Cython general implementation of the Glove multi-threaded training.

GloVe is an unsupervised learning algorithm for generating vector representations for words. Training is done using a co-occcurence matrix from a corpus. The resulting representations contain structure useful for many other tasks.

The paper describing the model is here.

The original implementation for this Machine Learning model can be found here.

@author Jonathan Raiman

To use this package you need a sparse co-occurence matrix. This matrix is represented by nested dictionaries that use ints as keys with a 0-index.

For instance below we have a corpus of 3 indices. Below 0 co-occurs with 2, 3.5 times:

import glove

cooccur = {
	0: {
		0: 1.0,
		2: 3.5
	},
	1: {
		2: 0.5
	},
	2: {
		0: 3.5,
		1: 0.5,
		2: 1.2
	}
}

model = glove.Glove(cooccur, d=50, alpha=0.75, x_max=100.0)

for epoch in range(25):
    err = model.train(batch_size=200, workers=9, batch_size=50)
    print("epoch %d, error %.3f" % (epoch, err), flush=True)

The trained embeddings are now present under model.W.

The model is controlled by setting several hyperpameters.

• cooccurence dict<int, dict<int, float>> : the co-occurence matrix
• alpha float : (default 0.75) hyperparameter for controlling the exponent for normalized co-occurence counts.
• x_max float : (default 100.0) hyperparameter for controlling smoothing for common items in co-occurence matrix.
• d int : (default 50) how many embedding dimensions for learnt vectors
• seed int : (default 1234) the random seed

• step_size float : the learning rate for the model
• workers int : number of worker threads used for training
• batch_size int : how many examples should each thread receive (controls the size of the job queue)