Open alanault opened 4 years ago
Hi,
Will work on it since I need to fix something else to be back on Cran... Hope next week I will have some time to work on it.
Sounds great - intrigued to see the results of this output
Hi there, just wondered how this was coming on?
I'm not familiar with Rcpp, but happy to help on any R-based areas if that's useful?
Hello, I have updated the C++ code. Should work using command line. Do you think it would make sense to have a R API for this?
Hi: that's fantastic!
Yes, I was thinking it could just be exactly the same as the other calls, just with the -autotune-validation
passed as an option in the execute
command, along with the validation file.
So, to use the supervised example:
execute(commands = c("supervised", "-input", train_tmp_file_txt, "-output", tmp_file_model, "-autotune-validation", valid_tmp_file_text))
That way everything is consistent within the execute command?
I think so. Not yet tried. If you check, can you let me know if it works?
Installed 0.3.4 So the call seems to work just fine... however each time I try it I get a C stack usage crash (is too close to the limit).
I'm only training on 100k short sentences and it crashes within seconds at (0.8% of completion), so wonder if something else is going on?
does it crash in other situations?
No - if I remove the -autotune-validation argument and just make the same call train, then it runs just fine on the same data, so must be related to the validation...
Did a test using the command line version and this worked fine, so the issue must be somewhere inside the rcpp wrapper
Any luck with this?
Hi there,
I saw on the Fasttext page here they've added an autotune feature, which automatically optimizes the various hyperparameters.
Seems it can be activated with the
-autotune-validation
option, which isn't currently supported. Wondered if this could be added with the updates for CRAN?https://fasttext.cc/docs/en/autotune.html
best
Alan