+emk_Latn,emk_Latn,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+sco_Latn,Scots,58,0.9739,0.9825,0.9655,0.9565,0.9649,0.9483,0.9565,0.9649,0.9483,0.0,0.0,0.0,0.0,0.0,0.0,0.0174,MillID,0.9739,MillID
+slr_Latn,slr_Latn,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+sme_Latn,Northern Sami,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+quc_Latn,Kʼicheʼ,58,1.0,1.0,1.0,0.9174,0.9804,0.8621,0.9174,0.9804,0.8621,0.0,0.0,0.0,0.0,0.0,0.0,0.0826,MillID,1.0,MillID
+qvc_Latn,Cajamarca Quechua,58,0.648,0.4793,1.0,0.9062,0.8286,1.0,0.9062,0.8286,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.2582,GlotLID,0.648,MillID
+que_Latn,que_Latn,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+cnh_Latn,Hakha Chin,58,0.9915,0.9831,1.0,0.9915,0.9831,1.0,0.9915,0.9831,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.9915,MillID
+crs_Latn,Seselwa Creole French,58,0.9915,0.9831,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0085,tie,0.9915,MillID
+pcd_Latn,Picard,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+pan_Guru,Punjabi,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+rgn_Latn,rgn_Latn,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+cri_Latn,cri_Latn,58,0.0,0.0,0.0,0.766,1.0,0.6207,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+rus_Cyrl,Russian,58,0.9431,0.8923,1.0,0.8,0.6667,1.0,0.8,0.6667,1.0,0.6237,0.4531,1.0,0.6237,0.4531,1.0,0.1431,MillID,0.3194,MillID
+run_Latn,Rundi,58,0.8444,0.7403,0.9828,0.8923,0.8056,1.0,0.8923,0.8056,1.0,0.7205,0.5631,1.0,0.7205,0.5631,1.0,-0.0479,GlotLID,0.1239,MillID
+eng_Latn,English,58,0.9748,0.9508,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.5918,0.4203,1.0,0.5918,0.4203,1.0,-0.0252,GlotLID,0.383,MillID
+epo_Latn,Esperanto,58,0.9913,1.0,0.9828,1.0,1.0,1.0,1.0,1.0,1.0,0.3402,0.2049,1.0,0.3402,0.2049,1.0,-0.0087,tie,0.6511,MillID
+fkv_Latn,Kven Finnish,58,0.6279,0.9643,0.4655,0.6667,1.0,0.5,0.6667,1.0,0.5,0.0,0.0,0.0,0.0,0.0,0.0,-0.0388,GlotLID,0.6279,MillID
+fuf_Latn,Pular,58,0.8807,0.9412,0.8276,0.4828,0.7241,0.3621,0.4828,0.7241,0.3621,0.0,0.0,0.0,0.0,0.0,0.0,0.398,MillID,0.8807,MillID
+fuf_Adlm,fuf_Adlm,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+fry_Latn,Western Frisian,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+fra_Latn,French,58,0.8544,0.9778,0.7586,0.9915,0.9831,1.0,0.9748,0.9508,1.0,0.9134,0.8406,1.0,0.9134,0.8406,1.0,-0.1204,GlotLID,-0.059,fastText
+fon_Latn,Fon,58,0.9333,0.9032,0.9655,0.9913,1.0,0.9828,0.9913,1.0,0.9828,0.4345,0.2775,1.0,0.4345,0.2775,1.0,-0.058,GlotLID,0.4989,MillID
+gej_Latn,gej_Latn,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+pap_Latn,Papiamento,58,1.0,1.0,1.0,0.9831,0.9667,1.0,0.9831,0.9667,1.0,0.6864,0.5225,1.0,0.6864,0.5225,1.0,0.0169,MillID,0.3136,MillID
+nno_Latn,Norwegian Nynorsk,58,0.9558,0.9818,0.931,0.9915,0.9831,1.0,0.9831,0.9667,1.0,0.9667,0.9355,1.0,0.9667,0.9355,1.0,-0.0273,GlotLID,-0.0109,fastText
+nld_Latn,Dutch,58,0.9915,0.9831,1.0,1.0,1.0,1.0,0.9062,0.8286,1.0,0.703,0.5421,1.0,0.703,0.5421,1.0,0.0852,MillID,0.2884,MillID
+nso_Latn,Northern Sotho,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9508,0.9062,1.0,0.9508,0.9062,1.0,0.0,tie,0.0492,MillID
+oaa_Cyrl,oaa_Cyrl,58,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+lns_Latn,lns_Latn,57,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+khk_Cyrl,Halh Mongolian,57,1.0,1.0,1.0,0.9655,0.9492,0.9825,0.9655,0.9492,0.9825,0.7862,0.6477,1.0,0.7862,0.6477,1.0,0.0345,MillID,0.2138,MillID
+zyb_Latn,Yongbei Zhuang,57,0.6064,0.4351,1.0,0.9828,0.9661,1.0,0.9744,0.95,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.368,GlotLID,0.6064,MillID
+aar_Latn,aar_Latn,57,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+abk_Cyrl,Abkhazian,57,0.9744,0.95,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9913,0.9828,1.0,0.9913,0.9828,1.0,-0.0256,GlotLID,-0.0169,fastText
+toj_Latn,Tojolabal,57,0.9825,0.9825,0.9825,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0175,GlotLID,0.9825,MillID
+tdt_Latn,Tetun Dili,57,0.4565,0.6,0.3684,0.6115,0.48,0.8421,0.6115,0.48,0.8421,0.0,0.0,0.0,0.0,0.0,0.0,-0.1549,GlotLID,0.4565,MillID
+tah_Latn,Tahitian,57,0.973,1.0,0.9474,0.8125,1.0,0.6842,0.8125,1.0,0.6842,0.9661,0.9344,1.0,0.9661,0.9344,1.0,0.1605,MillID,0.0069,tie
+tuk_Cyrl,Turkmen,57,0.9913,0.9828,1.0,0.9913,0.9828,1.0,0.95,0.9048,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0413,MillID,0.9913,MillID
+cfm_Latn,Falam Chin,57,0.9912,1.0,0.9825,0.9735,0.9821,0.9649,0.9735,0.9821,0.9649,0.0,0.0,0.0,0.0,0.0,0.0,0.0177,MillID,0.9912,MillID
+toi_Latn,Tonga (Zambia),57,0.9825,0.9825,0.9825,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0175,GlotLID,0.9825,MillID
+tat_Cyrl,Tatar,57,0.9268,0.8636,1.0,1.0,1.0,1.0,0.8702,0.7703,1.0,0.479,0.3149,1.0,0.479,0.3149,1.0,0.0566,MillID,0.4478,MillID
+som_Latn,Somali,57,0.8906,0.8028,1.0,0.9344,0.8769,1.0,0.9344,0.8769,1.0,0.5816,0.4101,1.0,0.5816,0.4101,1.0,-0.0438,GlotLID,0.309,MillID
+tgk_Cyrl,Tajik,57,0.9421,0.8906,1.0,0.9421,0.8906,1.0,0.9421,0.8906,1.0,0.4453,0.2864,1.0,0.4453,0.2864,1.0,0.0,tie,0.4968,MillID
+cha_Latn,Chamorro,57,0.9828,0.9661,1.0,0.9159,0.98,0.8596,0.9159,0.98,0.8596,0.0,0.0,0.0,0.0,0.0,0.0,0.0669,MillID,0.9828,MillID
+cbt_Latn,Chayahuita,57,1.0,1.0,1.0,0.9913,0.9828,1.0,0.9913,0.9828,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0087,tie,1.0,MillID
+sid_Latn,Sidamo,57,0.9655,0.9492,0.9825,0.912,0.8382,1.0,0.912,0.8382,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0535,MillID,0.9655,MillID
+sah_Cyrl,Yakut,57,0.8028,0.6706,1.0,0.6441,0.475,1.0,0.6441,0.475,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1587,MillID,0.8028,MillID
+rup_Latn,Aromanian,57,0.9828,0.9661,1.0,0.9744,0.95,1.0,0.9661,0.9344,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0167,MillID,0.9828,MillID
+cak_Latn,Kaqchikel,57,0.9912,1.0,0.9825,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0088,tie,0.9912,MillID
+cbu_Latn,Candoshi-Shapra,57,0.5,1.0,0.3333,0.8824,1.0,0.7895,0.8738,0.9783,0.7895,0.0,0.0,0.0,0.0,0.0,0.0,-0.3738,GlotLID,0.5,MillID
+pis_Latn,Pijin,57,1.0,1.0,1.0,0.9913,0.9828,1.0,0.9913,0.9828,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0087,tie,1.0,MillID
+qvn_Latn,North Junín Quechua,57,0.2188,1.0,0.1228,0.4595,1.0,0.2982,0.4595,1.0,0.2982,0.0,0.0,0.0,0.0,0.0,0.0,-0.2407,GlotLID,0.2188,MillID
+piu_Latn,piu_Latn,57,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+pon_Latn,Pohnpeian,57,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+quh_Latn,South Bolivian Quechua,57,0.9655,0.9492,0.9825,0.9369,0.963,0.9123,0.9649,0.9649,0.9649,0.0,0.0,0.0,0.0,0.0,0.0,0.0006,tie,0.9655,MillID
+sot_Latn,Southern Sotho,57,1.0,1.0,1.0,0.9913,0.9828,1.0,0.9913,0.9828,1.0,0.748,0.697,0.807,0.748,0.697,0.807,0.0087,tie,0.252,MillID
+buc_Latn,buc_Latn,57,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+dag_Latn,Dagbani,57,0.8485,0.7467,0.9825,0.8175,0.7,0.9825,0.7887,0.6588,0.9825,0.0,0.0,0.0,0.0,0.0,0.0,0.0598,MillID,0.8485,MillID
+pau_Latn,Palauan,57,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+fvr_Latn,fvr_Latn,57,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+evn_Cyrl,evn_Cyrl,57,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+fat_Latn,Fanti,57,1.0,1.0,1.0,0.9744,0.95,1.0,0.9661,0.9344,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0339,MillID,1.0,MillID
+fao_Latn,Faroese,57,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.7312,0.9444,0.5965,0.7312,0.9444,0.5965,0.0,tie,0.2688,MillID
+mri_Latn,Māori,57,0.9194,0.8507,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.3619,0.2209,1.0,0.3608,0.2201,1.0,-0.0806,GlotLID,0.5586,MillID
+hni_Latn,hni_Latn,57,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+lao_Laoo,Lao,57,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+koi_Cyrl,Komi-Permyak,57,0.8367,1.0,0.7193,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.1633,GlotLID,0.8367,MillID
+quz_Latn,Cusco Quechua,57,0.6571,0.5542,0.807,0.7083,0.5862,0.8947,0.7211,0.5889,0.9298,0.0,0.0,0.0,0.0,0.0,0.0,-0.0639,GlotLID,0.6571,MillID
+qxn_Latn,Northern Conchucos Ancash Quechua,57,0.127,0.6667,0.0702,0.0345,1.0,0.0175,0.0345,1.0,0.0175,0.0,0.0,0.0,0.0,0.0,0.0,0.0925,MillID,0.127,MillID
+csa_Latn,csa_Latn,57,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+dip_Latn,dip_Latn,57,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+duu_Latn,duu_Latn,57,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+bam_Latn,Bambara,57,0.6316,0.4737,0.9474,0.7092,0.5952,0.8772,0.6369,0.5,0.8772,0.1511,0.0826,0.8772,0.1497,0.0818,0.8772,-0.0054,tie,0.4819,MillID
+aii_Syrc,Assyrian Neo-Aramaic,57,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+bem_Latn,Bemba,57,0.9913,0.9828,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.4254,0.2701,1.0,0.4254,0.2701,1.0,-0.0087,tie,0.5659,MillID
+agr_Latn,Aguaruna,57,0.9483,0.9322,0.9649,0.9739,0.9655,0.9825,0.9739,0.9655,0.9825,0.0,0.0,0.0,0.0,0.0,0.0,-0.0256,GlotLID,0.9483,MillID
+kjh_Cyrl,Khakas,57,0.9744,0.95,1.0,0.9913,0.9828,1.0,0.9913,0.9828,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0169,GlotLID,0.9744,MillID
+yor_Latn,Yoruba,57,0.8906,0.8028,1.0,0.9828,0.9661,1.0,0.9828,0.9661,1.0,0.5758,0.4043,1.0,0.5758,0.4043,1.0,-0.0921,GlotLID,0.3149,MillID
