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0ecc562 · Apr 18, 2020

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text-classification

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How-to

  1. Make sure data folder in the same directory of the notebooks.

  2. Run any notebook using Jupyter Notebook.

Score and average time taken per epoch, not sorted

Based on 20% validation, time taken based on single Tesla V100 32GB VRAM.

name accuracy time taken (s)
1. basic-rnn 0.68 1.3219
2. basic-rnn-hinge 0.65 1.2455
3. basic-rnn-huber 0.68 1.2468
4. basic-rnn-bidirectional 0.71 3.8174
5. basic-rnn-bidirectional-hinge 0.68 2.5127
6. basic-rnn-bidirectional-huber 0.63 3.5095
7. lstm-rnn 0.73 2.69683
8. lstm-rnn-hinge 0.72 8.2088
9. lstm-rnn-huber 0.73 10.1754
10. lstm-rnn-bidirectional 0.71 11.0388
11. lstm-rnn-bidirectional-huber 0.71 5.5258
12. lstm-rnn-dropout-l2 0.74 3.2420
13. gru-rnn 0.72 3.16123
14. gru-rnn-hinge 0.72 6.71951
15. gru-rnn-huber 0.70 7.93373
16. gru-rnn-bidirectional 0.73 2.91590
17. gru-rnn-bidirectional-hinge 0.72 5.66385
18. gru-rnn-bidirectional-huber 0.70 18.01133
19. lstm-cnn-rnn 0.65 4.42849
20. kmax-cnn 0.73 18.89667
21. lstm-cnn-rnn-highway 0.68 3.23122
22. lstm-rnn-attention 0.75 13.97496
23. dilated-rnn-lstm 0.25 24.54002
24. lnlstm-rnn 0.68 24.86363
25. only-attention 0.74 2.63291
26. multihead-attention 0.69 9.033228
27. neural-turing-machine
28. lstm-seq2seq 0.72 9.63291
29. lstm-seq2seq-luong
30. lstm-seq2seq-bahdanau
31. lstm-seq2seq-beam
32. lstm-seq2seq-birnn
33. pointer-net
34. lstm-rnn-bahdanau 0.71 9.81993
35. lstm-rnn-luong 0.66 27.73932
36. lstm-rnn-bahdanau-luong 0.69 36.97628
37. lstm-birnn-bahdanau-luong 0.70 38.86009
38. bytenet
39. fast-slow-lstm
40. siamese-network 0.52 7.13535
41. estimator
42. capsule-rnn-lstm
43. capsule-seq2seq-lstm
44. capsule-birrn-seq2seq-lstm
45. nested-lstm
46. lstm-seq2seq-highway
47. triplet-loss-lstm 0.50
48. dnc 0.68 85.98529
49. convlstm 0.69 2.66726
50. temporalconvd 0.66 11.90590
51. batch-all-triplet-loss-lstm 0.70
52. fast-text 0.76 0.49499
53. gated-convolution-network 0.67 3.37712
54. simple-recurrent-units 0.65 3.12624
55. lstm-han 0.50 3.47965
56. bert 0.73 6.31015
57. dynamic-memory-network 0.71 3.25820
58. entity-network 0.74 1.10458
59. memory-network 0.58 1.157306
60. char-sparse 0.76 2.350096
61. residual-network 0.72 9.557085
62. residual-network-bahdanau 0.71 11.53799
63. deep-pyramid-cnn 0.68 6.980528
64. transformer-xl 0.51 38.66338
65. transfer-learning-gpt2 0.79 178.0716
66. quasi-rnn 0.66 166.1456
67. tacotron 0.74 360.5551
68. slice-gru 0.72 10.140633
69. slice-gru-bahdanau 0.70 20.247409
70. wavenet 0.59 101.293274
71. transfer-learning-bert 0.81 887.590460
72. transfer-learning-xlnet-large 0.846 340.7679
73. lstm-birnn-max-avg 0.7552 9.35624
74. transfer-learning-bert-base-6 0.7655 494.169
75. transfer-learning-bert-large-12 0.80 1365.30
76. transfer-learning-xlnet-base 0.820441 240.262
77. transfer-learning-albert-base 0.799053 61.8179
78. transfer-learning-electra-base 0.836336 66.0257
79. transfer-learning-electra-large 0.875248 195.37280