Skipgram Negative Sampling in PyTorch

Overview

PyTorch SGNS

Word2Vec's SkipGramNegativeSampling in Python.

Yet another but quite general negative sampling loss implemented in PyTorch.

It can be used with ANY embedding scheme! Pretty fast, I bet.

vocab_size = 20000
word2vec = Word2Vec(vocab_size=vocab_size, embedding_size=300)
sgns = SGNS(embedding=word2vec, vocab_size=vocab_size, n_negs=20)
optim = Adam(sgns.parameters())
for batch, (iword, owords) in enumerate(dataloader):
    loss = sgns(iword, owords)
    optim.zero_grad()
    loss.backward()
    optim.step()

New: support negative sampling based on word frequency distribution (0.75th power) and subsampling (resolving word frequency imbalance).

To test this repo, place a space-delimited corpus as data/corpus.txt then run python preprocess.py and python train.py --weights --cuda (use -h option for help).

Owner
Jamie J. Seol
@theeluwin
Jamie J. Seol
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