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masakhane/africomet-qe-stl-1.1
africomet-qe-stl-1.1 is a translation model from masakhane. Use it when you need text moved from one language to another. The card lists the license as apache-2.0.
This is an improved version of AfriCOMET-QE-STL (quality estimation single task) evaluation model: It receives a source sentence, and a translation, and returns a score that reflects the quality of the translation com…
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Updated Apr 11, 2025
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From the Hugging Face model README
This is an improved version of AfriCOMET-QE-STL (quality estimation single task) evaluation model: It receives a source sentence, and a translation, and returns a score that reflects the quality of the translation compared to the source. Different from the original AfriCOMET-QE-STL, this QE model is based on an improved African enhanced encoder, afro-xlmr-large-76L, which leads better performance on quality estimation of African-related machine translation, verified in WMT 2024 Metrics Shared Task.
AfriMTE and AfriCOMET: Empowering COMET to Embrace Under-resourced African Languages (Wang et al., arXiv 2023)
Apache-2.0
Using this model requires unbabel-comet to be installed:
pip install --upgrade pip # ensures that pip is current
pip install unbabel-comet
Then you can use it through comet CLI:
comet-score -s {source-inputs}.txt -t {translation-outputs}.txt --model masakhane/africomet-qe-stl
Or using Python:
from comet import download_model, load_from_checkpoint
model_path = download_model("masakhane/africomet-qe-stl-1.1")
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.",
},
{
"src": "Laipe yi o padanu si Raoniki ni ere Sisi Brisbeni.",
"mt": "He recently lost against Raonic in the Brisbane Open.",
}
]
model_output = model.predict(data, batch_size=8, gpus=1)
print (model_output)
Our model is intented to be used for MT quality estimation.
Given a source sentence and a translation, the model outputs a single quality score between 0 and 1 where 1 represents a perfect translation.
There are 76 languages available :