Turkish WebVectors: Tool to explore semantic relations between words in distributional models. Based on WebVectors prepared by Kutuzov et al. [1].
- First, clone the repository from GitHub to local machine using the following command:
git clone https://github.com/akutuzov/webvectors.git .- Then, create a virtual environment using the following command:
python -m venv env- Then, install all the Python requirements using the following command:
pip install -r requirements.txt- Gunicorn is a pure Python WSGI server with simple configuration and multiple worker implementations for performance tuning. It is used for production deployment. Install
gunicornas follows:
pip install gunicorn- First, clone the repository from GitHub to local machine using the following command:
git clone https://github.com/Turkish-Word-Embeddings/Turkish-WebVectors.git . - Launch a terminal in the root directory of the repo and build the Docker image where
-tis the tag for the Docker image. You can provide any name you want..is the relative path to the Dockerfile
docker build -t turkish-webvectors .- Dockerfile downloads the necessary two models from our release page. You can also download them manually and put them in the
modelsdirectory. - Run the Docker image using the following command:
docker run -p 8000:8000 turkish-webvectorsBoth of the following models are trained with gensim using 300 dimensions, 10 epochs, 5 negative samples, minimum word count set to 10 and window size set to 5.
Word2Vec skip-gram Negative SamplingFastText skip-gram Negative Sampling
- Kutuzov, Andrei & Kuzmenko, Elizaveta. (2017). WebVectors: A Toolkit for Building Web Interfaces for Vector Semantic Models. Communications in Computer and Information Science. 661. 155-161. 10.1007/978-3-319-52920-2_15.