glove-semantic-explorer 0.1.1

Easy to use CLI to set up embedding-explorer for deployment on a corpus with GloVe word embeddings

glove-semantic-explorer

Set up embedding explorer on a corpus with GloVe word embeddings with an easy-to-use CLI.

Installation

You can install the CLI from PyPI:

We recommend that you use a Linux/Unix system, preferably Debian when using this tool. Windows and MacOS could still work, but we do not guarrantee this.

pip install glove-semantic-explorer

Usage

1. Train a model

You will need a corpus in the format of a bunch of .txt files in a folder. Every line in a file should represent one sentence/passage.

To train a GloVe model on the corpus, run:

python3 -m glove_semantic_explorer train_model dat/ -o model/glove.kv

This will output a keyed vectors file to model/glove.kv.

2. Run the Explorer

To run the explorer on the trained model locally, run:

python3 -m glove_semantic_explorer run_explorer -m model/glove.kv --port 8080

This will start embedding-explorer on the trained embedding model on port 8080.

3. Deploy!

You can deploy the application using docker compose. The way this can be done with our CLI is by auto-generating a Dockerfile, a compose.yaml and a main.py file, that contains all the code for running the server.

To output this into a folder called deployment/, run the following command:

python3 -m glove_semantic_explorer generate_docker "your_project_name" -m model/glove.kv -p 8080 -o deployment/

Beware that the model file only gets mounted to the container, and thus should not be removed, moved or renamed.

To deploy the app with docker compose run the following:

cd deployment/
sudo docker compose up

The app will then run on port 8080.