"He Said She Said" classification challenge (2nd edition)
Give the probability that a text in Polish was written by a man. [ver. 2.0.2]
This is a long list of all submissions, if you want to see only the best, click leaderboard.
# | submitter | when | ver. | description | dev-0 Accuracy | dev-0 Likelihood | dev-1 Accuracy | dev-1 Likelihood | test-A Accuracy | test-A Likelihood | |
---|---|---|---|---|---|---|---|---|---|---|---|
86 | 444498 | 2023-11-06 15:39 | 2.0.2 | test old after key modify on new | 0.51347 | 0.00000 | 0.52350 | 0.00000 | 0.51307 | 0.00000 | |
85 | 444498 | 2023-05-19 07:07 | 2.0.2 | test tag request algo attention backoff | 0.64646 | 0.00000 | 0.63821 | 0.00000 | 0.63615 | 0.00000 | |
84 | wirus wirus | 2023-03-24 13:58 | 2.0.2 | test2 algo attention backoff | 0.51282 | 0.00000 | 0.50770 | 0.00000 | 0.51005 | 0.00000 | |
83 | wirus wirus | 2023-01-05 21:47 | 2.0.2 | test 5.01 10:44 | 0.66890 | 0.00000 | 0.65028 | 0.00000 | 0.65503 | 0.00000 | |
82 | wirus wirus | 2022-12-05 11:26 | 2.0.2 | test 12-05 12:25 | 0.68248 | 0.00000 | 0.67544 | 0.00000 | 0.67034 | 0.00000 | |
617 | wirus wirus | 2022-12-05 11:20 | 2.0.2 | test 12-05 12:19 python | N/A | N/A | N/A | N/A | N/A | N/A | |
81 | wirus wirus | 2022-12-05 11:15 | 2.0.2 | test 12-05 12:15 | 0.52244 | 0.00000 | 0.52444 | 0.00000 | 0.52032 | 0.00000 | |
80 | wirus wirus | 2022-11-08 12:05 | 2.0.2 | test ulumulu miki miki | 0.67610 | 0.00000 | 0.66838 | 0.00000 | 0.66439 | 0.00000 | |
79 | wirus wirus | 2022-11-08 11:51 | 2.0.2 | test python | 0.52509 | 0.00000 | 0.52555 | 0.00000 | 0.51885 | 0.00000 | |
78 | Kamil Guttmann | 2022-04-30 15:42 | 2.0.2 | s444380 logistic regression bigrams self-made | 0.68248 | 0.00000 | 0.67544 | 0.00000 | 0.67034 | 0.00000 | |
77 | 444498 | 2022-04-27 10:50 | 2.0.2 | 444498 self-made | 0.52509 | 0.00000 | 0.52555 | 0.00000 | 0.51885 | 0.00000 | |
76 | 444498 | 2022-04-27 10:42 | 2.0.2 | 444498 self-made | 0.50402 | 0.00000 | 0.50482 | 0.00000 | 0.50265 | 0.00000 | |
75 | s478815 | 2022-04-27 10:35 | 2.0.2 | 478815 self-made | 0.52244 | 0.00000 | 0.52444 | 0.00000 | 0.52032 | 0.00000 | |
74 | 444498 | 2022-04-27 10:31 | 2.0.2 | 444498 self-made | 0.51473 | 0.00000 | 0.51745 | 0.00000 | 0.50972 | 0.00000 | |
73 | s478815 | 2022-04-27 10:23 | 2.0.2 | 478815 self-made | 0.51300 | 0.00000 | 0.50881 | 0.00000 | 0.50827 | 0.00000 | |
72 | 444498 | 2022-04-27 10:17 | 2.0.2 | 444498 self-made | 0.51398 | 0.00000 | 0.51643 | 0.00000 | 0.50921 | 0.00000 | |
71 | s478815 | 2022-04-27 10:00 | 2.0.2 | 478815 self-made | 0.51344 | 0.00000 | 0.50862 | 0.00000 | 0.50825 | 0.00000 | |
70 | 444498 | 2022-04-27 09:51 | 2.0.2 | 444498 self-made | 0.51440 | 0.00000 | 0.51726 | 0.00000 | 0.50949 | 0.00000 | |
69 | 444498 | 2022-04-27 09:30 | 2.0.2 | 444498 self-made | 0.51446 | 0.00000 | 0.51723 | 0.00000 | 0.50947 | 0.00000 | |
68 | Jakub | 2022-04-27 09:04 | 2.0.2 | s434624 self-made | 0.51282 | 0.00000 | 0.50770 | 0.00000 | 0.51005 | 0.00000 | |
67 | s444354 | 2022-04-26 23:26 | 2.0.2 | s444354 | 0.51559 | 0.00000 | 0.51575 | 0.00000 | 0.51333 | 0.00000 | |
616 | s444354 | 2022-04-26 23:01 | 2.0.2 | s444354 self-made | N/A | N/A | N/A | N/A | N/A | N/A | |
66 | Cezary | 2022-04-26 22:57 | 2.0.2 | s470623 | 0.51418 | 0.00000 | 0.51001 | 0.00000 | 0.51036 | 0.00000 | |
615 | s444354 | 2022-04-26 22:49 | 2.0.2 | s444354 self-made | N/A | N/A | N/A | N/A | N/A | N/A | |
65 | Cezary | 2022-04-26 22:44 | 2.0.2 | s470623 self-made | 0.51078 | 0.00000 | 0.50838 | 0.00000 | 0.50799 | 0.00000 | |
614 | s444354 | 2022-04-26 22:41 | 2.0.2 | s444354 self-made | N/A | N/A | N/A | N/A | N/A | N/A | |
