Am doing a translation from swahili to my local dialect here in Kenya and it seems am stuck at the interface for the end users since i think shell is not a good interface for the end user of the model. Please help. Thanks.
See the REST server usage and API that you can use to build your application:
thanks for the link…
I’m currently working on a LibreOffice Plug-In that I’d hope helps to make things easy for end users.
There still is a lot of UI to figure out (I’m currently looking into options there), but here is the very early git repository with two screenshots:
I don’t currently use the “Server” part and likely will have particular configuration needs. (Though if we had a “model zoo” with a unified format, that would certainly be cool to just “import” a translation model.)
If you think the above can be or can be made helpful for your use case, I’d be most happy to hear about your ideas.
Hi, Tomas. Am still working on the user interface for my trained model using the REST API with Flask and i think your libreoffice project on the git repository can help solve the problem for the end users of the translation models.
In that connection, i have a question concerning your user interface. Is it possible for me to use the libreoffice with added plugins for my trained transaltion model? I am translating swahili to Meru where meru is a local dialect language and swahili is spoken widely in East Africa.
I think your response to this will solve the problem with end users. Thanks.
thank you for sharing your plans!
So the answer is “not yet”!
So the linked repository is a first proof of concept that I’ve been working on for a few weeks, precisely to start a conversation such as ours, and I’m very keen to now extend this in three ways to make it more generally useful:
- Provide a way to add and run your model. More about this below.
- Find out what a good user could be.
- Currently I’m just replacing the source language text by the target language. That might be a bit too fast.
- Have some interface similar to spell checking to let the user confirm replacement or edit the text before replacement. This is my current working hypothesis, but I’d love to hear your opinion. As an aside, a popular word processor seems to open a side bar that roughly has the same function.
- What can we learn from the papers such as seq2seq vis (and I think I found a few other recent papers in that general direction) for a general purpose NMT UI?
- I’d love to hear about your ideas!
- Look at how and if it is useful to wrap the training functionality. Maybe provide a way of finetuning translations based on not-terribly-aligned corpora. I have some ideas here, but they’re quite speculative and my time budget is not inifite.
So to make this concrete for using your model: I’d be very humbled if I be a little help in providin a translation function for languages that are beyond my reach!
- I’m mainly interested in offline use, so I’m trying to create an open-source application (and maybe if it’s generally useful enough I’d try to reach out to libreoffice about including it).
- My idea is that there should be a model-zoo like way of installing translation models.
- The first question here is: Is your model publicly available or would you be willing to make it available? Does it run in “vanilla” OpenNMT-py?
- As the format, maybe a zip file with a json-like description like for the rest server would work.
- One thing I noticed was the for EN->DE we need a preprocessing step that splits the text into sentences. I’m not aware of OpenNMT(-py) providing this. For EN and DE, we can use spacy for this step. Do you have any idea of what we could use with Swahili? As I’m a bit weary of having spacy as a dependency (it does have some very tight versioned dependencies), I will probably make available a “decoupled” sentence-splitting model, but we’d need something to start with.
If we can figure out these things, I think we could have a working Swaheli->Meru translation in LibreOffice soon.
I do have some more technical things on my mind (e.g. I copy-pasted a large part of the translator to get the output and attention information in the precise way I want, so I’d be looking into getting these from OpenNMT-py without copy-pasting.), but these are probably for another thread.
I look forward to hear your thoughts on this!