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Optimize new queue? #14
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Hi! That's not a bad idea, I've had it in the passed but can't exactly remember why I didn't pursue it :/.
Would you mind trying and reporting back? Btw the tone of your message made it really nice to read and made me happy, have a great day too! |
Cool! Editing the file I noticed this: I'm currently learning Chinese and Swedish, do I need to edit this like that or something else?
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Hi!
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Cool, understanding stopwords now! What would I need to set the highjack values to? I read the readme file but there are no possible values |
Open anki's browser, look for cards using a search query for example This query is the way you ask anki to find cards. Well highjack arguments by default are set to
Tell me if it's more clear, in which case I'll link the README to this issue. |
Oh! Perfectly understood now...hahaha I didn't get it at first. They're now like this:
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You might want to add something like Don't forget to tell me if it works :) I suggest lowering the score adjustment factor to (1, 0.1) to try and see if it's better. |
I've got approximately 15.000 sentence cards which I use to mine the language, so I'll try these out and report back! Thanks so much for your time :) |
Working!! Swedish worked flawlessly :) Will report back with my chinese deck. |
Hey again! So this error pops up when running the script on my chinese deck. Probably I'm running out of memory because my notebook only has 4gb of ram. I googled and it seems to be a python problem and not your script's. Anyways, maybe this'll happen to other people, so maybe you need to implement something here?
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Hi, I implemented the argument "low_power_mode". If you set it to true, the tokenizer will use unigram instead of ngrams between 1 and 5. This should considerably reduce the number of computation. It's currently only in the dev branch, if you test it and i works I'll merge it with main. Another thing you might want to test afterwards please is lowering TFIDF_dim, currently 100 dimensions is enough for 98.2% of the variance, which means you are wayyy overdoing it. |
Reporting back! |
Muchos gracias por tu mensaje! (btw, the name of this software is from an argentinian person :) ) I think it's better to use low_power_mode than to reduce the number of dim drastically. That being said, the number of dim can and should be reduced anyway if you see it's keeping more than say 70% of the variance IMO. |
Oh! That's so cool :) |
Hey! Hope you're doing fine! :)
I was wondering if it's possible to optimize new cards through their content based on what one already learned (is:review).
If one learns sentences and they're all pretty similar in context one ends up learning too much of the same...
Just an idea!
Have a great day
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