+kek_Latn,Kekchí,57,1.0,1.0,1.0,0.9913,0.9828,1.0,0.9913,0.9828,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0087,tie,1.0,MillID
+div_Thaa,Divehi,57,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+zro_Latn,zro_Latn,56,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+mya_Mymr,Burmese,56,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.6707,0.5045,1.0,0.6707,0.5045,1.0,0.0,tie,0.3293,MillID
+mos_Latn,Mossi,56,0.9739,0.9492,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.8296,0.7089,1.0,0.8296,0.7089,1.0,-0.0261,GlotLID,0.1443,MillID
+lun_Latn,Lunda,56,0.991,1.0,0.9821,0.991,1.0,0.9821,0.991,1.0,0.9821,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.991,MillID
+amc_Latn,amc_Latn,56,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+ura_Latn,Urarina,56,1.0,1.0,1.0,0.9825,0.9655,1.0,0.9825,0.9655,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0175,MillID,1.0,MillID
+ady_Cyrl,Adyghe,56,0.963,1.0,0.9286,0.9818,1.0,0.9643,0.9818,1.0,0.9643,0.655,0.487,1.0,0.655,0.487,1.0,-0.0189,GlotLID,0.308,MillID
+azj_Cyrl,North Azerbaijani,56,0.9739,0.9492,1.0,1.0,1.0,1.0,0.9912,0.9825,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0172,GlotLID,0.9739,MillID
+niv_Cyrl,niv_Cyrl,56,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+gkp_Latn,gkp_Latn,56,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+pbb_Latn,Páez,56,0.5941,0.6667,0.5357,0.6458,0.775,0.5536,0.6458,0.775,0.5536,0.0,0.0,0.0,0.0,0.0,0.0,-0.0518,GlotLID,0.5941,MillID
+pcm_Latn,Nigerian Pidgin,56,0.9825,0.9655,1.0,0.9244,0.873,0.9821,0.9256,0.8615,1.0,0.8627,0.9565,0.7857,0.955,0.9636,0.9464,0.0568,MillID,0.0275,MillID
+shn_Mymr,Shan,56,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+slk_Latn,Slovak,56,0.9825,0.9655,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.918,0.8485,1.0,0.918,0.8485,1.0,-0.0175,GlotLID,0.0644,MillID
+gle_Latn,Irish,56,1.0,1.0,1.0,0.9825,0.9655,1.0,0.9825,0.9655,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0175,MillID,0.0,tie
+bho_Deva,Bhojpuri,56,0.8515,0.9556,0.7679,0.9333,1.0,0.875,0.9333,1.0,0.875,0.8,0.8571,0.75,0.8,0.8571,0.75,-0.0818,GlotLID,0.0515,MillID
+yad_Latn,Yagua,55,0.963,0.9811,0.9455,0.9815,1.0,0.9636,0.9815,1.0,0.9636,0.0,0.0,0.0,0.0,0.0,0.0,-0.0185,GlotLID,0.963,MillID
+dyo_Latn,Jola-Fonyi,55,0.9908,1.0,0.9818,0.9908,1.0,0.9818,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0092,tie,0.9908,MillID
+tem_Latn,Timne,55,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+san_Deva,Sanskrit,55,0.9815,1.0,0.9636,0.9908,1.0,0.9818,0.9908,1.0,0.9818,0.9558,0.931,0.9818,0.9558,0.931,0.9818,-0.0093,tie,0.0257,MillID
+shk_Latn,shk_Latn,55,0.0,0.0,0.0,0.9216,1.0,0.8545,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+npi_Deva,Nepali,55,1.0,1.0,1.0,0.9908,1.0,0.9818,0.9908,1.0,0.9818,0.9565,0.9167,1.0,0.9565,0.9167,1.0,0.0092,tie,0.0435,MillID
+ido_Latn,Ido,55,0.955,0.9464,0.9636,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.045,GlotLID,0.955,MillID
+mnw_Mymr,Mon,55,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+guk_Latn,guk_Latn,55,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+kbd_Cyrl,Kabardian,55,0.9649,0.9322,1.0,0.9735,0.9483,1.0,0.9735,0.9483,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0085,tie,0.9649,MillID
+yrk_Cyrl,Nenets,55,0.9725,0.9815,0.9636,0.963,0.9811,0.9455,0.9455,0.9455,0.9455,0.0,0.0,0.0,0.0,0.0,0.0,0.027,MillID,0.9725,MillID
+hlt_Latn,hlt_Latn,55,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+dje_Latn,Zarma,54,0.8381,0.8627,0.8148,0.8571,0.8276,0.8889,0.8448,0.7903,0.9074,0.0,0.0,0.0,0.0,0.0,0.0,-0.0067,tie,0.8381,MillID
+qxu_Latn,qxu_Latn,54,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+blt_Tavt,blt_Tavt,54,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+koo_Latn,Konzo,54,0.9714,1.0,0.9444,0.9615,1.0,0.9259,0.9615,1.0,0.9259,0.0,0.0,0.0,0.0,0.0,0.0,0.0099,tie,0.9714,MillID
+mag_Deva,Magahi,53,0.8679,0.8679,0.8679,0.9358,0.9107,0.9623,0.9358,0.9107,0.9623,0.6012,0.4455,0.9245,0.6012,0.4455,0.9245,-0.0679,GlotLID,0.2667,MillID
+plt_Latn,Malagasy (Plateau),53,0.9907,0.9815,1.0,1.0,1.0,1.0,0.9907,0.9815,1.0,0.9217,0.8548,1.0,0.9217,0.8548,1.0,0.0,tie,0.0689,MillID
+iii_Yiii,iii_Yiii,53,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+nku_Latn,nku_Latn,52,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+cni_Latn,Asháninka,52,0.7727,0.6375,0.9808,0.963,0.9286,1.0,0.963,0.9286,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.1902,GlotLID,0.7727,MillID
+yao_Latn,Yao,52,0.9811,0.963,1.0,0.9903,1.0,0.9808,0.9903,1.0,0.9808,0.0,0.0,0.0,0.0,0.0,0.0,-0.0092,tie,0.9811,MillID
+ykg_Cyrl,ykg_Cyrl,51,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+idu_Latn,Idoma,51,0.6027,1.0,0.4314,0.8791,1.0,0.7843,0.9375,1.0,0.8824,0.0,0.0,0.0,0.0,0.0,0.0,-0.3348,GlotLID,0.6027,MillID
+kde_Latn,Makonde,51,0.9278,0.9783,0.8824,0.9703,0.98,0.9608,0.9703,0.98,0.9608,0.0,0.0,0.0,0.0,0.0,0.0,-0.0425,GlotLID,0.9278,MillID
+kkh_Lana,kkh_Lana,51,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+amh_Ethi,Amharic,51,0.9901,1.0,0.9804,1.0,1.0,1.0,1.0,1.0,1.0,0.8409,1.0,0.7255,0.8409,1.0,0.7255,-0.0099,tie,0.1492,MillID
+alt_Cyrl,Southern Altai,51,0.9808,0.9623,1.0,0.9623,0.9273,1.0,0.9623,0.9273,1.0,0.6939,0.5312,1.0,0.6939,0.5312,1.0,0.0185,MillID,0.2869,MillID
+miq_Latn,Mískito,51,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+mto_Latn,Totontepec Mixe,51,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+nio_Cyrl,nio_Cyrl,51,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+san_Gran,san_Gran,51,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+ijs_Latn,ijs_Latn,50,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+nyn_Latn,Nyankole,50,0.9804,0.9615,1.0,0.9796,1.0,0.96,0.9796,1.0,0.96,0.0,0.0,0.0,0.0,0.0,0.0,0.0008,tie,0.9804,MillID
+zdj_Latn,Ngazidja Comorian,50,0.6536,0.4854,1.0,0.9697,0.9796,0.96,0.6853,0.5269,0.98,0.0,0.0,0.0,0.0,0.0,0.0,-0.0317,GlotLID,0.6536,MillID
+yue_Hani,Cantonese,49,0.7073,0.8788,0.5918,0.5797,1.0,0.4082,0.5797,1.0,0.4082,0.0687,0.0357,0.9184,0.0687,0.0357,0.9184,0.1276,MillID,0.6386,MillID
+gan_Hani,gan_Hani,49,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+nym_Latn,nym_Latn,49,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+eve_Cyrl,Even,49,0.4118,0.2593,1.0,0.392,0.26,0.7959,0.39,0.2583,0.7959,0.0,0.0,0.0,0.0,0.0,0.0,0.0218,MillID,0.4118,MillID
+lug_Latn,Ganda,49,0.9703,0.9423,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.3311,0.1984,1.0,0.3311,0.1984,1.0,-0.0297,GlotLID,0.6392,MillID
+gyr_Latn,Guarayu,49,0.5373,1.0,0.3673,0.9897,1.0,0.9796,0.9897,1.0,0.9796,0.0,0.0,0.0,0.0,0.0,0.0,-0.4524,GlotLID,0.5373,MillID
+gug_Latn,Paraguayan Guaraní,49,0.9515,0.9074,1.0,0.9899,0.98,1.0,0.9899,0.98,1.0,0.2328,0.1317,1.0,0.2328,0.1317,1.0,-0.0384,GlotLID,0.7187,MillID
+wuu_Hani,Wu Chinese,48,0.4653,0.3052,0.9792,0.4312,0.2765,0.9792,0.4273,0.2733,0.9792,0.0,0.0,0.0,0.0,0.0,0.0,0.0381,MillID,0.4653,MillID
+cjy_Hani,cjy_Hani,48,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+hsn_Hani,hsn_Hani,48,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+nan_Hani,Min Nan Chinese,48,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+hak_Hani,Hakka Chinese,48,0.0392,0.3333,0.0208,0.04,0.5,0.0208,0.04,0.5,0.0208,0.0,0.0,0.0,0.0,0.0,0.0,-0.0008,tie,0.0392,MillID
+bin_Latn,Bini,47,0.9892,1.0,0.9787,0.9318,1.0,0.8723,0.9318,1.0,0.8723,0.0,0.0,0.0,0.0,0.0,0.0,0.0574,MillID,0.9892,MillID
+lia_Latn,West-Central Limba,45,1.0,1.0,1.0,0.989,0.9783,1.0,0.989,0.9783,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.011,MillID,1.0,MillID
+oss_Cyrl,Ossetic,44,1.0,1.0,1.0,0.9888,0.9778,1.0,0.9888,0.9778,1.0,0.9565,0.9167,1.0,0.9565,0.9167,1.0,0.0112,MillID,0.0435,MillID
+ike_Cans,Eastern Canadian Inuktitut,44,1.0,1.0,1.0,0.9778,0.9565,1.0,0.9778,0.9565,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0222,MillID,1.0,MillID
+cbs_Latn,Cashinahua,43,0.8485,0.75,0.9767,0.8723,0.8039,0.9535,0.8723,0.8039,0.9535,0.0,0.0,0.0,0.0,0.0,0.0,-0.0239,GlotLID,0.8485,MillID
+csw_Cans,csw_Cans,43,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+ojb_Cans,Northwestern Ojibwa,43,1.0,1.0,1.0,0.9639,1.0,0.9302,0.9639,1.0,0.9302,0.0,0.0,0.0,0.0,0.0,0.0,0.0361,MillID,1.0,MillID
+bfa_Latn,bfa_Latn,42,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+wwa_Latn,wwa_Latn,39,0.0,0.0,0.0,0.975,0.9512,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+ese_Latn,Ese Ejja,35,1.0,1.0,1.0,0.9855,1.0,0.9714,0.9855,1.0,0.9714,0.0,0.0,0.0,0.0,0.0,0.0,0.0145,MillID,1.0,MillID
+tzh_Latn,Tzeltal,25,0.9259,0.8621,1.0,0.9804,0.9615,1.0,0.9804,0.9615,1.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0545,GlotLID,0.9259,MillID
+chv_Cyrl,Chuvash,25,1.0,1.0,1.0,0.9259,0.8621,1.0,0.9259,0.8621,1.0,0.8929,0.8065,1.0,0.8929,0.8065,1.0,0.0741,MillID,0.1071,MillID
+zlm_Arab,zlm_Arab,24,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+tzm_Tfng,Central Atlas Tamazight,1,0.1667,0.0909,1.0,0.0625,0.0323,1.0,0.0625,0.0323,1.0,0.0328,0.0167,1.0,0.0328,0.0167,1.0,0.1042,MillID,0.1339,MillID
+tgl_Tglg,tgl_Tglg,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,0.0,tie
+taj_Deva,Eastern Tamang,1,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID

reports/udhr-all/millid_per_language.csv

@@ -0,0 +1,430 @@
+label,support,tp,fp,fn,tn,precision,recall,f1,specificity,fp_per_million,sensitivity
+aar_Latn,57,0,0,57,27566,0.0,0.0,0.0,1.0,0.0,0.0
+abk_Cyrl,57,57,3,0,27563,0.95,1.0,0.974359,0.999891,108.829718,1.0
+ace_Latn,61,61,0,0,27562,1.0,1.0,1.0,1.0,0.0,1.0
+acu_Latn,180,130,5,50,27438,0.962963,0.722222,0.825397,0.999818,182.195824,0.722222
+ada_Latn,59,59,3,0,27561,0.951613,1.0,0.975207,0.999891,108.837614,1.0
+ady_Cyrl,56,52,0,4,27567,1.0,0.928571,0.962963,1.0,0.0,0.928571
+afr_Latn,60,59,0,1,27563,1.0,0.983333,0.991597,1.0,0.0,0.983333
+agr_Latn,57,55,4,2,27562,0.932203,0.964912,0.948276,0.999855,145.10629,0.964912
+aii_Syrc,57,0,0,57,27566,0.0,0.0,0.0,1.0,0.0,0.0
+ajg_Latn,59,59,63,0,27501,0.483607,1.0,0.651934,0.997714,2285.5899,1.0
+als_Latn,59,58,0,1,27564,1.0,0.983051,0.991453,1.0,0.0,0.983051
+alt_Cyrl,51,51,2,0,27570,0.962264,1.0,0.980769,0.999927,72.537357,1.0
+amc_Latn,56,0,0,56,27567,0.0,0.0,0.0,1.0,0.0,0.0
+ame_Latn,59,59,0,0,27564,1.0,1.0,1.0,1.0,0.0,1.0
+amh_Ethi,51,50,0,1,27572,1.0,0.980392,0.990099,1.0,0.0,0.980392
+ami_Latn,58,0,0,58,27565,0.0,0.0,0.0,1.0,0.0,0.0
+amr_Latn,61,61,3,0,27559,0.953125,1.0,0.976,0.999891,108.845512,1.0
+arb_Arab,58,46,0,12,27565,1.0,0.793103,0.884615,1.0,0.0,0.793103
+arl_Latn,60,60,0,0,27563,1.0,1.0,1.0,1.0,0.0,1.0
+arn_Latn,58,56,2,2,27563,0.965517,0.965517,0.965517,0.999927,72.555777,0.965517
+ast_Latn,58,55,1,3,27564,0.982143,0.948276,0.964912,0.999964,36.277889,0.948276
+auc_Latn,154,0,0,154,27469,0.0,0.0,0.0,1.0,0.0,0.0
+ayr_Latn,59,59,2,0,27562,0.967213,1.0,0.983333,0.999927,72.55841,1.0
+azj_Cyrl,56,56,3,0,27564,0.949153,1.0,0.973913,0.999891,108.82577,1.0
+azj_Latn,58,58,1,0,27564,0.983051,1.0,0.991453,0.999964,36.277889,1.0
+bam_Latn,57,54,60,3,27506,0.473684,0.947368,0.631579,0.997823,2176.594355,0.947368
+ban_Latn,60,57,1,3,27562,0.982759,0.95,0.966102,0.999964,36.280521,0.95
+bax_Latn,58,0,0,58,27565,0.0,0.0,0.0,1.0,0.0,0.0
+bba_Latn,58,57,38,1,27527,0.6,0.982759,0.745098,0.998621,1378.559768,0.982759
+bci_Latn,58,58,1,0,27564,0.983051,1.0,0.991453,0.999964,36.277889,1.0
+bcl_Latn,59,58,0,1,27564,1.0,0.983051,0.991453,1.0,0.0,0.983051
+bel_Cyrl,58,58,0,0,27565,1.0,1.0,1.0,1.0,0.0,1.0
+bem_Latn,57,57,1,0,27565,0.982759,1.0,0.991304,0.999964,36.276573,1.0
+ben_Beng,58,58,0,0,27565,1.0,1.0,1.0,1.0,0.0,1.0
+bfa_Latn,42,0,0,42,27581,0.0,0.0,0.0,1.0,0.0,0.0
+bho_Deva,56,43,2,13,27565,0.955556,0.767857,0.851485,0.999927,72.550513,0.767857
+bin_Latn,47,46,0,1,27576,1.0,0.978723,0.989247,1.0,0.0,0.978723
+bis_Latn,59,59,0,0,27564,1.0,1.0,1.0,1.0,0.0,1.0
+blt_Tavt,54,0,0,54,27569,0.0,0.0,0.0,1.0,0.0,0.0
+boa_Latn,61,61,0,0,27562,1.0,1.0,1.0,1.0,0.0,1.0
+bod_Tibt,60,58,1,2,27562,0.983051,0.966667,0.97479,0.999964,36.280521,0.966667
+bos_Cyrl,59,0,0,59,27564,0.0,0.0,0.0,1.0,0.0,0.0
+bos_Latn,59,13,58,46,27506,0.183099,0.220339,0.2,0.997896,2104.193876,0.220339
+bre_Latn,59,58,1,1,27563,0.983051,0.983051,0.983051,0.999964,36.279205,0.983051
+buc_Latn,57,0,0,57,27566,0.0,0.0,0.0,1.0,0.0,0.0
+bug_Latn,60,53,1,7,27562,0.981481,0.883333,0.929825,0.999964,36.280521,0.883333
+bul_Cyrl,58,57,2,1,27563,0.966102,0.982759,0.974359,0.999927,72.555777,0.982759
+bum_Latn,58,7,3,51,27562,0.7,0.12069,0.205882,0.999891,108.833666,0.12069
+cab_Latn,60,60,0,0,27563,1.0,1.0,1.0,1.0,0.0,1.0
+cak_Latn,57,56,0,1,27566,1.0,0.982456,0.99115,1.0,0.0,0.982456
+cat_Latn,58,58,3,0,27562,0.95082,1.0,0.97479,0.999891,108.833666,1.0
+cbi_Latn,62,62,4,0,27557,0.939394,1.0,0.96875,0.999855,145.132615,1.0
+cbr_Latn,60,60,0,0,27563,1.0,1.0,1.0,1.0,0.0,1.0
+cbs_Latn,43,42,14,1,27566,0.75,0.976744,0.848485,0.999492,507.614213,0.976744
+cbt_Latn,57,57,0,0,27566,1.0,1.0,1.0,1.0,0.0,1.0
+cbu_Latn,57,19,0,38,27566,1.0,0.333333,0.5,1.0,0.0,0.333333
+ccp_Cakm,59,0,0,59,27564,0.0,0.0,0.0,1.0,0.0,0.0
+ceb_Latn,59,59,1,0,27563,0.983333,1.0,0.991597,0.999964,36.279205,1.0
+ces_Latn,58,56,1,2,27564,0.982456,0.965517,0.973913,0.999964,36.277889,0.965517
+cfm_Latn,57,56,0,1,27566,1.0,0.982456,0.99115,1.0,0.0,0.982456
+cha_Latn,57,57,2,0,27564,0.966102,1.0,0.982759,0.999927,72.553145,1.0
+chj_Latn,59,0,0,59,27564,0.0,0.0,0.0,1.0,0.0,0.0
+chk_Latn,58,58,1,0,27564,0.983051,1.0,0.991453,0.999964,36.277889,1.0
+chr_Cher,115,58,0,57,27508,1.0,0.504348,0.67052,1.0,0.0,0.504348
+chv_Cyrl,25,25,0,0,27598,1.0,1.0,1.0,1.0,0.0,1.0
+cic_Latn,60,0,0,60,27563,0.0,0.0,0.0,1.0,0.0,0.0
+cjk_Latn,114,88,1,26,27508,0.988764,0.77193,0.866995,0.999964,36.351739,0.77193
+cjs_Cyrl,59,54,1,5,27563,0.981818,0.915254,0.947368,0.999964,36.279205,0.915254
+cjy_Hani,48,0,0,48,27575,0.0,0.0,0.0,1.0,0.0,0.0
+cmn_Hani,319,306,152,13,27152,0.668122,0.959248,0.787645,0.994433,5566.949897,0.959248
+cnh_Latn,58,58,1,0,27564,0.983051,1.0,0.991453,0.999964,36.277889,1.0
+cni_Latn,52,51,29,1,27542,0.6375,0.980769,0.772727,0.998948,1051.829821,0.980769
+cnr_Latn,59,0,4,59,27560,0.0,0.0,0.0,0.999855,145.116819,0.0
+cof_Latn,59,59,0,0,27564,1.0,1.0,1.0,1.0,0.0,1.0
+cos_Latn,59,59,1,0,27563,0.983333,1.0,0.991597,0.999964,36.279205,1.0
+cot_Latn,60,59,0,1,27563,1.0,0.983333,0.991597,1.0,0.0,0.983333
+cpu_Latn,60,35,1,25,27562,0.972222,0.583333,0.729167,0.999964,36.280521,0.583333
+crh_Latn,59,59,2,0,27562,0.967213,1.0,0.983333,0.999927,72.55841,1.0
+cri_Latn,58,0,0,58,27565,0.0,0.0,0.0,1.0,0.0,0.0
+crs_Latn,58,58,1,0,27564,0.983051,1.0,0.991453,0.999964,36.277889,1.0
+csa_Latn,57,0,0,57,27566,0.0,0.0,0.0,1.0,0.0,0.0
+csw_Cans,43,0,0,43,27580,0.0,0.0,0.0,1.0,0.0,0.0
+ctd_Latn,60,26,0,34,27563,1.0,0.433333,0.604651,1.0,0.0,0.433333
+cym_Latn,60,60,1,0,27562,0.983607,1.0,0.991736,0.999964,36.280521,1.0
+dag_Latn,57,56,19,1,27547,0.746667,0.982456,0.848485,0.999311,689.254879,0.982456
+dan_Latn,61,58,4,3,27558,0.935484,0.95082,0.943089,0.999855,145.127349,0.95082
+ddn_Latn,58,0,0,58,27565,0.0,0.0,0.0,1.0,0.0,0.0
+deu_Latn,67,67,0,0,27556,1.0,1.0,1.0,1.0,0.0,1.0
+dga_Latn,59,59,2,0,27562,0.967213,1.0,0.983333,0.999927,72.55841,1.0
+dip_Latn,57,0,0,57,27566,0.0,0.0,0.0,1.0,0.0,0.0
+div_Thaa,57,57,0,0,27566,1.0,1.0,1.0,1.0,0.0,1.0
+dje_Latn,54,44,7,10,27562,0.862745,0.814815,0.838095,0.999746,253.908375,0.814815
+duu_Latn,57,0,0,57,27566,0.0,0.0,0.0,1.0,0.0,0.0
+dyo_Latn,55,54,0,1,27568,1.0,0.981818,0.990826,1.0,0.0,0.981818
+dyu_Latn,58,0,24,58,27541,0.0,0.0,0.0,0.999129,870.669327,0.0
+dzo_Tibt,59,58,2,1,27562,0.966667,0.983051,0.97479,0.999927,72.55841,0.983051
+ekk_Latn,60,60,1,0,27562,0.983607,1.0,0.991736,0.999964,36.280521,1.0
+ell_Grek,115,58,0,57,27508,1.0,0.504348,0.67052,1.0,0.0,0.504348
+emk_Latn,58,0,0,58,27565,0.0,0.0,0.0,1.0,0.0,0.0
+eng_Latn,58,58,3,0,27562,0.95082,1.0,0.97479,0.999891,108.833666,1.0
+epo_Latn,58,57,0,1,27565,1.0,0.982759,0.991304,1.0,0.0,0.982759
+ese_Latn,35,35,0,0,27588,1.0,1.0,1.0,1.0,0.0,1.0
+eus_Latn,59,59,11,0,27553,0.842857,1.0,0.914729,0.999601,399.071252,1.0
+eve_Cyrl,49,49,140,0,27434,0.259259,1.0,0.411765,0.994923,5077.246682,1.0
+evn_Cyrl,57,0,0,57,27566,0.0,0.0,0.0,1.0,0.0,0.0
+ewe_Latn,59,59,9,0,27555,0.867647,1.0,0.929134,0.999673,326.512843,1.0
+fao_Latn,57,57,0,0,27566,1.0,1.0,1.0,1.0,0.0,1.0
+fas_Arab,114,112,0,2,27509,1.0,0.982456,0.99115,1.0,0.0,0.982456
+fat_Latn,57,57,0,0,27566,1.0,1.0,1.0,1.0,0.0,1.0
+fij_Latn,59,59,0,0,27564,1.0,1.0,1.0,1.0,0.0,1.0
+fil_Latn,60,60,0,0,27563,1.0,1.0,1.0,1.0,0.0,1.0
+fin_Latn,61,61,0,0,27562,1.0,1.0,1.0,1.0,0.0,1.0
+fkv_Latn,58,27,1,31,27564,0.964286,0.465517,0.627907,0.999964,36.277889,0.465517
+fon_Latn,58,56,6,2,27559,0.903226,0.965517,0.933333,0.999782,217.667332,0.965517
+fra_Latn,58,44,1,14,27564,0.977778,0.758621,0.854369,0.999964,36.277889,0.758621
+fry_Latn,58,58,0,0,27565,1.0,1.0,1.0,1.0,0.0,1.0
+fuf_Adlm,58,0,0,58,27565,0.0,0.0,0.0,1.0,0.0,0.0
+fuf_Latn,58,48,3,10,27562,0.941176,0.827586,0.880734,0.999891,108.833666,0.827586
+fur_Latn,59,58,3,1,27561,0.95082,0.983051,0.966667,0.999891,108.837614,0.983051
+fuv_Latn,108,56,12,52,27503,0.823529,0.518519,0.636364,0.999564,436.12575,0.518519
+fvr_Latn,57,0,0,57,27566,0.0,0.0,0.0,1.0,0.0,0.0
+gaa_Latn,59,59,4,0,27560,0.936508,1.0,0.967213,0.999855,145.116819,1.0
+gag_Latn,59,57,1,2,27563,0.982759,0.966102,0.974359,0.999964,36.279205,0.966102
+gan_Hani,49,0,0,49,27574,0.0,0.0,0.0,1.0,0.0,0.0
+gaz_Latn,60,57,8,3,27555,0.876923,0.95,0.912,0.99971,290.244168,0.95
+gej_Latn,58,0,0,58,27565,0.0,0.0,0.0,1.0,0.0,0.0
+gjn_Latn,60,59,1,1,27562,0.983333,0.983333,0.983333,0.999964,36.280521,0.983333
+gkp_Latn,56,0,0,56,27567,0.0,0.0,0.0,1.0,0.0,0.0
+gla_Latn,62,61,0,1,27561,1.0,0.983871,0.99187,1.0,0.0,0.983871
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reports/udhr-all/millid_summary.json

@@ -0,0 +1,17 @@
+{
+  "top1_accuracy": 0.7273648770951743,
+  "top3_accuracy": 0.7808710132860297,
+  "top5_accuracy": 0.7928175795532708,
+  "passages": 27623,
+  "languages": 429,
+  "macro_precision": 0.7340309394117082,
+  "macro_recall": 0.7290411714617072,
+  "macro_f1": 0.7148579318843858,
+  "macro_specificity": 0.9997903094250377,
+  "weighted_f1": 0.7196404590366388,
+  "median_f1": 0.9649122807017544,
+  "languages_f1_at_least_0_9": 254,
+  "languages_f1_below_0_5": 105,
+  "mean_fp_per_million": 209.69057496245543,
+  "micro_f1": 0.7273648770951743
+}

reports/udhr-common/fasttext@millid_per_language.csv

@@ -0,0 +1,171 @@
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reports/udhr-common/fasttext@millid_summary.json

@@ -0,0 +1,17 @@
+{
+  "top1_accuracy": 0.9194794290512175,
+  "top3_accuracy": 0.964735516372796,
+  "top5_accuracy": 0.974223341729639,
+  "passages": 11910,
+  "languages": 170,
+  "macro_precision": 0.9473808585265043,
+  "macro_recall": 0.9440916022324787,
+  "macro_f1": 0.9328542536140149,
+  "macro_specificity": 0.9996006150778249,
+  "weighted_f1": 0.9181958923496206,
+  "median_f1": 1.0,
+  "languages_f1_at_least_0_9": 142,
+  "languages_f1_below_0_5": 6,
+  "mean_fp_per_million": 399.3849221751375,
+  "micro_f1": 0.9194794290512175
+}

reports/udhr-common/fasttext_per_language.csv

@@ -0,0 +1,171 @@
+label,support,tp,fp,fn,tn,precision,recall,f1,specificity,fp_per_million,sensitivity
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reports/udhr-common/fasttext_summary.json

@@ -0,0 +1,17 @@
+{
+  "top1_accuracy": 0.9187237615449202,
+  "top3_accuracy": 0.964735516372796,
+  "top5_accuracy": 0.9740554156171285,
+  "passages": 11910,
+  "languages": 170,
+  "macro_precision": 0.9473808585265043,
+  "macro_recall": 0.9431462240812183,
+  "macro_f1": 0.9323102708833079,
+  "macro_specificity": 0.9996006150778249,
+  "weighted_f1": 0.917761071560676,
+  "median_f1": 1.0,
+  "languages_f1_at_least_0_9": 141,
+  "languages_f1_below_0_5": 6,
+  "mean_fp_per_million": 399.3849221751375,
+  "micro_f1": 0.9187237615449202
+}

