README.md

---
pipeline_tag: translation
language:
  - multilingual
  - en
  - am
  - ar
  - so
  - sw
  - pt
  - af
  - fr
  - zu
  - mg
  - ha
  - sn
  - arz
  - ny
  - ig
  - xh
  - yo
  - st
  - rw
  - tn
  - ti
  - ts
  - om
  - run
  - nso
  - ee
  - ln
  - tw
  - pcm
  - gaa
  - loz
  - lg
  - guw
  - bem
  - efi
  - lue
  - lua
  - toi
  - ve
  - tum
  - tll
  - iso
  - kqn
  - zne
  - umb
  - mos
  - tiv
  - lu
  - ff
  - kwy
  - bci
  - rnd
  - luo
  - wal
  - ss
  - lun
  - wo
  - nyk
  - kj
  - ki
  - fon
  - bm
  - cjk
  - din
  - dyu
  - kab
  - kam
  - kbp
  - kr
  - kmb
  - kg
  - nus
  - sg
  - taq
  - tzm
  - nqo

license: apache-2.0
---
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.
This model is based on an improved African enhanced encoder, [afro-xlmr-large-76L](https://huggingface.co/Davlan/afro-xlmr-large-76L).

# Paper

Coming soon

# License

Apache-2.0

# Usage (SSA-COMET)

Using this model requires unbabel-comet to be installed:

```bash
pip install --upgrade pip  # ensures that pip is current 
pip install unbabel-comet
```

Then you can use it through comet CLI:

```bash
comet-score -s {source-inputs}.txt -t {translation-outputs}.txt -r {references}.txt --model McGill-NLP/ssa-comet-stl
```

Or using Python:

```python
from comet import download_model, load_from_checkpoint
model_path = download_model("McGill-NLP/ssa-comet-stl")
model = load_from_checkpoint(model_path)
data = [
    {
        "src": "Nadal sàkọọ́lẹ̀ ìforígbárí o ní àmì méje sóódo pẹ̀lú ilẹ̀ Canada.",
        "mt": "Nadal's head to head record against the Canadian is 7–2.",
        "ref": "Nadal scored seven unanswered points against Canada."
    },
    {
        "src": "Laipe yi o padanu si Raoniki ni ere Sisi Brisbeni.",
        "mt": "He recently lost against Raonic in the Brisbane Open.",
        "ref": "He recently lost to Raoniki in the game Sisi Brisbeni."
    }
]
model_output = model.predict(data, batch_size=8, gpus=1)
print (model_output)
```

# Intended uses

Our model is intended to be used for **MT evaluation**. 

Given a triplet with (source sentence, translation, reference translation), it outputs a single score between 0 and 1, where 1 represents a perfect translation. 

# Languages Covered:

There are 76 languages available :
- English (eng)
- Amharic (amh)
- Arabic (ara) 
- Somali (som)
- Kiswahili (swa)
- Portuguese (por)
- Afrikaans (afr)
- French (fra)
- isiZulu (zul)
- Malagasy (mlg)
- Hausa (hau)
- chiShona (sna)
- Egyptian Arabic (arz)
- Chichewa (nya)
- Igbo (ibo)
- isiXhosa (xho)
- Yorùbá (yor)
- Sesotho (sot)
- Kinyarwanda (kin)
- Tigrinya (tir)
- Tsonga (tso)
- Oromo (orm)
- Rundi (run)
- Northern Sotho (nso)
- Ewe (ewe)
- Lingala (lin)
- Twi (twi)
- Nigerian Pidgin (pcm)
- Ga (gaa)
- Lozi (loz)
- Luganda (lug)
- Gun (guw)
- Bemba (bem)
- Efik (efi)
- Luvale (lue) 
- Luba-Lulua (lua)
- Tonga (toi)
- Tshivenḓa (ven)
- Tumbuka (tum)
- Tetela (tll)
- Isoko (iso)
- Kaonde (kqn)
- Zande (zne)
- Umbundu (umb)
- Mossi (mos)
- Tiv (tiv)
- Luba-Katanga (lub)
- Fula (fuv)
- San Salvador Kongo (kwy)
- Baoulé (bci)
- Ruund (rnd)
- Luo (luo)
- Wolaitta (wal) 
- Swazi (ssw)
- Lunda (lun)
- Wolof (wol)
- Nyaneka (nyk) 
- Kwanyama (kua)
- Kikuyu (kik)
- Fon (fon)
- Bambara (bam)
- Chokwe (cjk)
- Dinka (dik)
- Dyula (dyu)
- Kabyle (kab)
- Kamba (kam)
- Kabiyè (kbp)
- Kanuri (knc)
- Kimbundu (kmb)
- Kikongo (kon)
- Nuer (nus)
- Sango (sag)
- Tamasheq (taq)
- Tamazight (tzm)
- N'ko (nqo)

# Specifically Finetuned on:
- Amharic (amh)
- Hausa (hau)
- Igbo (ibo)
- Kikuyu (kik)
- Kinyarwanda (kin)
- Luo (luo)
- Twi (twi)
- Yoruba (yor)
- Zulu (zul)
- Ewe (Ewe)
- Lingala (lin)
- Wolof (wol)