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Unzip dataset.tar.gz
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Run any notebook using Jupyter Notebook.
Based on 20 epochs accuracy. The results will be different on different dataset. Trained on a GTX 960, 4GB VRAM.
name | accuracy |
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1.basic-seq2seq-manual | 0.816000 |
2.lstm-seq2seq-manual | 0.735000 |
3.gru-seq2seq-manual | 0.846833 |
4.basic-seq2seq-api-greedy | 1.009119 |
5.lstm-seq2seq-api-greedy | 0.984596 |
6.gru-seq2seq-greedy | 1.008869 |
7.basic-birnn-seq2seq-manual | 0.990333 |
8.lstm-birnn-seq2seq-manual | 0.732833 |
9.gru-birnn-seq2seq-manual | 0.936667 |
10.basic-birnn-seq2seq-greedy | 1.009586 |
11.lstm-birnn-seq2seq-greedy | 0.991938 |
12.gru-birnn-seq2seq-greedy | 1.008791 |
13.basic-seq2seq-luong | 0.821167 |
14.lstm-seq2seq-luong | 0.723167 |
15.gru-seq2seq-luong | 0.751667 |
16.basic-seq2seq-bahdanau | 0.811833 |
17.lstm-seq2seq-bahdanau | 0.721833 |
18.gru-seq2seq-bahdanau | 0.728167 |
19.lstm-birnn-seq2seq-luong | 0.728500 |
20.gru-birnn-seq2seq-luong | 0.743833 |
21.lstm-birnn-seq2seq-bahdanau | 0.718833 |
22.gru-birnn-seq2seq-bahdanau | 0.746667 |
23.lstm-birnn-seq2seq-bahdanau-luong | 0.721000 |
24.gru-birnn-seq2seq-bahdanau-luong | 0.747667 |
25.lstm-seq2seq-greedy-luong | 0.974864 |
26.gru-seq2seq-greedy-luong | 0.999175 |
27.lstm-seq2seq-greedy-bahdanau | 0.987874 |
28.gru-seq2seq-greedy-bahdanau | 1.000434 |
29.lstm-seq2seq-beam | 0.874802 |
30.gru-seq2seq-beam | 0.905397 |
31.lstm-birnn-seq2seq-beam-luong | 0.913772 |
32.gru-birnn-seq2seq-beam-luong | 0.856824 |
33.lstm-birnn-seq2seq-luong-bahdanau-stack-beam | 0.732801 |
34.gru-birnn-seq2seq-luong-bahdanau-stack-beam | 0.756537 |
35.byte-net | 0.877510 |
36.estimator | |
37.capsule-lstm-seq2seq-greedy | 0.655007 |
38.capsule-lstm-seq2seq-luong-beam | 0.275569 |
39.lstm-birnn-seq2seq-luong-bahdanau-stack-beam-dropout-l2 | 0.312999 |
40.dnc-seq2seq-bahdanau-greedy | 0.962712 |
41.lstm-birnn-seq2seq-beam-luongmonotic | 0.917333 |
42.lstm-birnn-seq2seq-beam-bahdanaumonotic | 0.929333 |
43.memory-network-basic | 0.945333 |
44.memory-network-lstm | 0.900000 |
45.attention-is-all-you-need | 0.704549 |
46.transformer-xl | 0.874486 |
47.attention-is-all-you-need-beam-search | 0.836433 |
48.transformer-xl-lstm | 0.826571 |
49.gpt-2-lstm | 0.645157 |
50.conv-encoder-conv-decoder | 0.518504 |
51.conv-encoder-lstm | 0.924609 |
52.tacotron-greedy | 0.876267 |
53.tacotron-beam | 0.855140 |
54.google-nmt | 1.006089 |