Web application for visual parameter space analysis and exploration of word embedding models. Further information to follow.
Use docker-compose files provided in repository. Further instructions to follow.
Web application for visual parameter space analysis of topic models utilizing word embeddigs.
License: GNU General Public License v3.0
Web application for visual parameter space analysis and exploration of word embedding models. Further information to follow.
Use docker-compose files provided in repository. Further instructions to follow.
Currently done for each individual topic model, is constant per corpus though. If that's a bottleneck - seems pretty fast so far - that justifies a moderate time investment (a few hours), LDADataset could be a mutual object in all spawned Java threads/tasks.
Base image: https://hub.docker.com/r/anapsix/alpine-java/.
Apart from that not much do include. Is started as container in same network; topic generation is done by executing spawning process taking parameters by docker exec
. Spawning process might be calling java on nexus class directly.
Modified source: https://github.com/rmitsch/JGibbLabeledLDA
Especially performance - suitable for large corpora?
Depends on #1.
Involves significant restructuring of existing topic generation code in backend - no direct generation with gensim (except for case of unbound topics), no topic chaining, cohesion and projection calculate on data loaded from DB instead of live data, etc.
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