Comments (3)
You're right, but it is likely coming from the model itself rather than the library. Here are the top 3 languages detected for your examples
[('__label__fr', 0.8692260980606079), ('__label__la', 0.07981850951910019), ('__label__vi', 0.015786711126565933)]
Text: ceci est du francais LANG: fr SCORE: 0.8692260980606079
[('__label__it', 0.44422411918640137), ('__label__fr', 0.2902344763278961), ('__label__en', 0.05172138288617134)]
Text: donnez quarante cinq cinquante euros LANG: xx SCORE: 0.44422411918640137
[('__label__en', 0.20330046117305756), ('__label__it', 0.14236119389533997), ('__label__fr', 0.10877827554941177)]
Text: donnez 45 50 euros LANG: en SCORE: 0.20330046117305756
You have a couple of options:
- provide your own model using
model_path
- add a
threshold
, so it falls back toxx
(or something else) if the confidence is too low
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Thank you for your help , I slove the problem with a bigger model found on fasttest lid.176.bin,
seem to be the same version of the lid.176.ftz but uncompressed.
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Glad to hear you found a solution. Yes, the bigger version is supposed to be more accurate, but it feels too big to be shipped by default in this project.
I'll close the issue for now.
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Related Issues (4)
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