Skipgram Negative Sampling in PyTorch

Last update: Dec 14, 2022

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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

GitHub Repository

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Skipgram Negative Sampling in PyTorch

Related tags

Overview

PyTorch SGNS

Owner

Jamie J. Seol

This repository is the official implementation of Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models

Unsupervised phone and word segmentation using dynamic programming on self-supervised VQ features.

Reproduce results and replicate training fo T0 (Multitask Prompted Training Enables Zero-Shot Task Generalization)

Repository for benchmarking graph neural networks

Byte-based multilingual transformer TTS for low-resource/few-shot language adaptation.

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SOLO and SOLOv2 for instance segmentation, ECCV 2020 & NeurIPS 2020.

The Unsupervised Reinforcement Learning Benchmark (URLB)

Creating Artificial Life with Reinforcement Learning

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GitHub repository for the ICLR Computational Geometry & Topology Challenge 2021

Contains modeling practice materials and homework for the Computational Neuroscience course at Okinawa Institute of Science and Technology

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