Hi,
I have built a generic baseline model and I’m trying to fine-tune my model to a specific domain. I have used the train_from option to load my model. I see my training going into an infinite loop.
“[2020-12-11 15:25:41,054 INFO] Loading ParallelCorpus(bpe.nmine.en, bpe.nmine.hi, align=None)…”. This message keeps on repeating in the log.
Yasmin,
Doesn’t it dilute the purpose of naming “on-the-fly”? The author in the paper “https://www.aclweb.org/anthology/W17-4713.pdf” states “using only the top-1 retrieved pair for updating the model”.I wanted to try it using OpenNMT but I’m encountering the data-size issue.@francoishernandez is there a way we can do this?
As stated earlier, this data loading pipeline is mainly aimed at big datasets, not very small ones. It’s working though. Just not optimised and a bit too verbose.
I was trying to load around 500 sentences, I see there is too much verbose. Is the sentencewise weights option included in OpenNMT? , I don’t see any documentation for it. It would be helpful if you can direct me to the documentation if any.
This means it’s not in the released version of OpenNMT-py, hence it’s not in the docs either.
Also, this was based on the legacy version of OpenNMT-py and is not compatible with 2.0.
Still, it should work standalone if you want to try it.
The use of the option is explained in the PR.
I introduce the -sentence_weights opt, to which we are supposed to pass some text file(s) containing the weights for each sentence / example. If several corpora are passed according to #1413 upgrades, such weight files should be passed as well. If we want/have weights for only some of the corpora in the list, we can pass None/none instead of the filename and it will be cast to python None by argparse, and weights of 1 will be assigned.