reports/udhr-common/glotlid@millid_per_language.csv

@@ -0,0 +1,171 @@
+label,support,tp,fp,fn,tn,precision,recall,f1,specificity,fp_per_million,sensitivity
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+dyu_Latn,58,18,7,40,11845,0.72,0.310345,0.433735,0.999409,590.617617,0.310345
+dzo_Tibt,59,58,10,1,11841,0.852941,0.983051,0.913386,0.999156,843.810649,0.983051
+ekk_Latn,60,60,0,0,11850,1.0,1.0,1.0,1.0,0.0,1.0
+ell_Grek,115,115,0,0,11795,1.0,1.0,1.0,1.0,0.0,1.0
+eng_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+epo_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+eus_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+ewe_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+fao_Latn,57,57,0,0,11853,1.0,1.0,1.0,1.0,0.0,1.0
+fas_Arab,114,114,0,0,11796,1.0,1.0,1.0,1.0,0.0,1.0
+fij_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+fil_Latn,60,60,0,0,11850,1.0,1.0,1.0,1.0,0.0,1.0
+fin_Latn,61,61,0,0,11849,1.0,1.0,1.0,1.0,0.0,1.0
+fon_Latn,58,57,0,1,11852,1.0,0.982759,0.991304,1.0,0.0,0.982759
+fra_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+fur_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+fuv_Latn,108,63,0,45,11802,1.0,0.583333,0.736842,1.0,0.0,0.583333
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+gle_Latn,56,56,2,0,11852,0.965517,1.0,0.982456,0.999831,168.71942,1.0
+glg_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+gug_Latn,49,49,0,0,11861,1.0,1.0,1.0,1.0,0.0,1.0
+guj_Gujr,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+hat_Latn,117,114,2,3,11791,0.982759,0.974359,0.978541,0.99983,169.592131,0.974359
+hau_Latn,155,155,0,0,11755,1.0,1.0,1.0,1.0,0.0,1.0
+heb_Hebr,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+hin_Deva,59,56,2,3,11849,0.965517,0.949153,0.957265,0.999831,168.76213,0.949153
+hrv_Latn,58,56,28,2,11824,0.666667,0.965517,0.788732,0.997638,2362.470469,0.965517
+hun_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+hye_Armn,70,70,0,0,11840,1.0,1.0,1.0,1.0,0.0,1.0
+ibo_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+ilo_Latn,58,57,0,1,11852,1.0,0.982759,0.991304,1.0,0.0,0.982759
+ind_Latn,60,51,1,9,11849,0.980769,0.85,0.910714,0.999916,84.388186,0.85
+isl_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+ita_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+jav_Latn,60,59,0,1,11850,1.0,0.983333,0.991597,1.0,0.0,0.983333
+jpn_Jpan,140,140,0,0,11770,1.0,1.0,1.0,1.0,0.0,1.0
+kab_Latn,58,57,0,1,11852,1.0,0.982759,0.991304,1.0,0.0,0.982759
+kal_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+kan_Knda,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+kat_Geor,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+kaz_Cyrl,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+kbp_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+kea_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+khk_Cyrl,57,56,0,1,11853,1.0,0.982456,0.99115,1.0,0.0,0.982456
+khm_Khmr,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+kin_Latn,59,47,0,12,11851,1.0,0.79661,0.886792,1.0,0.0,0.79661
+kir_Cyrl,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+kmb_Latn,116,116,0,0,11794,1.0,1.0,1.0,1.0,0.0,1.0
+kmr_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+knc_Latn,64,64,0,0,11846,1.0,1.0,1.0,1.0,0.0,1.0
+kng_Latn,115,16,0,99,11795,1.0,0.13913,0.244275,1.0,0.0,0.13913
+kor_Hang,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
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+lij_Latn,58,58,18,0,11834,0.763158,1.0,0.865672,0.998481,1518.731016,1.0
+lin_Latn,110,110,0,0,11800,1.0,1.0,1.0,1.0,0.0,1.0
+lit_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+ltz_Latn,59,58,0,1,11851,1.0,0.983051,0.991453,1.0,0.0,0.983051
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+mag_Deva,53,51,4,2,11853,0.927273,0.962264,0.944444,0.999663,337.353462,0.962264
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+mkd_Cyrl,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+mlt_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
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+mya_Mymr,56,56,0,0,11854,1.0,1.0,1.0,1.0,0.0,1.0
+nav_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+nld_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+nno_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+nob_Latn,62,62,2,0,11846,0.96875,1.0,0.984127,0.999831,168.804862,1.0
+npi_Deva,55,54,0,1,11855,1.0,0.981818,0.990826,1.0,0.0,0.981818
+nso_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+nya_Latn,116,116,0,0,11794,1.0,1.0,1.0,1.0,0.0,1.0
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+pcm_Latn,56,56,0,0,11854,1.0,1.0,1.0,1.0,0.0,1.0
+plt_Latn,53,53,0,0,11857,1.0,1.0,1.0,1.0,0.0,1.0
+pol_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+por_Latn,115,115,1,0,11794,0.991379,1.0,0.995671,0.999915,84.781687,1.0
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+rus_Cyrl,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
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+sin_Sinh,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+slk_Latn,56,56,0,0,11854,1.0,1.0,1.0,1.0,0.0,1.0
+slv_Latn,59,59,1,0,11850,0.983333,1.0,0.991597,0.999916,84.381065,1.0
+smo_Latn,63,63,0,0,11847,1.0,1.0,1.0,1.0,0.0,1.0
+sna_Latn,60,60,0,0,11850,1.0,1.0,1.0,1.0,0.0,1.0
+som_Latn,57,57,0,0,11853,1.0,1.0,1.0,1.0,0.0,1.0
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+spa_Latn,78,77,0,1,11832,1.0,0.987179,0.993548,1.0,0.0,0.987179
+srp_Cyrl,59,58,0,1,11851,1.0,0.983051,0.991453,1.0,0.0,0.983051
+ssw_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+sun_Latn,66,65,0,1,11844,1.0,0.984848,0.992366,1.0,0.0,0.984848
+swe_Latn,61,61,0,0,11849,1.0,1.0,1.0,1.0,0.0,1.0
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+tat_Cyrl,57,57,0,0,11853,1.0,1.0,1.0,1.0,0.0,1.0
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+ton_Latn,60,60,0,0,11850,1.0,1.0,1.0,1.0,0.0,1.0
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+uzn_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+vec_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
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+ydd_Hebr,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
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+yue_Hani,49,20,0,29,11861,1.0,0.408163,0.57971,1.0,0.0,0.408163
+zul_Latn,58,57,0,1,11852,1.0,0.982759,0.991304,1.0,0.0,0.982759

reports/udhr-common/glotlid@millid_summary.json

@@ -0,0 +1,17 @@
+{
+  "top1_accuracy": 0.9338371116708648,
+  "top3_accuracy": 0.9682619647355164,
+  "top5_accuracy": 0.9735516372795969,
+  "passages": 11910,
+  "languages": 170,
+  "macro_precision": 0.9887806114106193,
+  "macro_recall": 0.9578604445634422,
+  "macro_f1": 0.9658801542070193,
+  "macro_specificity": 0.9999338492838657,
+  "weighted_f1": 0.9468741038422634,
+  "median_f1": 1.0,
+  "languages_f1_at_least_0_9": 156,
+  "languages_f1_below_0_5": 3,
+  "mean_fp_per_million": 66.15071613431165,
+  "micro_f1": 0.9338371116708648
+}

reports/udhr-common/glotlid_per_language.csv

@@ -0,0 +1,171 @@
+label,support,tp,fp,fn,tn,precision,recall,f1,specificity,fp_per_million,sensitivity
+abk_Cyrl,57,57,0,0,11853,1.0,1.0,1.0,1.0,0.0,1.0
+ace_Latn,61,60,0,1,11849,1.0,0.983607,0.991736,1.0,0.0,0.983607
+ady_Cyrl,56,54,0,2,11854,1.0,0.964286,0.981818,1.0,0.0,0.964286
+afr_Latn,60,60,0,0,11850,1.0,1.0,1.0,1.0,0.0,1.0
+als_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+alt_Cyrl,51,51,0,0,11859,1.0,1.0,1.0,1.0,0.0,1.0
+amh_Ethi,51,51,0,0,11859,1.0,1.0,1.0,1.0,0.0,1.0
+arb_Arab,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+arn_Latn,58,47,0,11,11852,1.0,0.810345,0.895238,1.0,0.0,0.810345
+ast_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+ayr_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+azj_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+bam_Latn,57,50,26,7,11827,0.657895,0.877193,0.75188,0.997806,2193.537501,0.877193
+ban_Latn,60,60,0,0,11850,1.0,1.0,1.0,1.0,0.0,1.0
+bel_Cyrl,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+bem_Latn,57,57,0,0,11853,1.0,1.0,1.0,1.0,0.0,1.0
+ben_Beng,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+bho_Deva,56,49,0,7,11854,1.0,0.875,0.933333,1.0,0.0,0.875
+bis_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+bod_Tibt,60,50,1,10,11849,0.980392,0.833333,0.900901,0.999916,84.388186,0.833333
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+bug_Latn,60,60,0,0,11850,1.0,1.0,1.0,1.0,0.0,1.0
+bul_Cyrl,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
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+ces_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+chv_Cyrl,25,25,0,0,11885,1.0,1.0,1.0,1.0,0.0,1.0
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+cmn_Hani,319,293,12,26,11579,0.960656,0.918495,0.939103,0.998965,1035.285998,0.918495
+crh_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+cym_Latn,60,60,0,0,11850,1.0,1.0,1.0,1.0,0.0,1.0
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+dzo_Tibt,59,58,10,1,11841,0.852941,0.983051,0.913386,0.999156,843.810649,0.983051
+ekk_Latn,60,60,0,0,11850,1.0,1.0,1.0,1.0,0.0,1.0
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+eng_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+epo_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+eus_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+ewe_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+fao_Latn,57,57,0,0,11853,1.0,1.0,1.0,1.0,0.0,1.0
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+fij_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
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+fuv_Latn,108,62,0,46,11802,1.0,0.574074,0.729412,1.0,0.0,0.574074
+gaz_Latn,60,59,0,1,11850,1.0,0.983333,0.991597,1.0,0.0,0.983333
+gla_Latn,62,60,0,2,11848,1.0,0.967742,0.983607,1.0,0.0,0.967742
+gle_Latn,56,56,2,0,11852,0.965517,1.0,0.982456,0.999831,168.71942,1.0
+glg_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+gug_Latn,49,49,0,0,11861,1.0,1.0,1.0,1.0,0.0,1.0
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+hau_Latn,155,155,0,0,11755,1.0,1.0,1.0,1.0,0.0,1.0
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+hin_Deva,59,56,2,3,11849,0.965517,0.949153,0.957265,0.999831,168.76213,0.949153
+hrv_Latn,58,56,28,2,11824,0.666667,0.965517,0.788732,0.997638,2362.470469,0.965517
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+ibo_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+ilo_Latn,58,57,0,1,11852,1.0,0.982759,0.991304,1.0,0.0,0.982759
+ind_Latn,60,51,1,9,11849,0.980769,0.85,0.910714,0.999916,84.388186,0.85
+isl_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
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+jav_Latn,60,59,0,1,11850,1.0,0.983333,0.991597,1.0,0.0,0.983333
+jpn_Jpan,140,140,0,0,11770,1.0,1.0,1.0,1.0,0.0,1.0
+kab_Latn,58,57,0,1,11852,1.0,0.982759,0.991304,1.0,0.0,0.982759
+kal_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
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+kat_Geor,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
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+kbp_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+kea_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+khk_Cyrl,57,56,0,1,11853,1.0,0.982456,0.99115,1.0,0.0,0.982456
+khm_Khmr,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+kin_Latn,59,47,0,12,11851,1.0,0.79661,0.886792,1.0,0.0,0.79661
+kir_Cyrl,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+kmb_Latn,116,116,0,0,11794,1.0,1.0,1.0,1.0,0.0,1.0
+kmr_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+knc_Latn,64,64,0,0,11846,1.0,1.0,1.0,1.0,0.0,1.0
+kng_Latn,115,16,0,99,11795,1.0,0.13913,0.244275,1.0,0.0,0.13913
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+lij_Latn,58,58,18,0,11834,0.763158,1.0,0.865672,0.998481,1518.731016,1.0
+lin_Latn,110,110,0,0,11800,1.0,1.0,1.0,1.0,0.0,1.0
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+ltz_Latn,59,58,0,1,11851,1.0,0.983051,0.991453,1.0,0.0,0.983051
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+lvs_Latn,115,115,0,0,11795,1.0,1.0,1.0,1.0,0.0,1.0
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+mkd_Cyrl,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+mlt_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+mos_Latn,56,56,0,0,11854,1.0,1.0,1.0,1.0,0.0,1.0
+mri_Latn,57,57,0,0,11853,1.0,1.0,1.0,1.0,0.0,1.0
+mya_Mymr,56,56,0,0,11854,1.0,1.0,1.0,1.0,0.0,1.0
+nav_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+nld_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+nno_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+nob_Latn,62,62,2,0,11846,0.96875,1.0,0.984127,0.999831,168.804862,1.0
+npi_Deva,55,54,0,1,11855,1.0,0.981818,0.990826,1.0,0.0,0.981818
+nso_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+nya_Latn,116,116,0,0,11794,1.0,1.0,1.0,1.0,0.0,1.0
+oci_Latn,398,92,0,306,11512,1.0,0.231156,0.37551,1.0,0.0,0.231156
+oss_Cyrl,44,44,0,0,11866,1.0,1.0,1.0,1.0,0.0,1.0
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+pap_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+pcm_Latn,56,55,0,1,11854,1.0,0.982143,0.990991,1.0,0.0,0.982143
+plt_Latn,53,53,0,0,11857,1.0,1.0,1.0,1.0,0.0,1.0
+pol_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
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+ron_Latn,97,97,0,0,11813,1.0,1.0,1.0,1.0,0.0,1.0
+run_Latn,58,58,12,0,11840,0.828571,1.0,0.90625,0.998988,1012.487344,1.0
+rus_Cyrl,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+sag_Latn,59,24,0,35,11851,1.0,0.40678,0.578313,1.0,0.0,0.40678
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+sin_Sinh,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+slk_Latn,56,56,0,0,11854,1.0,1.0,1.0,1.0,0.0,1.0
+slv_Latn,59,59,1,0,11850,0.983333,1.0,0.991597,0.999916,84.381065,1.0
+smo_Latn,63,63,0,0,11847,1.0,1.0,1.0,1.0,0.0,1.0
+sna_Latn,60,60,0,0,11850,1.0,1.0,1.0,1.0,0.0,1.0
+som_Latn,57,57,0,0,11853,1.0,1.0,1.0,1.0,0.0,1.0
+sot_Latn,57,57,0,0,11853,1.0,1.0,1.0,1.0,0.0,1.0
+spa_Latn,78,77,0,1,11832,1.0,0.987179,0.993548,1.0,0.0,0.987179
+srp_Cyrl,59,58,0,1,11851,1.0,0.983051,0.991453,1.0,0.0,0.983051
+ssw_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+sun_Latn,66,65,0,1,11844,1.0,0.984848,0.992366,1.0,0.0,0.984848
+swe_Latn,61,61,0,0,11849,1.0,1.0,1.0,1.0,0.0,1.0
+swh_Latn,58,56,0,2,11852,1.0,0.965517,0.982456,1.0,0.0,0.965517
+tah_Latn,57,39,0,18,11853,1.0,0.684211,0.8125,1.0,0.0,0.684211
+tam_Taml,68,68,0,0,11842,1.0,1.0,1.0,1.0,0.0,1.0
+tat_Cyrl,57,57,0,0,11853,1.0,1.0,1.0,1.0,0.0,1.0
+tel_Telu,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
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+tha_Thai,114,114,0,0,11796,1.0,1.0,1.0,1.0,0.0,1.0
+tir_Ethi,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+ton_Latn,60,60,0,0,11850,1.0,1.0,1.0,1.0,0.0,1.0
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+urd_Arab,108,106,0,2,11802,1.0,0.981481,0.990654,1.0,0.0,0.981481
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+yue_Hani,49,20,0,29,11861,1.0,0.408163,0.57971,1.0,0.0,0.408163
+zul_Latn,58,57,0,1,11852,1.0,0.982759,0.991304,1.0,0.0,0.982759

