I want to use a Dutch model for named entity recognition with Core NLP.
I have found a pre-trained model from OpenNLP, but it doesn't seem to be interoperable with CoreNLP.
Why is that? Can we still use Core NLP with other languages than English, Chinese and Spanish?
CoreNLP currently does not support Dutch. There are some components which work for German and Arabic, but the pipeline is currently only for English, Chinese and Spanish. You can retrain our NER model on the same conllx data, but we have not done this.
The OpenNLP models are not compatible with CoreNLP.
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We are working on a project to train a Auto ML models for each language supported by Google. We would like to know the basic Blue score set by Google NMT for all the supported languages. (Not only Auto ML supported languages, but also the V2, V3 supported languages)
There are ~500k code snippets written in proprietary language that I have to port to a new system also using its own proprietary language. I have the following with me
Vocabulary and grammar of source and destination languages
Sample of 1500 converted rules (for training if required) of different complexity
I am not looking for 100% automation but may be a transpiler that may automate part of it. Can it be done using NLP? Have already gone through this, this, Rascal , Haxe and Spoofax. I could not find much documentation on how to create a custom tranpiler.
Any help is appreciated. Thank you!
I have trained IBM Watson to recognize objects of interest. Since remote execution isn’t a requirement I want to export to .mlmodel with the tool provided and run in macOS.
Unfortunately learning Swift and macOS development isn’t a requirement either. It is possible to invoke Vision directly from the command line or from a scripting language? As alternative anybody knows a skeleton of macOS app to run Vision over a list of files and obtain classification scores in tabular form? Thanks.
The code mentioned in this article uses a downloaded Core ML model in an iOS App through Watson SDK.
Additionally, Here’s a code sample that uses Watson Visual Recognition and Core ML to classify images. The workspace has two projects
Core ML Vision Simple: Classify images locally with Visual Recognition.
Core ML Vision Custom: Train a custom Visual Recognition model for more specialized classification.
Refer the code and instructions here
Also, there’s a starter kit that comes with Watson Visual Recognition preconfigured with Core ML - https://console.bluemix.net/developer/appledevelopment/starter-kits/custom-vision-model-for-core-ml-with-watson
You can also load the mlmodel into Python and use the coremltools package to make predictions. I wouldn't use that in a production environment, but it's OK to get something basic up and running.
I'm working on object detection using dlib, I was going through python implementation. I tested couple of examples from dlib python examples, specially i worked on train_object_detector.py. this works well. Now I would like to train same data model on CNN based object detector mode But i could not find python implementation for training CNN using python, but there is c++ example. (dnn_mmod_ex.cpp). I think it seems i'm missing something or python implementation is not available?
if python implementation is not available, then should i switch to c++ for CNN based object detector training
Yes, use C++ for CNN training. The dlib DNN tooling is meant to be used from C++ and so uses C++11 features that can't be represented in Python.
I am working on a Java application primarily on English but also hope to support Indian languages like Hindi, Telugu and Gujarati. I am wondering what can be a good strategy for this.
I have seen I18N projects in Latin script based languages but Hindi uses Devanagri script so it's a little more complicated.
Has anyone done anything close to this ?
To be honest, the scripts and languages you mentioned are not necessary very common in programming. Since you didn't mention whether it will be desktop or web application, it will be quite hard to give you any advice apart from use latest Java (7) and ICU (49.1 or even 50M2) version.
That's because this releases support newer version of the Unicode Standard, and it will play a role here.
BTW. You might want to know, that the Unicode Consortium is working on better support for Indian scripts. Therefore developing applications with this languages in mind will be easier in the future, for now you will probably struggle a bit.