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]
This is a long list of all submissions, if you want to see only the best, click leaderboard.
# | 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 |
Showing 1 to 50 of 125 entries