Comments (1)
Hi @timpal0l ,
The lower the better indeed. We are tracking the error rate for each pairs of languages.
For curious readers:
A document describes a given language. There are as many documents as languages. We iterate over each document sentence by sentence. What we are comparing in the tables is how similar are the documents according to each multilingual architecture. If the model was perfect, the error rates in the tables from the README would be equal to 0. Indeed, for each sentence of a document d_i, it would find the translation in the document d_k for all k != i and hence we would never increment the error rate.
Regards,
Mastafa
from multilingual_similarity_compare.
Related Issues (10)
- Why does XLM-R have low performance on this task?
- Add batch-size as a parameter in parser.args
- Add distilBERT with batches and test it HOT 3
- Time comparison between GPU and CPU HOT 1
- Why is mean pooling giving such bad performance? HOT 2
- Shift everything to batch version with GPU support for faster results after testing HOT 2
- Test XLM-R with mean pooling after solving issue on mean pooling HOT 1
- Arabic and Urdu HOT 3
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from multilingual_similarity_compare.