613 | s444354 | 2022-04-26 21:59 | 2.0.2 | s444354 self-made | N/A | N/A | N/A | N/A | N/A | N/A | |
64 | s443930 | 2022-04-26 21:44 | 2.0.2 | s443930 self-made | 0.65773 | 0.00000 | 0.64068 | 0.00000 | 0.64618 | 0.00000 | |
63 | s444452 | 2022-04-26 21:05 | 2.0.2 | 444452 self-made | 0.64646 | 0.00000 | 0.63821 | 0.00000 | 0.63615 | 0.00000 | |
62 | s444452 | 2022-04-26 20:54 | 2.0.2 | add solution self-made | 0.64646 | 0.00000 | 0.63821 | 0.00000 | 0.63615 | 0.00000 | |
61 | [anonymized] | 2022-04-26 20:41 | 2.0.2 | s478831 | 0.51529 | 0.00000 | 0.51265 | 0.00000 | 0.51241 | 0.00000 | |
60 | Adam Wojdyła | 2022-04-26 16:40 | 2.0.2 | 4444507 self-made | 0.51347 | 0.00000 | 0.52350 | 0.00000 | 0.51307 | 0.00000 | |
11 | Adam Wojdyła | 2022-04-26 16:15 | 2.0.2 | 4444507 self-made | 0.50000 | 0.49749 | 0.50000 | 0.49749 | 0.50000 | 0.49749 | |
59 | s444018 | 2022-04-26 14:19 | 2.0.2 | s444018 self-made | 0.51347 | 0.00000 | 0.52350 | 0.00000 | 0.51307 | 0.00000 | |
58 | Mikołaj Pokrywka | 2022-04-26 14:14 | 2.0.2 | 444463 self-made | 0.67610 | 0.00000 | 0.66838 | 0.00000 | 0.66439 | 0.00000 | |
57 | Mikołaj Pokrywka | 2022-04-26 11:27 | 2.0.2 | 444463 self-made | 0.67610 | 0.00000 | N/A | N/A | 0.66439 | 0.00000 | |
56 | s478840 | 2022-04-26 05:20 | 2.0.2 | s478840 self-made | 0.67845 | 0.00000 | 0.67162 | 0.00000 | 0.66531 | 0.00000 | |
55 | [anonymized] | 2022-04-25 23:49 | 2.0.2 | 478841 self-made | 0.67765 | 0.00000 | 0.67074 | 0.00000 | 0.66702 | 0.00000 | |
54 | s478873 | 2022-04-25 22:18 | 2.0.2 | s478873 self-made | 0.51935 | 0.00000 | 0.51592 | 0.00000 | 0.51352 | 0.00000 | |
53 | s478839 | 2022-04-25 22:13 | 2.0.2 | s478839 self-made | 0.51905 | 0.00000 | 0.51579 | 0.00000 | 0.51284 | 0.00000 | |
52 | s444465 | 2022-04-25 20:36 | 2.0.2 | Solution 444465 self-made | 0.64724 | 0.00000 | 0.63072 | 0.00000 | 0.62985 | 0.00000 | |
51 | s444465 | 2022-04-25 20:20 | 2.0.2 | Solution 444465 self-made | 0.64724 | 0.00000 | 0.63072 | 0.00000 | 0.66528 | 0.00000 | |
50 | s444465 | 2022-04-25 19:43 | 2.0.2 | solution self-made | 0.67675 | 0.00000 | 0.66973 | 0.00000 | 0.66528 | 0.00000 | |
49 | s444465 | 2022-04-25 19:14 | 2.0.2 | Gonito solution self-made | 0.67675 | 0.00000 | N/A | N/A | 0.66528 | 0.00000 | |
48 | [anonymized] | 2022-04-25 17:13 | 2.0.2 | 444421 self-made | 0.67569 | 0.00000 | 0.66856 | 0.00000 | 0.66394 | 0.00000 | |
47 | s444386 | 2022-04-25 15:56 | 2.0.2 | logistic regresion 444386 self-made | 0.68025 | 0.00000 | 0.67335 | 0.00000 | 0.66769 | 0.00000 | |
46 | s444517 | 2022-04-25 07:39 | 2.0.2 | s444517 - logistic regression self-made | 0.67760 | 0.00000 | 0.67057 | 0.00000 | 0.66705 | 0.00000 | |
45 | Kamil Guttmann | 2022-04-24 19:20 | 2.0.2 | s444380 self-made | 0.67843 | 0.00000 | N/A | N/A | 0.66771 | 0.00000 | |
44 | Mikołaj Pokrywka | 2022-04-24 08:19 | 2.0.2 | 444463 self-made | N/A | N/A | N/A | N/A | 0.66439 | 0.00000 | |
43 | s444417 | 2022-04-23 08:45 | 2.0.2 | imbalance words self-made | 0.51183 | 0.00000 | 0.51960 | 0.00000 | 0.51237 | 0.00000 | |
42 | s444501 | 2022-04-22 17:31 | 2.0.2 | 444501 self-made | 0.51763 | 0.00000 | 0.51441 | 0.00000 | 0.51405 | 0.00000 | |
41 | s444476 | 2022-04-22 15:59 | 2.0.2 | s444476 self-made | 0.52288 | 0.00000 | 0.52066 | 0.00000 | 0.51889 | 0.00000 | |
40 | s409771 | 2022-04-22 15:12 | 2.0.2 | multinomial naive bayes self-made | 0.66890 | 0.00000 | 0.65028 | 0.00000 | 0.65503 | 0.00000 | |
39 | ked | 2022-04-22 15:09 | 2.0.2 | s449288 - dumb wordlist lookup self-made | 0.51690 | 0.00000 | 0.51665 | 0.00000 | 0.51464 | 0.00000 | |
38 | s444476 | 2022-04-22 09:33 | 2.0.2 | s444476 self-made | 0.51025 | 0.00000 | 0.50907 | 0.00000 | 0.50849 | 0.00000 | |
37 | s478846 | 2022-04-21 08:55 | 2.0.2 | First solution s478846 self-made | 0.52095 | 0.00000 | 0.51573 | 0.00000 | 0.51599 | 0.00000 | |
36 | s478855 | 2022-04-20 19:16 | 2.0.2 | 478855 - improvement self-made | 0.51478 | 0.00000 | 0.51404 | 0.00000 | 0.51230 | 0.00000 | |
35 | s478855 | 2022-04-20 19:05 | 2.0.2 | s478855 self-made | 0.51154 | 0.00000 | 0.51147 | 0.00000 | 0.50940 | 0.00000 | |
34 | s444356 | 2022-04-20 11:04 | 2.0.2 | s444356 self-made | 0.51347 | 0.00000 | 0.52350 | 0.00000 | 0.51307 | 0.00000 | |
10 | kubapok | 2022-04-20 07:22 | 2.0.2 | always return 0.45 | 0.50000 | 0.49749 | 0.50000 | 0.49749 | 0.50000 | 0.49749 | |
33 | Jakub Adamski | 2022-04-20 06:45 | 2.0.2 | s444341 zadanie self-made | 0.60198 | 0.00000 | 0.58889 | 0.00000 | 0.58960 | 0.00000 | |
9 | kubapok | 2022-04-19 12:58 | 2.0.2 | always return 0.45 | 0.50000 | 0.49749 | 0.50000 | 0.49749 | 0.50000 | 0.49749 | |
612 | s444354 | 2022-04-13 15:35 | 2.0.2 | s444354 | N/A | N/A | N/A | N/A | N/A | N/A | |
611 | kubapok | 2022-04-13 10:56 | 2.0.2 | always return 0.8 prob | N/A | N/A | N/A | N/A | N/A | N/A | |
610 | kubapok | 2022-04-13 09:17 | 2.0.2 | always 0.8 prob self-made | N/A | N/A | N/A | N/A | N/A | N/A | |
32 | [anonymized] | 2021-03-13 14:48 | 2.0.2 | my brilliant solution logistic-regression pytorch-nn | 0.54393 | 0.00000 | 0.53859 | 0.00000 | 0.53925 | 0.00000 | |
609 | [anonymized] | 2021-02-23 19:46 | 2.0.2 | solution logistic-regression pytorch-nn | 0.58942 | 0.00000 | N/A | N/A | N/A | N/A | |
31 | [anonymized] | 2021-02-03 17:42 | 2.0.2 | TAU15 logistic-regression pytorch-nn | 0.50283 | 0.00000 | 0.50808 | 0.00000 | 0.50074 | 0.00000 | |
608 | [anonymized] | 2021-02-03 15:37 | 2.0.2 | TAU15 logistic-regression pytorch-nn | N/A | N/A | N/A | N/A | N/A | N/A | |
30 | [anonymized] | 2021-02-01 20:38 | 2.0.2 | logistic-regression logistic-regression pytorch-nn | 0.65071 | 0.00000 | N/A | N/A | 0.63924 | 0.00000 | |
29 | [anonymized] | 2021-01-29 19:01 | 2.0.2 | logistic-regression logistic-regression pytorch-nn | 0.65071 | 0.00000 | N/A | N/A | 0.61928 | 0.00000 | |
28 | [anonymized] | 2021-01-27 02:20 | 2.0.2 | 1try logistic-regression pytorch-nn | N/A | N/A | N/A | N/A | 0.49914 | 0.00000 | |
27 | [anonymized] | 2021-01-26 23:25 | 2.0.2 | try again logistic-regression pytorch-nn | 0.54330 | 0.00000 | 0.53850 | 0.00000 | 0.53999 | 0.00000 | |
607 | [anonymized] | 2020-12-16 08:51 | 2.0.2 | initial logistic-regression pytorch-nn | 0.49930 | 0.00000 | N/A | N/A | N/A | N/A | |
26 | [anonymized] | 2020-12-16 08:42 | 2.0.2 | 'final' logistic-regression pytorch-nn | 0.50843 | 0.00000 | 0.51324 | 0.00000 | 0.50878 | 0.00000 | |
606 | [anonymized] | 2020-12-16 08:38 | 2.0.2 | initial logistic-regression pytorch-nn | 0.49930 | 0.00000 | N/A | N/A | N/A | N/A | |
25 | [anonymized] | 2020-12-15 22:09 | 2.0.2 | added code with output for dev-0, dev-1, test-A logistic-regression pytorch-nn | 0.49811 | 0.00000 | 0.49709 | 0.00000 | 0.49317 | 0.00000 | |
24 | [anonymized] | 2020-12-15 21:55 | 2.0.2 | added source file logistic-regression pytorch-nn | 0.50036 | 0.00000 | 0.50625 | 0.00000 | 0.49728 | 0.00000 | |
23 | [anonymized] | 2020-12-15 21:05 | 2.0.2 | added rest of files | 0.50036 | 0.00000 | 0.50625 | 0.00000 | 0.49728 | 0.00000 | |
605 | [anonymized] | 2020-12-15 20:09 | 2.0.2 | test push | 0.50038 | 0.00000 | N/A | N/A | N/A | N/A | |
604 | [anonymized] | 2020-12-14 22:03 | 2.0.2 | Basic torch nn logistic-regression pytorch-nn | N/A | N/A | N/A | N/A | 0.54713 | N/A | |
603 | [anonymized] | 2020-12-14 22:01 | 2.0.2 | Basic torch nn logistic-regression pytorch-nn | N/A | N/A | N/A | N/A | N/A | N/A | |
8 | [anonymized] | 2020-12-13 22:48 | 2.0.2 | model-size=100k voc-size=100k logistic-regression pytorch-nn | N/A | N/A | N/A | N/A | 0.60299 | 0.51313 | |
22 | [anonymized] | 2020-12-13 22:29 | 2.0.2 | model-size=100k voc-size=100k logistic-regression pytorch-nn | N/A | N/A | N/A | N/A | 0.60299 | 0.00000 | |
21 | [anonymized] | 2020-12-10 01:01 | 2.0.2 | solution logistic-regression pytorch-nn | 0.58941 | 0.00000 | 0.58775 | 0.00000 | 0.58344 | 0.00000 | |
20 | [anonymized] | 2020-12-09 07:50 | 2.0.2 | Simple Solution for Logistic Regresion logistic-regression pytorch-nn | 0.58942 | 0.00000 | 0.58776 | 0.00000 | 0.58355 | 0.00000 | |
19 | [anonymized] | 2020-12-09 07:18 | 2.0.2 | Simple Solution for Logistic Regresion logistic-regression pytorch-nn | 0.58942 | 0.00000 | 0.58776 | 0.00000 | 0.58355 | 0.00000 | |
12 | [anonymized] | 2020-12-08 15:08 | 2.0.2 | pytorch logistic regression logistic-regression pytorch-nn | 0.54587 | 0.44115 | 0.54082 | 0.43867 | 0.54176 | 0.43930 | |
18 | [anonymized] | 2020-12-07 18:12 | 2.0.2 | add code and fixed test-A logistic-regression pytorch-nn | 0.58539 | 0.00000 | 0.58503 | 0.00000 | 0.57926 | 0.00000 | |
602 | [anonymized] | 2020-12-07 18:02 | 2.0.2 | add out files logistic-regression pytorch-nn | 0.58539 | 0.00000 | 0.58503 | 0.00000 | N/A | N/A | |
13 | [anonymized] | 2020-12-05 16:13 | 2.0.2 | pytorch logistic regression logistic-regression pytorch-nn | 0.52900 | 0.18920 | 0.52097 | 0.18847 | 0.52250 | 0.19279 | |
14 | [anonymized] | 2020-12-05 13:45 | 2.0.2 | second try logistic-regression pytorch-nn | 0.51805 | 0.13101 | 0.51130 | 0.13501 | 0.51124 | 0.13660 | |
17 | [anonymized] | 2020-12-05 12:47 | 2.0.2 | first try logistic-regression pytorch-nn | 0.49788 | 0.00000 | 0.50072 | 0.00000 | 0.49691 | 0.00000 | |
601 | [anonymized] | 2020-10-28 20:03 | 2.0.2 | Laboratorium 1 | N/A | N/A | N/A | N/A | N/A | N/A | |
16 | kubapok | 2020-07-16 15:01 | 2.0.2 | human ensemble | 0.50028 | 0.00000 | 0.50013 | 0.00000 | 0.50118 | 0.00000 | |
1 | kaczla | 2020-07-02 08:55 | 2.0.2 | Polish RoBERTa (base), epoch 5, seq_len 512, active dropout fairseq roberta-pl | 0.75644 | 0.62799 | 0.74832 | 0.62476 | 0.74332 | 0.62110 | |
15 | kubapok | 2020-06-28 14:33 | 2.0.2 | unsupervised_men_women_mean_closer | 0.50007 | 0.00000 | 0.50013 | 0.00000 | 0.49990 | 0.00000 | |
600 | kubapok | 2020-06-25 13:10 | 2.0.0 | unsupervised fix test-a | 0.56230 | 0.00000 | 0.55795 | 0.00000 | 0.55828 | 0.00000 | |
599 | kubapok | 2020-06-25 09:54 | 2.0.0 | unsupervised | 0.56230 | 0.00000 | 0.55795 | 0.00000 | N/A | N/A | |
598 | kubapok | 2020-06-19 10:51 | 2.0.0 | pl roberta large active dropout 1 run | 0.75675 | 0.62653 | 0.74711 | 0.61832 | 0.74160 | 0.61615 | |
597 | kubapok | 2020-06-19 10:25 | 2.0.0 | soften probs roberta large finetunned | 0.75989 | 0.62311 | 0.74943 | 0.61641 | 0.74388 | 0.61240 | |
2 | kubapok | 2020-06-19 09:29 | 2.0.2 | pl roberta large active dropout avg 12 runs | 0.75965 | 0.63032 | 0.74988 | 0.62247 | 0.74406 | 0.61949 | |
596 | kaczla | 2020-06-15 09:21 | 2.0.0 | XLM-RoBERTa 1 epoch model=xlmr_base-seq_len=512 roberta-xlm | 0.73154 | 0.60428 | 0.72444 | 0.60192 | 0.72356 | 0.60015 | |
595 | kaczla | 2020-06-15 09:21 | 2.0.0 | XLM-RoBERTa 1 epoch model=xlmr_large-seq_len=512 roberta-xlm | 0.70719 | 0.57917 | 0.68732 | 0.57095 | 0.69047 | 0.57141 | |
5 | kaczla | 2020-06-15 06:40 | 2.0.2 | XLM-R model=xlmr_large-seq_len=512 roberta-xlm | 0.70719 | 0.57917 | 0.68732 | 0.57095 | 0.69047 | 0.57141 | |
3 | kaczla | 2020-06-15 06:40 | 2.0.2 | XLM-R model=xlmr_base-seq_len=512 roberta-xlm | 0.71171 | 0.58902 | 0.70184 | 0.58230 | 0.70118 | 0.58280 | |
594 | kubapok | 2020-06-14 09:06 | 2.0.0 | bilstm emb size 300 | 0.70417 | 0.57807 | 0.70061 | 0.57678 | 0.69496 | 0.57405 | |
593 | kaczla | 2020-06-13 20:48 | 2.0.0 | from scratch RoBERTa classifier (only), seq_len=256 epoch=epoch20 fairseq roberta | 0.68991 | 0.56160 | 0.68586 | 0.56150 | 0.67951 | 0.55784 | |
592 | kaczla | 2020-06-13 20:48 | 2.0.0 | from scratch RoBERTa classifier (only), seq_len=256 epoch=epoch10 fairseq roberta | 0.69010 | 0.55805 | 0.68526 | 0.55695 | 0.67744 | 0.55314 | |
591 | kaczla | 2020-06-13 20:45 | 2.0.0 | from scratch RoBERTa MLM + classifier, seq_len=256 fairseq roberta | 0.70276 | 0.57555 | 0.69255 | 0.57229 | 0.69153 | 0.57068 | |
590 | kaczla | 2020-06-13 18:28 | 2.0.0 | Polish RoBERTa (base), epoch 5, seq_len 512 fairseq roberta-pl | 0.75686 | 0.61212 | 0.74828 | 0.61205 | 0.74185 | 0.60913 | |
589 | kubapok | 2020-06-12 21:37 | 2.0.0 | logistic regression on polish roberta | 0.67341 | 0.54798 | 0.65471 | 0.53876 | 0.65956 | 0.54113 | |
588 | kubapok | 2020-06-12 20:44 | 2.0.0 | logistic regression on xlm roberta | 0.66913 | 0.54803 | 0.65301 | 0.53899 | 0.65545 | 0.54067 | |
587 | kubapok | 2020-06-12 13:43 | 2.0.0 | polish roberta finetunned large 3 epochs fairseq | 0.75989 | 0.61474 | 0.74943 | 0.60794 | 0.74388 | 0.60503 | |
4 | kubapok | 2020-06-11 21:49 | 2.0.2 | keras lstm on spe 50k vocab, 5epochs | 0.70685 | 0.57509 | 0.70046 | 0.57281 | 0.69786 | 0.57177 | |
586 | kubapok | 2020-06-10 19:41 | 2.0.0 | xgb on tfidf | 0.66207 | 0.54693 | 0.65765 | 0.54675 | 0.65112 | 0.54269 | |
585 | kubapok | 2020-06-10 19:34 | 2.0.0 | fix fasttext standard params | 0.67156 | 0.53548 | 0.66367 | 0.53158 | 0.65865 | 0.52771 | |
584 | kubapok | 2020-06-10 19:22 | 2.0.0 | fasttext hypertune | 0.68800 | 0.55105 | 0.67664 | 0.54717 | 0.67448 | 0.54541 | |
583 | kubapok | 2020-06-09 14:06 | 2.0.0 | linearSVM on tfidf | 0.67327 | 0.00000 | 0.66925 | 0.00000 | 0.66477 | 0.00000 | |
582 | kubapok | 2020-06-09 13:19 | 2.0.0 | logistic regression on polish roberta last layer | 0.67341 | 0.54798 | 0.65471 | 0.53876 | 0.65956 | 0.54113 | |
581 | kubapok | 2020-06-09 10:56 | 2.0.0 | fasttext 50 epochs | 0.79221 | 0.00000 | 0.49968 | 0.00000 | 0.49144 | 0.00000 | |
580 | kubapok | 2020-06-09 09:56 | 2.0.0 | fasttext standard | 0.66971 | 0.00000 | 0.61290 | 0.52516 | 0.60438 | 0.52068 | |
579 | kubapok | 2020-06-07 20:55 | 2.0.0 | xgbclassifier standard params on tfidf | 0.61196 | 0.52337 | 0.61290 | 0.52516 | 0.60438 | 0.52068 | |
578 | [anonymized] | 2020-06-07 12:45 | 2.0.0 | XGBoost ready-made ready-made xgboost | 0.61238 | 0.52290 | 0.60935 | 0.52390 | 0.60039 | 0.52000 | |
6 | kubapok | 2020-06-06 09:14 | 2.0.2 | tfidf logistic regression | 0.68278 | 0.55867 | 0.67661 | 0.55626 | 0.67175 | 0.55278 | |
577 | [anonymized] | 2020-06-05 10:17 | 2.0.0 | svm ready-made svm | 0.60424 | 0.00000 | 0.59963 | 0.00000 | 0.59623 | 0.00000 | |
576 | [anonymized] | 2020-06-03 17:39 | 2.0.0 | 2nd logistic-regression word2vec | N/A | N/A | N/A | N/A | 0.49299 | 0.49679 | |
575 | wirus wirus | 2020-06-03 09:34 | 2.0.0 | test submission 4 baseline | 0.50000 | 0.48990 | 0.50000 | 0.48990 | 0.50000 | 0.48990 | |
574 | wirus wirus | 2020-06-03 08:33 | 2.0.0 | test submission 3 baseline | 0.50000 | 0.48990 | 0.50000 | 0.48990 | 0.50000 | 0.48990 | |
573 | [anonymized] | 2020-06-02 14:12 | 2.0.0 | 1st logistic-regression word2vec | N/A | N/A | N/A | N/A | 0.00000 | N/A | |
572 | wirus wirus | 2020-05-30 21:30 | 2.0.0 | null model null-model | 0.50000 | 0.50000 | 0.50000 | 0.50000 | 0.50000 | 0.48990 | |
571 | [anonymized] | 2020-05-26 11:59 | 2.0.0 | improved results for probabilities probabilities | 0.67284 | N/A | 0.66882 | N/A | 0.66309 | N/A | |
570 | [anonymized] | 2020-05-26 10:00 | 2.0.0 | probabilities solution probabilities | 0.67284 | N/A | 0.66882 | N/A | 0.66309 | N/A | |
569 | kubapok | 2020-05-25 09:49 | 2.0.0 | polish large roberta finetune 1 epoch (add likelihood) | 0.75119 | 0.59610 | 0.74263 | 0.59295 | 0.73598 | 0.58475 | |
7 | [anonymized] | 2020-05-24 14:52 | 2.0.2 | self-made NB with probs ISI-2019-063 probabilities | 0.66983 | 0.52984 | 0.65097 | 0.52370 | 0.65612 | 0.52537 | |
568 | [anonymized] | 2020-05-24 14:17 | 2.0.0 | v5 probabilities | 0.80379 | 0.59574 | 0.91633 | 0.65340 | 0.64427 | 0.52134 | |
567 | [anonymized] | 2020-05-24 14:11 | 2.0.0 | v6 | 0.80379 | 0.57457 | 0.91633 | 0.61566 | 0.64427 | 0.52052 | |
566 | [anonymized] | 2020-05-24 14:02 | 2.0.0 | v5 | 0.80379 | 0.59574 | 0.91633 | 0.65340 | 0.64427 | 0.52134 | |
565 | [anonymized] | 2020-05-24 13:56 | 2.0.0 | v4 | 0.80379 | 0.60903 | 0.91633 | 0.68521 | 0.64427 | 0.51176 | |
564 | [anonymized] | 2020-05-24 13:39 | 2.0.0 | v3 | 0.80379 | 0.62231 | 0.91633 | 0.71879 | 0.64427 | 0.50223 | |
563 | kubapok | 2020-05-24 07:57 | 2.0.0 | Merge branch 'master' of ssh://gonito.net/kubapok/petite-difference-challenge2 | 0.75023 | 0.00000 | N/A | N/A | 0.73240 | 0.00000 | |
562 | wirus wirus | 2020-05-23 21:29 | 2.0.0 | null model null-model | 0.50000 | 0.50000 | 0.50000 | 0.50000 | 0.50000 | 0.50000 | |
561 | [anonymized] | 2020-05-23 09:50 | 2.0.0 | v2 | 0.80379 | 0.62830 | 0.91633 | 0.77274 | 0.64427 | 0.45354 | |
560 | [anonymized] | 2020-05-22 23:04 | 2.0.0 | v1 | 0.76235 | 0.00000 | 0.85390 | 0.00000 | 0.61107 | 0.00000 | |
559 | [anonymized] | 2020-05-13 13:24 | 1.0.1 | small-roberta classifier eval_batch_size=200 evaluate_during_training=true evaluate_during_training_steps=5000 num_train_epochs=5 save_steps=5000 train max_lines=1_500_000 train_batch_size=100 use_cached_eval_features=true neural-network transformer roberta | 0.71307 | N/A | 0.70805 | N/A | 0.70384 | N/A | |
558 | kaczla | 2020-05-02 12:41 | 1.0.1 | Classifier with RoBERTa corpus=base_with_cc model=small seq_len=256 sliding=False valid=dev-0 neural-network transformer roberta | 0.72328 | N/A | 0.71980 | N/A | 0.71603 | N/A | |
557 | kaczla | 2020-05-02 12:41 | 1.0.1 | Classifier with RoBERTa corpus=base_with_cc model=small seq_len=128 sliding=False valid=dev-0 neural-network transformer roberta | 0.71685 | N/A | 0.71588 | N/A | 0.71112 | N/A | |
556 | kaczla | 2020-05-02 12:41 | 1.0.1 | Classifier with RoBERTa corpus=base model=small seq_len=512 sliding=False valid=dev-0 neural-network transformer roberta | 0.72839 | N/A | 0.72392 | N/A | 0.71900 | N/A | |
555 | kaczla | 2020-05-02 12:41 | 1.0.1 | Classifier with RoBERTa corpus=base model=small seq_len=256 sliding=False valid=dev-0 neural-network transformer roberta | 0.72722 | N/A | 0.72474 | N/A | 0.71711 | N/A | |
554 | kaczla | 2020-05-02 12:41 | 1.0.1 | Classifier with RoBERTa corpus=base model=small seq_len=128 sliding=True valid=dev-0 neural-network transformer roberta | 0.71886 | N/A | 0.71783 | N/A | 0.71010 | N/A | |
553 | kaczla | 2020-05-02 12:41 | 1.0.1 | Classifier with RoBERTa corpus=base model=small seq_len=128 sliding=False valid=dev-1 neural-network transformer roberta | 0.71623 | N/A | 0.71499 | N/A | 0.70933 | N/A | |
552 | kaczla | 2020-05-02 12:41 | 1.0.1 | Classifier with RoBERTa corpus=base model=small seq_len=128 sliding=False valid=dev-0 neural-network transformer roberta | 0.71972 | N/A | 0.71869 | N/A | 0.71250 | N/A | |
551 | kaczla | 2020-05-02 12:41 | 1.0.1 | Classifier with RoBERTa corpus=base model=small seq_len=128 sliding=False valid=dev-0-dev-1 neural-network transformer roberta | 0.72099 | N/A | 0.71799 | N/A | 0.71292 | N/A | |
550 | kaczla | 2020-05-02 12:41 | 1.0.1 | Classifier with RoBERTa corpus=base model=normal seq_len=512 sliding=False valid=dev-0 neural-network transformer roberta | 0.71632 | N/A | 0.71134 | N/A | 0.70462 | N/A | |
549 | kaczla | 2020-05-02 12:41 | 1.0.1 | Classifier with RoBERTa corpus=base model=normal seq_len=256 sliding=False valid=dev-0 neural-network transformer roberta | 0.73558 | N/A | 0.73049 | N/A | 0.72432 | N/A | |
548 | kaczla | 2020-05-02 12:41 | 1.0.1 | Classifier with RoBERTa corpus=base model=normal seq_len=128 sliding=False valid=dev-0 neural-network transformer roberta | 0.72824 | N/A | 0.72552 | N/A | 0.71995 | N/A | |
547 | kaczla | 2020-05-02 12:41 | 1.0.1 | Classifier with RoBERTa corpus=base model=big seq_len=512 sliding=False valid=dev-0 neural-network transformer roberta | 0.74090 | N/A | 0.73562 | N/A | 0.72914 | N/A | |
546 | kaczla | 2020-05-02 12:41 | 1.0.1 | Classifier with RoBERTa corpus=base model=big seq_len=256 sliding=False valid=dev-0 neural-network transformer roberta | 0.74382 | N/A | 0.74011 | N/A | 0.73017 | N/A | |
545 | kaczla | 2020-05-02 12:41 | 1.0.1 | Classifier with RoBERTa corpus=base model=big seq_len=128 sliding=False valid=dev-0 neural-network transformer roberta | 0.73382 | N/A | 0.72900 | N/A | 0.72484 | N/A | |
544 | Artur Nowakowski | 2020-04-20 17:14 | 1.0.0 | fine-tuned RoBERTa classifier (full-train) neural-network transformer roberta | 0.72107 | N/A | 0.71903 | N/A | 0.71279 | N/A | |
543 | Artur Nowakowski | 2020-04-18 08:12 | 1.0.0 | fine-tuned RoBERTa classifier (full-train) neural-network transformer roberta | 0.72107 | N/A | 0.71903 | N/A | 0.71279 | N/A | |
542 | kaczla | 2020-04-17 07:00 | 1.0.0 | fine-tune RoBERTa classifier (train 1M lines in classification) - RoBERTA pretrained for 7 days (5 epochs) on the current corpora model=base neural-network transformer roberta | 0.70466 | N/A | 0.69787 | N/A | 0.69680 | N/A | |
541 | [anonymized] | 2019-02-21 22:58 | 1.0.0 | my brilliant solution | 0.65445 | N/A | N/A | N/A | 0.64519 | N/A | |
540 | [anonymized] | 2019-02-21 15:18 | 1.0.0 | Michal Mioduszewski - solution | 0.64952 | N/A | N/A | N/A | 0.63986 | N/A | |
539 | [anonymized] | 2019-01-27 16:29 | 1.0.0 | dampie5 solution v3.5 | 0.66219 | N/A | 0.65437 | N/A | 0.65190 | N/A | |
538 | [anonymized] | 2019-01-27 16:25 | 1.0.0 | dampie5 solution v3.4 | 0.65339 | N/A | 0.63752 | N/A | 0.62094 | N/A | |
537 | [anonymized] | 2019-01-27 16:18 | 1.0.0 | dampie5 solution v3.3 | 0.65339 | N/A | 0.63752 | N/A | 0.64933 | N/A | |
536 | [anonymized] | 2019-01-27 15:43 | 1.0.0 | dampie5 solution v3.2 | 0.65339 | N/A | 0.63752 | N/A | 0.64495 | N/A | |
535 | [anonymized] | 2019-01-27 15:36 | 1.0.0 | dampie5 solution v3.1 | 0.65339 | N/A | 0.63752 | N/A | 0.64203 | N/A | |
534 | [anonymized] | 2019-01-27 15:30 | 1.0.0 | dampie5 solution v3 | 0.65339 | N/A | 0.63752 | N/A | 0.64137 | N/A | |
533 | [anonymized] | 2019-01-27 13:51 | 1.0.0 | asdds | N/A | N/A | N/A | N/A | 0.64519 | N/A | |
532 | [anonymized] | 2019-01-27 13:33 | 1.0.0 | Blah | N/A | N/A | N/A | N/A | 0.66275 | N/A | |
531 | [anonymized] | 2019-01-27 13:11 | 1.0.0 | dampie5 solution v2 | 0.65070 | N/A | 0.61818 | N/A | 0.63449 | N/A | |
530 | [anonymized] | 2019-01-27 13:01 | 1.0.0 | Wesja milion7 | 0.63707 | N/A | N/A | N/A | 0.65659 | N/A | |
