This is a COMET 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.
NOTE: This model was recently replaced by an improved version wmt22-comet-da
1from comet import download_model, load_from_checkpoint
23model_path = download_model("Unbabel/wmt20-comet-da")4model = load_from_checkpoint(model_path)5data =[6{7"src":"Dem Feuer konnte Einhalt geboten werden",8"mt":"The fire could be stopped",9"ref":"They were able to control the fire."10},11{12"src":"Schulen und Kindergärten wurden eröffnet.",13"mt":"Schools and kindergartens were open",14"ref":"Schools and kindergartens opened"15}16]17model_output = model.predict(data, batch_size=8, gpus=1)18print(model_output)
Intended uses
Our model is intented to be used for MT evaluation.
Given a a triplet with (source sentence, translation, reference translation) outputs a single score. This score is unbounded but typically falls between -1 and 1 where 1 reflects a perfect translation.
Languages Covered:
This model builds on top of XLM-R which cover the following languages: