NLP-Models-Tensorflow/text-classification at master · mesolitica/NLP-Models-Tensorflow

History

Name		Name	Last commit message	Last commit date
parent directory ..
data		data
1.basic-rnn.ipynb		1.basic-rnn.ipynb
10.lstm-rnn-bidirectional.ipynb		10.lstm-rnn-bidirectional.ipynb
11.lstm-rnn-bidirectional-huber.ipynb		11.lstm-rnn-bidirectional-huber.ipynb
12.lstm-rnn-dropout-l2.ipynb		12.lstm-rnn-dropout-l2.ipynb
13.gru-rnn.ipynb		13.gru-rnn.ipynb
14.gru-rnn-hinge.ipynb		14.gru-rnn-hinge.ipynb
15.gru-rnn-huber.ipynb		15.gru-rnn-huber.ipynb
16.gru-rnn-bidirectional.ipynb		16.gru-rnn-bidirectional.ipynb
17.gru-rnn-bidirectional-hinge.ipynb		17.gru-rnn-bidirectional-hinge.ipynb
18.gru-rnn-bidirectional-huber.ipynb		18.gru-rnn-bidirectional-huber.ipynb
19.lstm-cnn-rnn.ipynb		19.lstm-cnn-rnn.ipynb
2.basic-rnn-hinge.ipynb		2.basic-rnn-hinge.ipynb
20.kmax-cnn.ipynb		20.kmax-cnn.ipynb
21.lstm-cnn-rnn-highway.ipynb		21.lstm-cnn-rnn-highway.ipynb
22.lstm-rnn-attention.ipynb		22.lstm-rnn-attention.ipynb
23.dilated-rnn-lstm.ipynb		23.dilated-rnn-lstm.ipynb
24.lnlstm-rnn.ipynb		24.lnlstm-rnn.ipynb
25.only-attention.ipynb		25.only-attention.ipynb
26.multihead-attention.ipynb		26.multihead-attention.ipynb
27.neural-turing-machine.ipynb		27.neural-turing-machine.ipynb
28.lstm-seq2seq.ipynb		28.lstm-seq2seq.ipynb
29.lstm-seq2seq-luong.ipynb		29.lstm-seq2seq-luong.ipynb
3.basic-rnn-huber.ipynb		3.basic-rnn-huber.ipynb
30.lstm-seq2seq-bahdanau.ipynb		30.lstm-seq2seq-bahdanau.ipynb
31.lstm-seq2seq-beam.ipynb		31.lstm-seq2seq-beam.ipynb
32.lstm-seq2seq-birnn.ipynb		32.lstm-seq2seq-birnn.ipynb
33.pointer-net.ipynb		33.pointer-net.ipynb
34.lstm-rnn-bahdanau.ipynb		34.lstm-rnn-bahdanau.ipynb
35.lstm-rnn-luong.ipynb		35.lstm-rnn-luong.ipynb
36.lstm-rnn-bahdanau-luong.ipynb		36.lstm-rnn-bahdanau-luong.ipynb
37.lstm-birnn-bahdanau-luong.ipynb		37.lstm-birnn-bahdanau-luong.ipynb
38.bytenet.ipynb		38.bytenet.ipynb
39.fast-slow-lstm.ipynb		39.fast-slow-lstm.ipynb
4.basic-rnn-bidirectional.ipynb		4.basic-rnn-bidirectional.ipynb
40.siamese-network.ipynb		40.siamese-network.ipynb
41.estimator.ipynb		41.estimator.ipynb
42.capsule-rnn-lstm.ipynb		42.capsule-rnn-lstm.ipynb
43.capsule-seq2seq-lstm.ipynb		43.capsule-seq2seq-lstm.ipynb
44.capsule-birrn-seq2seq-lstm.ipynb		44.capsule-birrn-seq2seq-lstm.ipynb
45.nested-lstm.ipynb		45.nested-lstm.ipynb
46.lstm-seq2seq-highway.ipynb		46.lstm-seq2seq-highway.ipynb
47.triplet-loss-lstm.ipynb		47.triplet-loss-lstm.ipynb
48.dnc.ipynb		48.dnc.ipynb
49.convlstm.ipynb		49.convlstm.ipynb
5.basic-rnn-bidirectional-hinge.ipynb		5.basic-rnn-bidirectional-hinge.ipynb
50.temporalconvd.ipynb		50.temporalconvd.ipynb
51.batch-all-triplet-loss-lstm.ipynb		51.batch-all-triplet-loss-lstm.ipynb
52.fast-text.ipynb		52.fast-text.ipynb
53.gated-convolution-network.ipynb		53.gated-convolution-network.ipynb
54.simple-recurrent-units.ipynb		54.simple-recurrent-units.ipynb
55.lstm-han.ipynb		55.lstm-han.ipynb
56.bert.ipynb		56.bert.ipynb
57.dynamic-memory-network.ipynb		57.dynamic-memory-network.ipynb
58.entity-network.ipynb		58.entity-network.ipynb
59.memory-network.ipynb		59.memory-network.ipynb
6.basic-rnn-bidirectional-huber.ipynb		6.basic-rnn-bidirectional-huber.ipynb
60.char-sparse.ipynb		60.char-sparse.ipynb
61.residual-network.ipynb		61.residual-network.ipynb
62.residual-network-bahdanau.ipynb		62.residual-network-bahdanau.ipynb
63.deep-pyramid-cnn.ipynb		63.deep-pyramid-cnn.ipynb
63.deep-pyramid-cnn.py		63.deep-pyramid-cnn.py
64.transformer-xl.ipynb		64.transformer-xl.ipynb
65.transfer-learning-gpt2.ipynb		65.transfer-learning-gpt2.ipynb
66.quasi-rnn.ipynb		66.quasi-rnn.ipynb
67.tacotron.ipynb		67.tacotron.ipynb
68.slice-gru.ipynb		68.slice-gru.ipynb
69.slice-gru-bahdanau.ipynb		69.slice-gru-bahdanau.ipynb
7.lstm-rnn.ipynb		7.lstm-rnn.ipynb
70.wavenet.ipynb		70.wavenet.ipynb
71.transfer-learning-bert-base.ipynb		71.transfer-learning-bert-base.ipynb
72.transfer-learning-xlnet-large.ipynb		72.transfer-learning-xlnet-large.ipynb
73.lstm-birnn-max-avg.ipynb		73.lstm-birnn-max-avg.ipynb
74.transfer-learning-bert-base-6.ipynb		74.transfer-learning-bert-base-6.ipynb
75.transfer-learning-bert-large-12.ipynb		75.transfer-learning-bert-large-12.ipynb
76.transfer-learning-xlnet-base.ipynb		76.transfer-learning-xlnet-base.ipynb
77.transfer-learning-albert-base.ipynb		77.transfer-learning-albert-base.ipynb
78.electra-base.ipynb		78.electra-base.ipynb
79.electra-large.ipynb		79.electra-large.ipynb
8.lstm-rnn-hinge.ipynb		8.lstm-rnn-hinge.ipynb
9.lstm-rnn-huber.ipynb		9.lstm-rnn-huber.ipynb
README.md		README.md
bert_model.py		bert_model.py
data.zip		data.zip
dynamic_memory_network.py		dynamic_memory_network.py
entity_network.py		entity_network.py
gpt_2.py		gpt_2.py
modules.py		modules.py
utils.py		utils.py
xl.py		xl.py

README.md

How-to

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

Files

text-classification

Directory actions

More options

Directory actions

More options

Latest commit

History

text-classification

Folders and files

parent directory