reports/udhr-common/glotlid_summary.json

@@ -0,0 +1,17 @@
+{
+  "top1_accuracy": 0.9335852225020991,
+  "top3_accuracy": 0.9677581863979848,
+  "top5_accuracy": 0.973215785054576,
+  "passages": 11910,
+  "languages": 170,
+  "macro_precision": 0.9887806114106193,
+  "macro_recall": 0.9575995164374027,
+  "macro_f1": 0.9657221446088623,
+  "macro_specificity": 0.9999338492838657,
+  "weighted_f1": 0.9467136106728533,
+  "median_f1": 1.0,
+  "languages_f1_at_least_0_9": 155,
+  "languages_f1_below_0_5": 3,
+  "mean_fp_per_million": 66.15071613431165,
+  "micro_f1": 0.9335852225020991
+}

reports/udhr-common/leaderboard.csv

@@ -0,0 +1,171 @@
+label,language,support,millid_f1,millid_precision,millid_recall,glotlid_f1,glotlid_precision,glotlid_recall,glotlid@millid_f1,glotlid@millid_precision,glotlid@millid_recall,fasttext_f1,fasttext_precision,fasttext_recall,fasttext@millid_f1,fasttext@millid_precision,fasttext@millid_recall,delta,winner,delta_fasttext,winner_fasttext
+roh_Latn,Romansh,399,0.995,1.0,0.99,1.0,1.0,1.0,1.0,1.0,1.0,0.9963,0.9925,1.0,0.9963,0.9925,1.0,-0.005,tie,-0.0013,tie
+oci_Latn,Occitan,398,0.2771,1.0,0.1608,0.3755,1.0,0.2312,0.3755,1.0,0.2312,0.5527,1.0,0.3819,0.5527,1.0,0.3819,-0.0985,GlotLID,-0.2757,fastText
+cmn_Hani,Chinese (Mandarin),319,0.9577,0.9562,0.9592,0.9391,0.9607,0.9185,0.9391,0.9607,0.9185,0.5254,0.8881,0.373,0.5254,0.8881,0.373,0.0186,MillID,0.4324,MillID
+hau_Latn,Hausa,155,0.9968,1.0,0.9935,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0032,tie,-0.0032,tie
+jpn_Jpan,Japanese,140,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.971,0.9853,0.9571,0.971,0.9853,0.9571,0.0,tie,0.029,MillID
+hat_Latn,Haitian Creole,117,0.9915,0.9915,0.9915,0.9785,0.9828,0.9744,0.9785,0.9828,0.9744,0.8755,0.7838,0.9915,0.8755,0.7838,0.9915,0.0129,MillID,0.116,MillID
+kmb_Latn,Kimbundu,116,0.9702,0.958,0.9828,1.0,1.0,1.0,1.0,1.0,1.0,0.9447,0.9328,0.9569,0.9447,0.9328,0.9569,-0.0298,GlotLID,0.0255,MillID
+twi_Latn,Twi,116,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9872,0.9748,1.0,0.9872,0.9748,1.0,0.0,tie,0.0128,MillID
+nya_Latn,Nyanja,116,0.9913,1.0,0.9828,1.0,1.0,1.0,1.0,1.0,1.0,0.9915,0.9831,1.0,0.9915,0.9831,1.0,-0.0087,tie,-0.0001,tie
+ell_Grek,Greek,115,0.6705,1.0,0.5043,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.3295,GlotLID,-0.3295,fastText
+lvs_Latn,Standard Latvian,115,0.9868,1.0,0.9739,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0132,GlotLID,-0.0132,fastText
+por_Latn,Portuguese,115,0.9554,0.9817,0.9304,0.9957,0.9914,1.0,0.9957,0.9914,1.0,0.8779,0.7823,1.0,0.8779,0.7823,1.0,-0.0403,GlotLID,0.0775,MillID
+kng_Latn,Koongo,115,0.0833,1.0,0.0435,0.2443,1.0,0.1391,0.2443,1.0,0.1391,0.7701,1.0,0.6261,0.7701,1.0,0.6261,-0.1609,GlotLID,-0.6867,fastText
+cjk_Latn,Chokwe,114,0.8713,1.0,0.7719,1.0,1.0,1.0,1.0,1.0,1.0,0.9821,1.0,0.9649,0.9821,1.0,0.9649,-0.1287,GlotLID,-0.1109,fastText
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+tha_Thai,Thai,114,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
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+fuv_Latn,Nigerian Fulfulde,108,0.6829,1.0,0.5185,0.7294,1.0,0.5741,0.7368,1.0,0.5833,0.9474,0.9,1.0,0.9474,0.9,1.0,-0.0539,GlotLID,-0.2644,fastText
+urd_Arab,Urdu,108,0.9907,1.0,0.9815,0.9907,1.0,0.9815,0.9907,1.0,0.9815,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,-0.0093,tie
+umb_Latn,Umbundu,107,0.9662,1.0,0.9346,0.9906,1.0,0.9813,0.9906,1.0,0.9813,0.9858,1.0,0.972,0.9858,1.0,0.972,-0.0244,GlotLID,-0.0196,fastText
+mal_Mlym,Malayalam,100,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+ron_Latn,Romanian,97,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9948,1.0,0.9897,0.9948,1.0,0.9897,0.0,tie,0.0052,tie
+spa_Latn,Spanish,78,0.9804,1.0,0.9615,0.9935,1.0,0.9872,0.9935,1.0,0.9872,1.0,1.0,1.0,1.0,1.0,1.0,-0.0132,GlotLID,-0.0196,fastText
+hye_Armn,Armenian,70,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9855,1.0,0.9714,0.9855,1.0,0.9714,0.0,tie,0.0145,MillID
+tam_Taml,Tamil,68,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+deu_Latn,German,67,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+sun_Latn,Sundanese,66,0.9924,1.0,0.9848,0.9924,1.0,0.9848,0.9924,1.0,0.9848,0.9774,0.9701,0.9848,0.9774,0.9701,0.9848,0.0,tie,0.0149,MillID
+knc_Latn,Central Kanuri,64,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+smo_Latn,Samoan,63,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9037,0.8472,0.9683,0.9037,0.8472,0.9683,0.0,tie,0.0963,MillID
+nob_Latn,Norwegian Bokmål,62,0.9134,0.8923,0.9355,0.9841,0.9688,1.0,0.9841,0.9688,1.0,0.992,0.9841,1.0,0.992,0.9841,1.0,-0.0707,GlotLID,-0.0786,fastText
+gla_Latn,Scottish Gaelic,62,0.9919,1.0,0.9839,0.9836,1.0,0.9677,0.9836,1.0,0.9677,0.9667,1.0,0.9355,0.9667,1.0,0.9355,0.0083,tie,0.0252,MillID
+fin_Latn,Finnish,61,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+swe_Latn,Swedish,61,0.9917,1.0,0.9836,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0083,tie,-0.0083,tie
+ace_Latn,Acehnese,61,1.0,1.0,1.0,0.9917,1.0,0.9836,0.9917,1.0,0.9836,0.9023,0.8333,0.9836,0.9023,0.8333,0.9836,0.0083,tie,0.0977,MillID
+dan_Latn,Danish,61,0.9431,0.9355,0.9508,0.9833,1.0,0.9672,0.9833,1.0,0.9672,0.9917,1.0,0.9836,0.9917,1.0,0.9836,-0.0402,GlotLID,-0.0486,fastText
+cym_Latn,Welsh,60,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+ekk_Latn,Estonian,60,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+bod_Tibt,Tibetan,60,0.9748,0.9831,0.9667,0.9009,0.9804,0.8333,0.9009,0.9804,0.8333,0.8992,0.8406,0.9667,0.8992,0.8406,0.9667,0.0739,MillID,0.0756,MillID
+bug_Latn,Buginese,60,0.9381,1.0,0.8833,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0619,GlotLID,-0.0619,fastText
+ban_Latn,Balinese,60,0.9744,1.0,0.95,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0256,GlotLID,-0.0256,fastText
+afr_Latn,Afrikaans,60,0.9916,1.0,0.9833,1.0,1.0,1.0,1.0,1.0,1.0,0.9916,1.0,0.9833,0.9916,1.0,0.9833,-0.0084,tie,0.0,tie
+gaz_Latn,Oromo (West Central),60,0.9744,1.0,0.95,0.9916,1.0,0.9833,0.9916,1.0,0.9833,1.0,1.0,1.0,1.0,1.0,1.0,-0.0172,GlotLID,-0.0256,fastText
+fil_Latn,Filipino,60,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+wol_Latn,Wolof,60,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9917,0.9836,1.0,0.9917,0.9836,1.0,0.0,tie,0.0083,tie
+war_Latn,Waray,60,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9916,1.0,0.9833,0.9916,1.0,0.9833,0.0,tie,0.0084,tie
+vie_Latn,Vietnamese,60,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+ton_Latn,Tongan,60,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,tie,1.0,MillID
+quy_Latn,Ayacucho Quechua,60,0.9831,1.0,0.9667,0.9916,1.0,0.9833,0.9916,1.0,0.9833,0.9916,1.0,0.9833,0.9916,1.0,0.9833,-0.0085,tie,-0.0085,tie
+sna_Latn,Shona,60,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9744,1.0,0.95,0.9744,1.0,0.95,0.0,tie,0.0256,MillID
+ind_Latn,Indonesian,60,0.7677,0.9744,0.6333,0.9107,0.9808,0.85,0.9107,0.9808,0.85,0.8966,0.9286,0.8667,0.8966,0.9286,0.8667,-0.143,GlotLID,-0.1289,fastText
+jav_Latn,Javanese,60,0.9833,0.9833,0.9833,0.9916,1.0,0.9833,0.9916,1.0,0.9833,0.9916,1.0,0.9833,0.9916,1.0,0.9833,-0.0083,tie,-0.0083,tie
+tuk_Latn,Turkmen,59,0.9828,1.0,0.9661,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0172,GlotLID,-0.0172,fastText
+ydd_Hebr,Eastern Yiddish,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+slv_Latn,Slovenian,59,0.9744,0.9828,0.9661,0.9916,0.9833,1.0,0.9916,0.9833,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0172,GlotLID,-0.0256,fastText
+sag_Latn,Sango,59,0.9915,1.0,0.9831,0.5783,1.0,0.4068,0.5783,1.0,0.4068,0.2121,1.0,0.1186,0.2121,1.0,0.1186,0.4131,MillID,0.7793,MillID
+sin_Sinh,Sinhala,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+pol_Latn,Polish,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.0,tie,0.0085,tie
+fur_Latn,Friulian,59,0.9748,0.9667,0.9831,1.0,1.0,1.0,1.0,1.0,1.0,0.7973,0.6629,1.0,0.7973,0.6629,1.0,-0.0252,GlotLID,0.1775,MillID
+srp_Cyrl,Serbian,59,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.0,tie,0.0,tie
+kbp_Latn,Kabiyè,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.0,tie,0.0085,tie
+khm_Khmr,Khmer,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+nav_Latn,Navajo,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+ewe_Latn,Ewe,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.0,tie,0.0085,tie
+eus_Latn,Basque,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.0,tie,0.0085,tie
+dzo_Tibt,Dzongkha,59,0.9748,0.9667,0.9831,0.9134,0.8529,0.9831,0.9134,0.8529,0.9831,0.8807,0.96,0.8136,0.8807,0.96,0.8136,0.0614,MillID,0.0941,MillID
+als_Latn,Albanian (Tosk),59,0.9915,1.0,0.9831,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0085,tie,-0.0085,tie
+ceb_Latn,Cebuano,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+bis_Latn,Bislama,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+bos_Latn,Bosnian,59,0.3291,0.65,0.2203,0.6136,0.931,0.4576,0.6136,0.931,0.4576,0.6154,0.875,0.4746,0.6154,0.875,0.4746,-0.2845,GlotLID,-0.2863,fastText
+ayr_Latn,Central Aymara,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.0,tie,0.0085,tie
+crh_Latn,Crimean Tatar,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+tur_Latn,Turkish,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+ssw_Latn,Swati,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+hun_Latn,Hungarian,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9833,0.9672,1.0,0.9833,0.9672,1.0,0.0,tie,0.0167,MillID
+fij_Latn,Fijian,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9916,0.9833,1.0,0.9916,0.9833,1.0,0.0,tie,0.0084,tie
+ltz_Latn,Luxembourgish,59,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.9915,1.0,0.9831,0.9431,0.9062,0.9831,0.9431,0.9062,0.9831,0.0,tie,0.0484,MillID
+kin_Latn,Kinyarwanda,59,0.8039,0.9535,0.6949,0.8868,1.0,0.7966,0.8868,1.0,0.7966,0.9189,0.9808,0.8644,0.9189,0.9808,0.8644,-0.0829,GlotLID,-0.115,fastText
+hin_Deva,Hindi,59,0.7234,0.9714,0.5763,0.9573,0.9655,0.9492,0.9573,0.9655,0.9492,0.9752,0.9516,1.0,0.9752,0.9516,1.0,-0.2339,GlotLID,-0.2518,fastText
