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@@ -32,7 +32,7 @@ language:
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  license: apache-2.0
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  ---
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- This is a [AfriCOMET-QE-STL (single task)](https://github.com/masakhane-io/africomet) evaluation model: It receives a triplet with (source sentence, translation, reference translation) and returns a score that reflects the quality of the translation compared to both source and reference.
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  # Paper
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@@ -54,7 +54,7 @@ pip install unbabel-comet
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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 masakhane/africomet-qe-stl
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  ```
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  Or using Python:
@@ -68,12 +68,10 @@ 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)
@@ -82,9 +80,9 @@ print (model_output)
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  # Intended uses
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- Our model is intented to be used for **MT evaluation**.
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- Given a a triplet with (source sentence, translation, reference translation) outputs a single score between 0 and 1 where 1 represents a perfect translation.
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  # Languages Covered:
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  license: apache-2.0
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  ---
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+ This is a [AfriCOMET-QE-STL (quality estimation single task)](https://github.com/masakhane-io/africomet) 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.
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  # Paper
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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 --model masakhane/africomet-qe-stl
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  ```
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  Or using Python:
 
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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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  },
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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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  }
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  ]
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  model_output = model.predict(data, batch_size=8, gpus=1)
 
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  # Intended uses
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+ Our model is intented to be used for **MT quality estimation**.
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+ Given a source sentence and a translation outputs a single score between 0 and 1 where 1 represents a perfect translation.
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  # Languages Covered:
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