sgns_pytorch
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Pytorch implementation of Skip-gram Negative Sampling and RNN-based SGNS. Sample dataset is Harry Potter series. The quality of the embedding is monitored with PIP loss with SPPMI matrix.
Pytorch 1.0
tensorboardX
nltk
python trainer.py --multi-gpu --num-gpu 4
python trainer.py --multi-node --backend nccl --init-method nccl://master.ip.address:port --rank 0 --world-size 4
| model | node | sync | gpu | embedding | batch | time/epoch | lowest PIP loss |
|---|---|---|---|---|---|---|---|
| sgns | 4 | async | 4 | 200 | 8192 * 4 | 46.5 | - |
| sgns | 4 | sync | 4 | 200 | 8192 * 4 | 52.79 | 193.6 |
| sgns | 4 | sync | 4 | 200 | 1024 * 4 | 394 | - |
| sgns | 4 | sync | 4 | 50 | 8192 * 4 | 16.93 | 44.12 |
| sgns | 4 | sync | 4 | 50 | 1024 * 4 | 93.81 | 44.21 |
| sgns | 1 | - | 1 | 200 | 8192 | 28.6 | 129.3 |
| sgns | 1 | - | 1 | 200 | 1024 | 34.1 | 123.6 |
| sgns | 1 | - | 1 | 50 | 8192 | 29 | 15.1885 |
| sgns | 1 | - | 1 | 50 | 1024 | 21.6 | 14.52 |
| sgns | 1 | - | 4 | 200 | 8192 * 4 | 24.1 | - |
| sgns | 1 | - | 4 | 200 | 1024 * 4 | 25.37 | 129.6 |
| sgns | 1 | - | 4 | 50 | 8192 * 4 | 21.28 | - |
| sgns | 1 | - | 4 | 50 | 1024 * 4 | 24.08 | 15.44 |
| rnn | 1 | - | 1 | 200 | 1024 | 1133.9 | 1.11 |
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