+mlt_Latn,Maltese,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+min_Latn,Minangkabau,59,1.0,1.0,1.0,0.9916,0.9833,1.0,0.9916,0.9833,1.0,0.127,1.0,0.0678,0.127,1.0,0.0678,0.0084,tie,0.873,MillID
+mar_Deva,Marathi,59,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+xho_Latn,Xhosa,58,0.9565,0.9649,0.9483,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0435,GlotLID,-0.0435,fastText
+vec_Latn,Venetian,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9355,0.8788,1.0,0.9355,0.8788,1.0,0.0,tie,0.0645,MillID
+ukr_Cyrl,Ukrainian,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+tsn_Latn,Tswana,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9355,0.8788,1.0,0.9355,0.8788,1.0,0.0,tie,0.0645,MillID
+tel_Telu,Telugu,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+dyu_Latn,Dyula,58,0.0,0.0,0.0,0.4337,0.72,0.3103,0.4337,0.72,0.3103,0.0303,0.125,0.0172,0.0303,0.125,0.0172,-0.4337,GlotLID,-0.0303,fastText
+eng_Latn,English,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9748,0.9508,1.0,0.9748,0.9508,1.0,0.0,tie,0.0252,MillID
+epo_Latn,Esperanto,58,0.9913,1.0,0.9828,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0087,tie,-0.0087,tie
+ces_Latn,Czech,58,0.9825,1.0,0.9655,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0175,GlotLID,-0.0175,fastText
+ast_Latn,Asturian,58,0.9735,1.0,0.9483,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0265,GlotLID,-0.0265,fastText
+arn_Latn,Mapuche,58,0.9825,1.0,0.9655,0.8952,1.0,0.8103,0.9057,1.0,0.8276,0.9825,1.0,0.9655,0.9825,1.0,0.9655,0.0768,MillID,0.0,tie
+arb_Arab,Arabic (Modern Standard),58,0.8846,1.0,0.7931,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.1154,GlotLID,-0.1154,fastText
+azj_Latn,North Azerbaijani,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+tpi_Latn,Tok Pisin,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+tir_Ethi,Tigrinya,58,0.9915,0.9831,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0085,tie,-0.0085,tie
+uig_Arab,Uyghur,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+zul_Latn,Zulu,58,0.9189,0.9623,0.8793,0.9913,1.0,0.9828,0.9913,1.0,0.9828,0.9915,0.9831,1.0,0.9915,0.9831,1.0,-0.0724,GlotLID,-0.0725,fastText
+bul_Cyrl,Bulgarian,58,0.9828,0.9828,0.9828,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0172,GlotLID,-0.0172,fastText
+cat_Latn,Catalan,58,0.9748,0.9508,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9831,0.9667,1.0,0.9831,0.9667,1.0,-0.0252,GlotLID,-0.0083,tie
+ben_Beng,Bangla,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+bel_Cyrl,Belarusian,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+mkd_Cyrl,Macedonian,58,0.9915,0.9831,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0085,tie,-0.0085,tie
+ilo_Latn,Iloko,58,1.0,1.0,1.0,0.9913,1.0,0.9828,0.9913,1.0,0.9828,1.0,1.0,1.0,1.0,1.0,1.0,0.0087,tie,0.0,tie
+ibo_Latn,Igbo,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+uzn_Latn,Northern Uzbek,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+pan_Guru,Punjabi,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+nno_Latn,Norwegian Nynorsk,58,0.9558,0.9818,0.931,1.0,1.0,1.0,1.0,1.0,1.0,0.9915,0.9831,1.0,0.9915,0.9831,1.0,-0.0442,GlotLID,-0.0357,fastText
+nso_Latn,Northern Sotho,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9748,0.9508,1.0,0.9748,0.9508,1.0,0.0,tie,0.0252,MillID
+pap_Latn,Papiamento,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+rus_Cyrl,Russian,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+run_Latn,Rundi,58,0.8636,0.7703,0.9828,0.9062,0.8286,1.0,0.9062,0.8286,1.0,0.9355,0.8788,1.0,0.9355,0.8788,1.0,-0.0426,GlotLID,-0.0718,fastText
+swh_Latn,Swahili,58,0.9159,1.0,0.8448,0.9825,1.0,0.9655,0.9825,1.0,0.9655,1.0,1.0,1.0,1.0,1.0,1.0,-0.0666,GlotLID,-0.0841,fastText
+kab_Latn,Kabyle,58,0.9259,1.0,0.8621,0.9913,1.0,0.9828,0.9913,1.0,0.9828,1.0,1.0,1.0,1.0,1.0,1.0,-0.0654,GlotLID,-0.0741,fastText
+kir_Cyrl,Kyrgyz,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+lua_Latn,Luba-Lulua,58,0.7174,0.9706,0.569,0.7391,1.0,0.5862,0.7391,1.0,0.5862,0.7571,0.6463,0.9138,0.7571,0.6463,0.9138,-0.0217,GlotLID,-0.0398,fastText
+kmr_Latn,Northern Kurdish,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.8227,0.6988,1.0,0.8227,0.6988,1.0,0.0,tie,0.1773,MillID
+lit_Latn,Lithuanian,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+lij_Latn,Ligurian,58,0.9831,0.9667,1.0,0.8657,0.7632,1.0,0.8657,0.7632,1.0,0.5561,0.3878,0.9828,0.5561,0.3878,0.9828,0.1174,MillID,0.427,MillID
+kea_Latn,Kabuverdianu,58,0.9913,1.0,0.9828,1.0,1.0,1.0,1.0,1.0,1.0,0.3429,1.0,0.2069,0.3429,1.0,0.2069,-0.0087,tie,0.6484,MillID
+lus_Latn,Mizo,58,0.9913,1.0,0.9828,1.0,1.0,1.0,1.0,1.0,1.0,0.9915,0.9831,1.0,0.9915,0.9831,1.0,-0.0087,tie,-0.0001,tie
+kaz_Cyrl,Kazakh,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+kor_Hang,Korean,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+glg_Latn,Galician,58,0.9828,0.9828,0.9828,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0172,GlotLID,-0.0172,fastText
+guj_Gujr,Gujarati,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+fra_Latn,French,58,0.8627,1.0,0.7586,1.0,1.0,1.0,1.0,1.0,1.0,0.9508,0.9062,1.0,0.9508,0.9062,1.0,-0.1373,GlotLID,-0.0881,fastText
+fon_Latn,Fon,58,0.9573,0.9492,0.9655,0.9913,1.0,0.9828,0.9913,1.0,0.9828,0.9134,0.8406,1.0,0.9134,0.8406,1.0,-0.034,GlotLID,0.0439,MillID
+hrv_Latn,Croatian,58,0.6277,0.5443,0.7414,0.7887,0.6667,0.9655,0.7887,0.6667,0.9655,0.7606,0.6429,0.931,0.7606,0.6429,0.931,-0.161,GlotLID,-0.1328,fastText
+heb_Hebr,Hebrew,58,0.9825,1.0,0.9655,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0175,GlotLID,-0.0175,fastText
+kan_Knda,Kannada,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+kal_Latn,Kalaallisut,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9913,1.0,0.9828,0.9913,1.0,0.9828,0.0,tie,0.0087,tie
+ita_Latn,Italian,58,0.9913,1.0,0.9828,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0087,tie,-0.0087,tie
+isl_Latn,Icelandic,58,0.8738,1.0,0.7759,1.0,1.0,1.0,1.0,1.0,1.0,0.8406,0.725,1.0,0.8406,0.725,1.0,-0.1262,GlotLID,0.0332,MillID
+mai_Deva,Maithili,58,0.9355,0.8788,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0645,GlotLID,-0.0645,fastText
+kat_Geor,Georgian,58,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+nld_Latn,Dutch,58,0.9915,0.9831,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0085,tie,-0.0085,tie
+abk_Cyrl,Abkhazian,57,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+sot_Latn,Southern Sotho,57,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.8679,0.9388,0.807,0.8679,0.9388,0.807,0.0,tie,0.1321,MillID
+som_Latn,Somali,57,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+tgk_Cyrl,Tajik,57,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+tah_Latn,Tahitian,57,0.973,1.0,0.9474,0.8125,1.0,0.6842,0.8125,1.0,0.6842,1.0,1.0,1.0,1.0,1.0,1.0,0.1605,MillID,-0.027,fastText
+tat_Cyrl,Tatar,57,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+bam_Latn,Bambara,57,0.8244,0.7297,0.9474,0.7519,0.6579,0.8772,0.7519,0.6579,0.8772,0.6711,0.5435,0.8772,0.6711,0.5435,0.8772,0.0725,MillID,0.1533,MillID
+bem_Latn,Bemba,57,0.9913,0.9828,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9744,0.95,1.0,0.9744,0.95,1.0,-0.0087,tie,0.0169,MillID
+fao_Latn,Faroese,57,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.7473,1.0,0.5965,0.7473,1.0,0.5965,0.0,tie,0.2527,MillID
+khk_Cyrl,Halh Mongolian,57,1.0,1.0,1.0,0.9912,1.0,0.9825,0.9912,1.0,0.9825,1.0,1.0,1.0,1.0,1.0,1.0,0.0088,tie,0.0,tie
+yor_Latn,Yoruba,57,1.0,1.0,1.0,0.9913,0.9828,1.0,0.9913,0.9828,1.0,0.95,0.9048,1.0,0.95,0.9048,1.0,0.0087,tie,0.05,MillID
+mri_Latn,Māori,57,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.7862,0.6477,1.0,0.7862,0.6477,1.0,0.0,tie,0.2138,MillID
+lao_Laoo,Lao,57,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+ady_Cyrl,Adyghe,56,0.963,1.0,0.9286,0.9818,1.0,0.9643,0.9818,1.0,0.9643,1.0,1.0,1.0,1.0,1.0,1.0,-0.0189,GlotLID,-0.037,fastText
+slk_Latn,Slovak,56,0.9825,0.9655,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.0175,GlotLID,-0.0175,fastText
+pcm_Latn,Nigerian Pidgin,56,1.0,1.0,1.0,0.991,1.0,0.9821,1.0,1.0,1.0,0.88,1.0,0.7857,0.9725,1.0,0.9464,0.0,tie,0.0275,MillID
+bho_Deva,Bhojpuri,56,0.8515,0.9556,0.7679,0.9333,1.0,0.875,0.9333,1.0,0.875,0.84,0.9545,0.75,0.84,0.9545,0.75,-0.0818,GlotLID,0.0115,MillID
+gle_Latn,Irish,56,1.0,1.0,1.0,0.9825,0.9655,1.0,0.9825,0.9655,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0175,MillID,0.0,tie
+mos_Latn,Mossi,56,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+shn_Mymr,Shan,56,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+mya_Mymr,Burmese,56,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+npi_Deva,Nepali,55,1.0,1.0,1.0,0.9908,1.0,0.9818,0.9908,1.0,0.9818,1.0,1.0,1.0,1.0,1.0,1.0,0.0092,tie,0.0,tie
+san_Deva,Sanskrit,55,0.9815,1.0,0.9636,0.9908,1.0,0.9818,0.9908,1.0,0.9818,0.9908,1.0,0.9818,0.9908,1.0,0.9818,-0.0093,tie,-0.0093,tie
+mag_Deva,Magahi,53,0.8679,0.8679,0.8679,0.9444,0.9273,0.9623,0.9444,0.9273,0.9623,0.8596,0.8033,0.9245,0.8596,0.8033,0.9245,-0.0765,GlotLID,0.0083,tie
+plt_Latn,Malagasy (Plateau),53,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+alt_Cyrl,Southern Altai,51,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+amh_Ethi,Amharic,51,0.9901,1.0,0.9804,1.0,1.0,1.0,1.0,1.0,1.0,0.8409,1.0,0.7255,0.8409,1.0,0.7255,-0.0099,tie,0.1492,MillID
+lug_Latn,Ganda,49,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9703,0.9423,1.0,0.9703,0.9423,1.0,0.0,tie,0.0297,MillID
+gug_Latn,Paraguayan Guaraní,49,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9703,0.9423,1.0,0.9703,0.9423,1.0,0.0,tie,0.0297,MillID
+yue_Hani,Cantonese,49,0.7436,1.0,0.5918,0.5797,1.0,0.4082,0.5797,1.0,0.4082,0.2719,0.1596,0.9184,0.2719,0.1596,0.9184,0.1639,MillID,0.4717,MillID
+oss_Cyrl,Ossetic,44,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie
+chv_Cyrl,Chuvash,25,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.9804,0.9615,1.0,0.9804,0.9615,1.0,0.0,tie,0.0196,MillID
+tzm_Tfng,Central Atlas Tamazight,1,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,tie,0.0,tie

