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How-to

  1. Unzip dataset.tar.gz

  2. Run any notebook using Jupyter Notebook.

Accuracy, not sorted

Based on training accuracy for 20 epochs.

name accuracy
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