Entropy, Zipf's law and distributional semantics
MIT License
Generate count-based Distributional Semantic Models by sampling SVD singular vectors instead of using top components.
pip install entropix
or, after a git clone:
python3 setup.py install
entropix sample \
--model /abs/path/to/dense/numpy/model.npy \
--vocab /abs/path/to/corresponding/model.vocab \
--dataset dataset_to_optimize_on \ # men, simlex or simverb
--shuffle \
--mode seq \
--kfold-size .2 \ # size of kfold, between 0 and .5
--metric pearson \ # spr(spearman), pearson, rmse or both (spr+rmse)
--num-threads 5
entropix sample \
--model /abs/path/to/dense/numpy/model.npy \
--vocab /abs/path/to/corresponding/model.vocab \
--dataset dataset_to_optimize_on \ # men, simlex or simverb
--mode limit \
--metric pearson \
--limit 10 # number of dimensions to sample