Sane words challenge

Guess if a given word is a correct Polish word in a given domain. Additionally, you have the information on reported frequency of the word in source texts. [ver. 1.0.0]

# submitter when ver. description dev-0 F2.0 test-A F2.0
100 [anonymized] 2020-01-27 13:02 1.0.0 16 neurons and 1 layer lr python linear-regression 0.25038 0.22124
98 [anonymized] 2019-12-17 16:52 1.0.0 dlugosci+ fixed python linear-regression 0.25065 0.22367
82 [anonymized] 2019-12-04 21:56 1.0.0 the best so far python pytorch-nn 0.30188 0.29350
78 [anonymized] 2019-12-04 19:29 1.0.0 pytorch-nn more iterations python pytorch-nn 0.35172 0.33117
106 [anonymized] 2019-12-04 10:09 1.0.0 dev-0 update python neural-network pytorch-nn 0.14078 0.21551
105 [anonymized] 2019-12-04 09:58 1.0.0 dev-0 update 0.00000 0.21551
104 [anonymized] 2019-12-04 09:44 1.0.0 ff network with tanh activation, batch size = 32 with 5000 epochs neural-network pytorch-nn N/A 0.21551
76 [anonymized] 2019-12-04 09:02 1.0.0 torch-nn python pytorch-nn 0.32339 0.33287
99 [anonymized] 2019-12-04 08:58 1.0.0 fasttext + nn python pytorch-nn 0.19271 0.22230
84 [anonymized] 2019-12-04 08:28 1.0.0 Pytorch nn, minibatch, improved previous solution python pytorch-nn 0.27727 0.28049
90 [anonymized] 2019-12-04 08:16 1.0.0 Pytorch nn, minibatch, improved previous solution python pytorch-nn 0.26027 0.24894
89 [anonymized] 2019-12-04 00:50 1.0.0 NN pytorch python pytorch-nn 0.25732 0.25147
97 [anonymized] 2019-12-04 00:08 1.0.0 sane_words_torch_nn_onehot python pytorch-nn 0.23461 0.22573
119 [anonymized] 2019-12-03 23:39 1.0.0 Attempt #3 0.16253 0.10073
108 [anonymized] 2019-12-03 23:24 1.0.0 test4 python pytorch-nn 0.20663 0.20459
118 [anonymized] 2019-12-03 22:16 1.0.0 test 0.09896 0.10610
73 Artur Nowakowski 2019-12-03 21:43 1.0.0 PyTorch simple NN python pytorch-nn 0.33812 0.33842
83 [anonymized] 2019-12-03 20:31 1.0.0 pytorch neural network second solution python pytorch-nn 0.30982 0.29079
111 [anonymized] 2019-12-03 17:01 1.0.0 Test for nn python pytorch-nn 0.17521 0.18517
101 [anonymized] 2019-12-03 14:08 1.0.0 Improve results python linear-regression 0.21041 0.21882
115 [anonymized] 2019-12-01 00:47 1.0.0 fixed regression python linear-regression 0.16082 0.15625
88 [anonymized] 2019-11-30 19:50 1.0.0 very simple solution with torch.nn python pytorch-nn 0.27827 0.25664
16 [anonymized] 2019-11-30 18:59 1.0.0 Neural net 512x256 one hot word python pytorch-nn 0.47321 0.44029
113 [anonymized] 2019-11-29 13:57 1.0.0 Sane words, with score python linear-regression 0.18430 0.17401
112 [anonymized] 2019-11-29 13:29 1.0.0 Sane words, early stopping python linear-regression 0.18712 0.18134
79 [anonymized] 2019-11-27 18:11 1.0.0 Simple nn solution with torch (stupid params???) python pytorch-nn 0.34200 0.32922
125 [anonymized] 2019-11-27 10:22 1.0.0 dlugosci+ python linear-regression 0.16112 N/A
86 [anonymized] 2019-11-27 09:35 1.0.0 solution python linear-regression 0.29004 0.26632
94 [anonymized] 2019-11-27 07:49 1.0.0 solution 16N python linear-regression 0.27095 0.24304
96 [anonymized] 2019-11-27 06:45 1.0.0 LR with 16 neurons python linear-regression 0.26102 0.23550
91 [anonymized] 2019-11-27 06:23 1.0.0 My solution python linear-regression 0.24028 0.24635
116 [anonymized] 2019-11-27 01:10 1.0.0 NN 16 python linear-regression 0.16082 0.15624
93 [anonymized] 2019-11-26 21:36 1.0.0 logistic regresion python linear-regression 0.27123 0.24328
95 [anonymized] 2019-11-26 21:13 1.0.0 Neural network python linear-regression 0.25245 0.23634
92 [anonymized] 2019-11-26 13:15 1.0.0 Neural net 1 hidden layer 16 neurons python linear-regression 0.27130 0.24335
87 [anonymized] 2019-11-22 02:02 1.0.0 32x32 selfmade NN pytorch alpha -1.5 420k loop python linear-regression 0.27660 0.26247
107 [anonymized] 2019-11-21 20:40 1.0.0 slightly better nn python linear-regression 0.21801 0.21084
114 [anonymized] 2019-11-21 20:00 1.0.0 simple nn with silly function on words python linear-regression 0.16670 0.16175
64 p/tlen 2018-05-22 11:44 1.0.0 adaboost, f-measure on train=0.4200 0.37069 0.36488
85 p/tlen 2018-05-22 09:28 1.0.0 decision tree, classes balanced, f-measure on train=0.9985 decision-tree 0.24223 0.26697
74 p/tlen 2018-05-22 09:15 1.0.0 decision tree, classes balanced, f-measure on train=0.4610 scikit-learn decision-tree 0.33214 0.33503
75 p/tlen 2018-05-22 07:48 1.0.0 random forest, n_estimators=100, max_depth=4, oob_score, f-measure on train=0.3805 scikit-learn random-forest 0.34976 0.33436
81 p/tlen 2018-05-22 07:34 1.0.0 RandomForest, n_estimators=5, classes balanced, f-measure on train=0.3884 scikit-learn random-forest 0.31619 0.29907
62 p/tlen 2018-05-22 07:27 1.0.0 bagging, oob_score, classes balanced, n_estimators=5, f-measure on train=0.5268 scikit-learn bagging 0.37227 0.37147
70 p/tlen 2018-05-22 07:19 1.0.0 bagging, class balanced, n_estimators=5, max_depth=10, f-measure on train=0.5237 bagging 0.36852 0.34739
120 p/tlen 2018-05-22 07:12 1.0.0 bagging, n_estimators=20, f-measure on train=0.1891 0.07812 0.08035
121 p/tlen 2018-05-22 06:52 1.0.0 bagging with 5 estimators, f-measure on train=0.2103 scikit-learn bagging 0.10113 0.08005
117 p/tlen 2018-05-22 06:38 1.0.0 decision tree, max_depth=10, f-measure on train=0.2918 scikit-learn decision-tree 0.13576 0.13388
110 p/tlen 2018-05-22 06:34 1.0.0 decision tree, max_depth=20, f-measure on train=0.6321 scikit-learn decision-tree 0.18003 0.19148
123 p/tlen 2018-05-22 06:32 1.0.0 forgotten test-A/out.tsv, f-measure on train=0.0609 scikit-learn decision-tree 0.03313 0.03102
103 p/tlen 2018-05-22 06:30 1.0.0 decision tree, max_depth=5, f-measure on train=0.0609 scikit-learn decision-tree 0.03313 0.21750
102 p/tlen 2018-05-22 06:24 1.0.0 decision tree, F-measure on train=0.9941 scikit-learn decision-tree 0.22011 0.21750
36 [anonymized] 2018-02-09 15:52 1.0.0 'baseline' neural-network N/A 0.42429
55 [anonymized] 2018-02-09 12:42 1.0.0 'baseline' N/A 0.38481
51 [anonymized] 2018-02-09 12:02 1.0.0 'baseline' N/A 0.39674
58 [anonymized] 2018-02-09 11:12 1.0.0 'baseline' N/A 0.38057
56 [anonymized] 2018-02-09 10:35 1.0.0 'baseline' N/A 0.38212
54 [anonymized] 2018-02-09 09:53 1.0.0 'baseline' N/A 0.38857
65 [anonymized] 2018-02-09 09:25 1.0.0 'baseline' N/A 0.36284
60 [anonymized] 2018-02-09 09:12 1.0.0 'baseline' N/A 0.37440
63 [anonymized] 2018-02-09 08:50 1.0.0 'baseline' N/A 0.36978
67 [anonymized] 2018-02-09 08:23 1.0.0 'baseline' N/A 0.35445
59 [anonymized] 2018-02-09 08:18 1.0.0 baseline max_words=40 batch_size=512 nb_epoch=64 neural-network N/A 0.37637
69 [anonymized] 2018-02-09 07:56 1.0.0 baseline max_words=50 batch_size=512 nb_epoch=32 neural-network N/A 0.34892
17 [anonymized] 2018-01-28 13:53 1.0.0 test 11 0.45326 0.43913
29 [anonymized] 2018-01-28 13:52 1.0.0 test 11 0.45424 0.43052
14 [anonymized] 2018-01-28 13:52 1.0.0 test 11 0.45849 0.44207
35 [anonymized] 2018-01-28 00:05 1.0.0 test 11 0.45119 0.42569
40 [anonymized] 2018-01-28 00:03 1.0.0 test 11 0.44995 0.40997
26 [anonymized] 2018-01-28 00:03 1.0.0 test 11 0.45091 0.43424
3 [anonymized] 2018-01-28 00:02 1.0.0 test 11 0.45184 0.45519
32 [anonymized] 2018-01-28 00:00 1.0.0 test 11 0.45758 0.42813
33 [anonymized] 2018-01-27 23:59 1.0.0 test 11 0.45050 0.42735
12 [anonymized] 2018-01-27 23:58 1.0.0 test 11 0.45236 0.44244
10 [anonymized] 2018-01-27 23:57 1.0.0 test 11 0.45505 0.44394
23 [anonymized] 2018-01-27 23:56 1.0.0 test 11 0.45155 0.43682
25 [anonymized] 2018-01-27 13:15 1.0.0 test 11 0.45155 0.43463
37 [anonymized] 2018-01-27 01:04 1.0.0 test 11 0.44717 0.41822
5 [anonymized] 2018-01-27 01:03 1.0.0 test 11 0.45306 0.45387
31 [anonymized] 2018-01-27 01:02 1.0.0 test 11 0.45758 0.42813
13 [anonymized] 2018-01-27 01:00 1.0.0 test 11 0.45188 0.44223
15 [anonymized] 2018-01-27 00:59 1.0.0 test 11 0.45438 0.44090
22 [anonymized] 2018-01-27 00:57 1.0.0 test 11 0.45188 0.43763
21 [anonymized] 2018-01-27 00:56 1.0.0 test 11 0.45438 0.43763
20 [anonymized] 2018-01-27 00:56 1.0.0 test 11 0.45673 0.43763
11 [anonymized] 2018-01-26 16:45 1.0.0 test 10 0.46167 0.44289
4 [anonymized] 2018-01-26 16:17 1.0.0 test 10 0.45167 0.45438
6 [anonymized] 2018-01-26 16:09 1.0.0 test 10 0.46217 0.44931
18 [anonymized] 2018-01-26 15:32 1.0.0 test 10 0.44949 0.43825
9 [anonymized] 2018-01-26 15:31 1.0.0 test 10 0.45471 0.44504
7 [anonymized] 2018-01-26 15:29 1.0.0 test 10 0.45609 0.44689
