This is a package for Distributed Embedding aggregation (e.g. average, weighted average or log weighted average). The name comes from the fact that the package can be used to bundle vectors (e.g. distributed word representations). Therefore in a certain sense to "stuff" them into a more general form. Besides, the name is also easy to remember, cool and it just happened to be Thanksgiving.
Currently, we only support a minimal CLI interface. However, R and python packages are on the TODO-list. To use the software you have to compile it first. For this you need a current C++17 compiler (clang or GNU g++) and the classic build tools (e.g. make).
make
sudo make install
stuffedturkey --help Although, there is no Python package yet, it can be quite easily used in jupyter(lab|hub)? using the bash-syntax. Here is a minimalistic example how it could be used in combination with gensim:
# TODO: insert paths
vec_path, agg_path, mapping = './...', './...', './...'
# save trained gensim model
model.wv.save_word2vec_format(vec_path, binary=False)
# call stuffedturkey (you have to install it first)
!stuffedturkey agg {vec_path} {agg_path} {mapping} --mode log_weighted
# now you can load the aggregated embedding
agg_model = KeyedVectors.load_word2vec_format(agg_path, binary=False)- add other input and output formats (e.g., json, bin)
- python package
- R package
- bigquery format export
- process gensim/word2vec-bin format