reports/udhr-common/millid_per_language.csv

@@ -0,0 +1,171 @@
+label,support,tp,fp,fn,tn,precision,recall,f1,specificity,fp_per_million,sensitivity
+abk_Cyrl,57,57,0,0,11853,1.0,1.0,1.0,1.0,0.0,1.0
+ace_Latn,61,61,0,0,11849,1.0,1.0,1.0,1.0,0.0,1.0
+ady_Cyrl,56,52,0,4,11854,1.0,0.928571,0.962963,1.0,0.0,0.928571
+afr_Latn,60,59,0,1,11850,1.0,0.983333,0.991597,1.0,0.0,0.983333
+als_Latn,59,58,0,1,11851,1.0,0.983051,0.991453,1.0,0.0,0.983051
+alt_Cyrl,51,51,0,0,11859,1.0,1.0,1.0,1.0,0.0,1.0
+amh_Ethi,51,50,0,1,11859,1.0,0.980392,0.990099,1.0,0.0,0.980392
+arb_Arab,58,46,0,12,11852,1.0,0.793103,0.884615,1.0,0.0,0.793103
+arn_Latn,58,56,0,2,11852,1.0,0.965517,0.982456,1.0,0.0,0.965517
+ast_Latn,58,55,0,3,11852,1.0,0.948276,0.973451,1.0,0.0,0.948276
+ayr_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+azj_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+bam_Latn,57,54,20,3,11833,0.72973,0.947368,0.824427,0.998313,1687.336539,0.947368
+ban_Latn,60,57,0,3,11850,1.0,0.95,0.974359,1.0,0.0,0.95
+bel_Cyrl,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+bem_Latn,57,57,1,0,11852,0.982759,1.0,0.991304,0.999916,84.366827,1.0
+ben_Beng,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
+bho_Deva,56,43,2,13,11852,0.955556,0.767857,0.851485,0.999831,168.71942,0.767857
+bis_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+bod_Tibt,60,58,1,2,11849,0.983051,0.966667,0.97479,0.999916,84.388186,0.966667
+bos_Latn,59,13,7,46,11844,0.65,0.220339,0.329114,0.999409,590.667454,0.220339
+bug_Latn,60,53,0,7,11850,1.0,0.883333,0.938053,1.0,0.0,0.883333
+bul_Cyrl,58,57,1,1,11851,0.982759,0.982759,0.982759,0.999916,84.373945,0.982759
+cat_Latn,58,58,3,0,11849,0.95082,1.0,0.97479,0.999747,253.121836,1.0
+ceb_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+ces_Latn,58,56,0,2,11852,1.0,0.965517,0.982456,1.0,0.0,0.965517
+chv_Cyrl,25,25,0,0,11885,1.0,1.0,1.0,1.0,0.0,1.0
+cjk_Latn,114,88,0,26,11796,1.0,0.77193,0.871287,1.0,0.0,0.77193
+cmn_Hani,319,306,14,13,11577,0.95625,0.959248,0.957746,0.998792,1207.833664,0.959248
+crh_Latn,59,59,0,0,11851,1.0,1.0,1.0,1.0,0.0,1.0
+cym_Latn,60,60,0,0,11850,1.0,1.0,1.0,1.0,0.0,1.0
+dan_Latn,61,58,4,3,11845,0.935484,0.95082,0.943089,0.999662,337.58123,0.95082
+deu_Latn,67,67,0,0,11843,1.0,1.0,1.0,1.0,0.0,1.0
+dyu_Latn,58,0,3,58,11849,0.0,0.0,0.0,0.999747,253.121836,0.0
+dzo_Tibt,59,58,2,1,11849,0.966667,0.983051,0.97479,0.999831,168.76213,0.983051
+ekk_Latn,60,60,0,0,11850,1.0,1.0,1.0,1.0,0.0,1.0
+ell_Grek,115,58,0,57,11795,1.0,0.504348,0.67052,1.0,0.0,0.504348
+eng_Latn,58,58,0,0,11852,1.0,1.0,1.0,1.0,0.0,1.0
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reports/udhr-common/millid_summary.json

@@ -0,0 +1,17 @@
+{
+  "top1_accuracy": 0.9123425692695214,
+  "top3_accuracy": 0.9606213266162889,
+  "top5_accuracy": 0.9654911838790932,
+  "passages": 11910,
+  "languages": 170,
+  "macro_precision": 0.9794271783100589,
+  "macro_recall": 0.9393117513722192,
+  "macro_f1": 0.9512636942896991,
+  "macro_specificity": 0.9999174355030307,
+  "weighted_f1": 0.92868171659548,
+  "median_f1": 1.0,
+  "languages_f1_at_least_0_9": 149,
+  "languages_f1_below_0_5": 4,
+  "mean_fp_per_million": 82.56449696922371,
+  "micro_f1": 0.9123425692695214
+}