27 [anonymized] 2018-01-26 15:29 1.0.0 test 10 0.45632 0.43122
24 [anonymized] 2018-01-26 15:28 1.0.0 test 9 0.45851 0.43470
2 [anonymized] 2018-01-26 11:16 1.0.0 test 8 0.46317 0.46489
1 [anonymized] 2018-01-26 10:59 1.0.0 Fixed class weight neural-network 0.45671 0.46603
30 [anonymized] 2018-01-26 10:40 1.0.0 test 6 0.44521 0.42891
19 [anonymized] 2018-01-26 10:36 1.0.0 test 5 0.43805 0.43823
28 [anonymized] 2018-01-26 10:32 1.0.0 test 4 0.44978 0.43112
8 [anonymized] 2018-01-24 23:53 1.0.0 Found nice parameters neural-network 0.45680 0.44607
34 [anonymized] 2018-01-24 16:05 1.0.0 test 2 0.45021 0.42614
68 [anonymized] 2018-01-10 16:07 1.0.0 test 0.36588 0.35159
41 [anonymized] 2018-01-10 15:14 1.0.0 max_word=80, batch_size=256, activition=relu, optymizer=rmsprob, nb_epoch=200 GPU 0.41722 0.40898
38 [anonymized] 2018-01-09 19:32 1.0.0 max_word=80, batch_size=256, activition=relu, optymizer=rmsprob, nb_epoch=200 neural-network 0.41279 0.41575
61 [anonymized] 2018-01-09 13:57 1.0.0 Keras 2.1.2 0.38394 0.37312
47 [anonymized] 2017-01-08 10:59 1.0.0 approximate frequencies 0.41749 0.40122
39 [anonymized] 2017-01-07 22:08 1.0.0 approximate frequencies 0.41871 0.41393
43 [anonymized] 2017-01-07 16:40 1.0.0 spellcheck suggestions 0.41217 0.40568
44 [anonymized] 2017-01-06 18:24 1.0.0 letter histograms 0.41605 0.40561
42 [anonymized] 2017-01-06 18:10 1.0.0 improved learning rate 0.41461 0.40665
48 [anonymized] 2017-01-06 18:06 1.0.0 improved spellchecking 0.41153 0.40112
52 p/tlen 2016-12-27 13:33 1.0.0 ensemble of 3 simple neural networks 0.41928 0.39376
53 p/tlen 2016-12-27 11:31 1.0.0 averaged 3 sub-NNs 0.41583 0.39254
57 p/tlen 2016-12-27 11:29 1.0.0 averaged 3 sub-NNs 0.41583 0.38095
46 p/tlen 2016-12-27 09:56 1.0.0 simple neural network with frequencies taken into account (tanh) 0.42156 0.40284
45 p/tlen 2016-12-27 09:14 1.0.0 simple neural network with frequencies taken into account neural-network 0.42670 0.40410
72 p/tlen 2016-12-27 09:07 1.0.0 simple neural network with frequencies taken into account 0.42670 0.34614
50 p/tlen 2016-12-26 17:18 1.0.0 simple neural network without drop-out 0.39933 0.40049
66 p/tlen 2016-12-23 22:07 1.0.0 simpler neural network but trained longer 0.37556 0.35480
77 p/tlen 2016-12-23 21:16 1.0.0 even simpler neural network 0.34912 0.33141
122 [anonymized] 2016-12-19 20:14 1.0.0 random yolo with correct output file xD N/A 0.06709
124 [anonymized] 2016-12-19 20:08 1.0.0 random yolo N/A N/A
49 [anonymized] 2016-12-15 20:26 1.0.0 tuned parameters 0.42095 0.40102
71 [anonymized] 2016-12-13 23:47 1.0.0 vw ngrams, suffixes, spellcheckers 0.39271 0.34730
80 p/tlen 2016-12-12 21:52 1.0.0 stupid neural network by night 0.33369 0.31117
109 p/tlen 2016-12-10 14:50 1.0.0 trivial solution (handcrafted one-liner) 0.24359 0.19574