java VS CoreNLP

There are Java bindings for TensorFlow, but that's quite low level. I tried to see if I can get some Keras API for Scala, but I'm no expert and haven't had enough time to invest in this, so it's stuck in alpha. Maybe I develop it slow burning over the next year. A bit envious that Kotlin has a Keras-like library.

Hope this is not too late to answer your question. In theory there are no official Java tutorials for Tensorflow 2. The Java implementation is still under development at https://github.com/tensorflow/java

To give a contrasting perspective, I think the Java ecosystem is much better suited for many data science tasks, and has a growing and well-maintained set of libraries for general purpose machine learning. I won't list them all, but TF-Java, DJL et al. have implementations of many modern architectures and there are a number of excellent libraries (CoreNLP, Lucene et al.) for working with text.

Here's the latest java git repo https://github.com/tensorflow/java

The Stanford CoreNLP Model

If your looking at spacy have a look at Apache OpenNLP and Core NLP.

Tokenize with NLTK, SpaCy or CoreNLP

It should not take nearly that long. However, again I must recommend you take this conversation to github

Stanford has a great program and the Stanford NLP Group maintains CoreNLP which I have used before.

So why not use the Stanford CoreNLP library?

If you need NER, there’s no need to implement it yourself. There are several popular libraries that can do this for you nowadays. Five of these libraries, Stanford CoreNLP, NLTK, OpenNLP, SpaCy, and GATE, were already mentioned in the title.

Check something like this out to start: https://stanfordnlp.github.io/CoreNLP/

You don't actually even need to learn how to do deep learning if you're doing something fairly basic, which it sounds like you are. There are a lot of good tools you can use basically straight out of the box for something like this. Check out https://huggingface.co/course/chapter1, https://course.spacy.io/en/, https://guide.allennlp.org/ and https://www.nltk.org/book/. If java's more your thing, add https://stanfordnlp.github.io/CoreNLP/ to the list.

To give a contrasting perspective, I think the Java ecosystem is much better suited for many data science tasks, and has a growing and well-maintained set of libraries for general purpose machine learning. I won't list them all, but TF-Java, DJL et al. have implementations of many modern architectures and there are a number of excellent libraries (CoreNLP, Lucene et al.) for working with text.