I've trained a POS tagger and neural dependency parser with Stanford corenlp. I can get them to work via command line, and now would like to access them via a server.
However, the documentation for the server doesn't say anything about using custom models. I checked the code and didn't find any obvious way of supplying a configuration file.
Any idea how to do this? I don't need all annotators, just the ones I trained.
Yes, the server should (in theory) support all the functionality of the regular pipeline. The properties GET parameter is translated into the Properties object you would normally pass into StanfordCoreNLP. Therefore, if you'd like the server to load a custom model, you can just call it via, e.g.:
wget \
--post-data 'the quick brown fox jumped over the lazy dog' \
'localhost:9000/?properties={"parse.model": "/path/to/model/on/server/computer", "annotators": "tokenize,ssplit,pos", "outputFormat": "json"}' -O -
Note that the server won't garbage-collect this model afterwards though, so if you load too many models there's a good chance you'll run into out-of-memory errors...
