This is a fork of https://github.com/TatsuyaShirakawa/poincare-embedding, which was designed to train word embeddings from WordNet pairs.
This implementation allows training word embeddings on arbitrary text files using either the continuous-bag-of-words or skipgram models.
I eventually plan to combine this with Facebook's fastText (e.g. char-ngrams), but so far there have only been a few minor additions.
Gitter link: https://gitter.im/poincare-embeddings/Lobby
- gcc-4.6.3 / CMake 3 or newer.
cd poincare-embedding
mkdir work & cd work
cmake ..
makepoincare-embedding/work/poincare-embedding [input_file] [output_file] -arg1 -arg2.....
The executable accepts any text file consisting of words separated by a space (i.e. sentences). For each word in the text file, the Poincare model is then trained on a sliding window to the left and right of that word. The model will train across sentence boundaries (e.g. full-stops) but not across newlines.
result_embeddng_file : string result file into which resulting embeddings are written
-s, --seed : int >= 0 random seed
-t, --num_threads : int > 0 number of threads
-m, --model : string model name ("cbow" (default) or "skipgram")
-n, --neg_size : int > 0 negativ sample size
-e, --max_epoch : int > 0 maximum training epochs
-d, --dim : int > 0 dimension of embeddings
-u, --uniform_range : float > 0 embedding uniform initializer range
-l, --learning_rate_init : float > 0 initial learning rate
-L, --learning_rate_final : float > 0 final learning rate
-v, --verbose : int 0,1 verbosity
-w, --window_size : int >= 0 window size for CBOW```
## References
[Poincaré Embeddings for Learning Hierarchical Representations](https://arxiv.org/abs/1705.08039)
[Neural Embeddings of Graphs in Hyperbolic Space](https://arxiv.org/abs/1705.10359)