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README.md
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---
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| 2 |
license: apache-2.0
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-
---
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| 1 |
---
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| 2 |
+
pipeline_tag: translation
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language:
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- multilingual
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- en
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- am
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- ar
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- so
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- sw
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- pt
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- af
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- fr
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- zu
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- mg
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- ha
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- sn
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- arz
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- ny
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- ig
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- xh
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- yo
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- st
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- rw
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- tn
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- ti
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- ts
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- om
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- run
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- nso
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- ee
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- ln
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- tw
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- pcm
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- gaa
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- loz
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- lg
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- guw
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- bem
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- efi
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- lue
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- lua
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- toi
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- ve
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- tum
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- tll
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- iso
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- kqn
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- zne
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- umb
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- mos
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- tiv
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- lu
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- ff
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- kwy
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- bci
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- rnd
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- luo
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- wal
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- ss
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- lun
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- wo
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- nyk
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- kj
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- ki
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- fon
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- bm
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- cjk
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- din
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- dyu
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- kab
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- kam
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- kbp
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- kr
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- kmb
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- kg
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- nus
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- sg
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- taq
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- tzm
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- nqo
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license: apache-2.0
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---
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SSA-COMET-STL, a robust, automatic metric for MTE, built based on SSA-MTE: It receives a triplet with (source sentence, translation, reference translation), and returns a score that reflects the quality of the translation.
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This model is based on an improved African enhanced encoder, [afro-xlmr-large-76L](https://huggingface.co/Davlan/afro-xlmr-large-76L).
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# Paper
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Coming soon
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# License
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Apache-2.0
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# Usage (SSA-COMET)
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Using this model requires unbabel-comet to be installed:
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```bash
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pip install --upgrade pip # ensures that pip is current
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pip install unbabel-comet
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```
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Then you can use it through comet CLI:
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```bash
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comet-score -s {source-inputs}.txt -t {translation-outputs}.txt -r {references}.txt --model McGill-NLP/ssa-comet-stl
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```
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Or using Python:
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```python
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from comet import download_model, load_from_checkpoint
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model_path = download_model("McGill-NLP/ssa-comet-stl")
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model = load_from_checkpoint(model_path)
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data = [
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{
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"src": "Nadal sàkọọ́lẹ̀ ìforígbárí o ní àmì méje sóódo pẹ̀lú ilẹ̀ Canada.",
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"mt": "Nadal's head to head record against the Canadian is 7–2.",
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"ref": "Nadal scored seven unanswered points against Canada."
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},
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{
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"src": "Laipe yi o padanu si Raoniki ni ere Sisi Brisbeni.",
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"mt": "He recently lost against Raonic in the Brisbane Open.",
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"ref": "He recently lost to Raoniki in the game Sisi Brisbeni."
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}
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]
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model_output = model.predict(data, batch_size=8, gpus=1)
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print (model_output)
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```
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# Intended uses
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Our model is intended to be used for **MT evaluation**.
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Given a triplet with (source sentence, translation, reference translation), it outputs a single score between 0 and 1, where 1 represents a perfect translation.
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# Languages Covered:
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There are 76 languages available :
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- English (eng)
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- Amharic (amh)
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- Arabic (ara)
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- Somali (som)
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- Kiswahili (swa)
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- Portuguese (por)
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- Afrikaans (afr)
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- French (fra)
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- isiZulu (zul)
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- Malagasy (mlg)
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- Hausa (hau)
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- chiShona (sna)
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- Egyptian Arabic (arz)
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- Chichewa (nya)
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- Igbo (ibo)
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- isiXhosa (xho)
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- Yorùbá (yor)
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- Sesotho (sot)
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- Kinyarwanda (kin)
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- Tigrinya (tir)
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- Tsonga (tso)
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- Oromo (orm)
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- Rundi (run)
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- Northern Sotho (nso)
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- Ewe (ewe)
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- Lingala (lin)
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- Twi (twi)
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- Nigerian Pidgin (pcm)
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- Ga (gaa)
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- Lozi (loz)
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- Luganda (lug)
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- Gun (guw)
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- Bemba (bem)
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- Efik (efi)
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- Luvale (lue)
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- Luba-Lulua (lua)
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- Tonga (toi)
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- Tshivenḓa (ven)
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- Tumbuka (tum)
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- Tetela (tll)
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- Isoko (iso)
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- Kaonde (kqn)
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- Zande (zne)
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- Umbundu (umb)
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- Mossi (mos)
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- Tiv (tiv)
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- Luba-Katanga (lub)
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- Fula (fuv)
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- San Salvador Kongo (kwy)
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- Baoulé (bci)
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- Ruund (rnd)
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- Luo (luo)
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- Wolaitta (wal)
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- Swazi (ssw)
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- Lunda (lun)
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- Wolof (wol)
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- Nyaneka (nyk)
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- Kwanyama (kua)
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- Kikuyu (kik)
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- Fon (fon)
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- Bambara (bam)
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- Chokwe (cjk)
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- Dinka (dik)
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- Dyula (dyu)
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- Kabyle (kab)
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- Kamba (kam)
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- Kabiyè (kbp)
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- Kanuri (knc)
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- Kimbundu (kmb)
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- Kikongo (kon)
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- Nuer (nus)
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- Sango (sag)
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- Tamasheq (taq)
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- Tamazight (tzm)
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- N'ko (nqo)
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# Specifically Finetuned on:
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- Amharic (amh)
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- Hausa (hau)
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- Igbo (ibo)
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- Kikuyu (kik)
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- Kinyarwanda (kin)
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- Luo (luo)
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- Twi (twi)
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- Yoruba (yor)
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- Zulu (zul)
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- Ewe (Ewe)
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- Lingala (lin)
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- Wolof (wol)
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