529 | [anonymized] | 2019-01-27 12:50 | 1.0.0 | Wesja milion6 | 0.63707 | N/A | N/A | N/A | 0.65600 | N/A | |
528 | [anonymized] | 2019-01-27 12:46 | 1.0.0 | Wesja milion5 | N/A | N/A | N/A | N/A | 0.65600 | N/A | |
527 | [anonymized] | 2019-01-27 12:31 | 1.0.0 | Wesja milion4 | N/A | N/A | N/A | N/A | 0.65223 | N/A | |
526 | [anonymized] | 2019-01-27 12:10 | 1.0.0 | Wesja milion3 | N/A | N/A | N/A | N/A | 0.62460 | N/A | |
525 | [anonymized] | 2019-01-27 12:06 | 1.0.0 | Wesja milion2 | N/A | N/A | N/A | N/A | 0.58706 | N/A | |
524 | [anonymized] | 2019-01-27 11:23 | 1.0.0 | Wesja milion | N/A | N/A | N/A | N/A | 0.55697 | N/A | |
523 | [anonymized] | 2019-01-27 09:40 | 1.0.0 | Test4 | N/A | N/A | N/A | N/A | 0.66276 | N/A | |
522 | [anonymized] | 2019-01-27 08:11 | 1.0.0 | Test | N/A | N/A | N/A | N/A | 0.59881 | N/A | |
521 | [anonymized] | 2019-01-27 08:07 | 1.0.0 | proba milion nowa | N/A | N/A | N/A | N/A | 0.64888 | N/A | |
520 | [anonymized] | 2019-01-27 07:53 | 1.0.0 | probamilion | N/A | N/A | N/A | N/A | 0.61159 | N/A | |
519 | [anonymized] | 2019-01-27 07:23 | 1.0.0 | dodanie zaktualizowanego test.py | 0.66416 | N/A | N/A | N/A | 0.65883 | N/A | |
518 | [anonymized] | 2019-01-27 06:21 | 1.0.0 | test | 0.66416 | N/A | N/A | N/A | 0.65883 | N/A | |
517 | [anonymized] | 2019-01-26 20:26 | 1.0.0 | zadanie1 proba testA | N/A | N/A | N/A | N/A | 0.64888 | N/A | |
516 | [anonymized] | 2019-01-26 19:51 | 1.0.0 | dodanie wyniku dla test-A | 0.64904 | N/A | 0.64187 | N/A | 0.63726 | N/A | |
515 | [anonymized] | 2019-01-26 17:54 | 1.0.0 | rozwiązanie zadania "He Said She Said" | 0.64904 | N/A | 0.64187 | N/A | N/A | N/A | |
514 | [anonymized] | 2019-01-26 17:38 | 1.0.0 | solution1 | 0.65872 | N/A | 0.64920 | N/A | 0.64647 | N/A | |
513 | [anonymized] | 2019-01-26 17:35 | 1.0.0 | Init_3-zad-1 | 0.65446 | N/A | 0.65023 | N/A | 0.64519 | N/A | |
512 | [anonymized] | 2019-01-26 17:26 | 1.0.0 | mk | 0.65445 | N/A | N/A | N/A | 0.64519 | N/A | |
511 | [anonymized] | 2019-01-26 17:12 | 1.0.0 | kd solution | 0.65445 | N/A | N/A | N/A | 0.64519 | N/A | |
510 | [anonymized] | 2019-01-26 17:04 | 1.0.0 | s402267 - petite-difference-challenge2 | 0.65446 | N/A | 0.65023 | N/A | 0.64519 | N/A | |
509 | [anonymized] | 2019-01-26 17:04 | 1.0.0 | my solution | 0.63105 | N/A | 0.61818 | N/A | 0.63194 | N/A | |
508 | [anonymized] | 2019-01-26 16:48 | 1.0.0 | My brilliant solution | 0.57248 | N/A | 0.56905 | N/A | 0.56693 | N/A | |
507 | [anonymized] | 2019-01-26 16:47 | 1.0.0 | xx | N/A | N/A | N/A | N/A | 0.64647 | N/A | |
506 | [anonymized] | 2019-01-26 16:44 | 1.0.0 | My brilliant solution | N/A | N/A | 0.56905 | N/A | 0.56693 | N/A | |
505 | [anonymized] | 2019-01-26 16:44 | 1.0.0 | my brilliant solution2 | 0.65445 | N/A | N/A | N/A | 0.64519 | N/A | |
504 | [anonymized] | 2019-01-26 16:44 | 1.0.0 | my briliant solution | 0.65446 | N/A | 0.65023 | N/A | 0.64519 | N/A | |
503 | [anonymized] | 2019-01-26 16:43 | 1.0.0 | my first soution .py | 0.65445 | N/A | N/A | N/A | 0.64519 | N/A | |
502 | [anonymized] | 2019-01-26 16:43 | 1.0.0 | my brilliant solution | 0.65445 | N/A | N/A | N/A | 0.64519 | N/A | |
501 | [anonymized] | 2019-01-26 16:40 | 1.0.0 | my first soution | 0.65445 | N/A | N/A | N/A | 0.64519 | N/A | |
500 | [anonymized] | 2019-01-26 16:39 | 1.0.0 | DODAŁAM TUTAJ MOJE ZADANIE ZROBIONE | N/A | N/A | N/A | N/A | 0.56192 | N/A | |
499 | [anonymized] | 2019-01-26 16:31 | 1.0.0 | dssdd | N/A | N/A | N/A | N/A | N/A | N/A | |
498 | [anonymized] | 2019-01-26 16:23 | 1.0.0 | dodanie plikow wynikowych oraz skryptu | 0.66416 | N/A | N/A | N/A | 0.65051 | N/A | |
497 | [anonymized] | 2019-01-26 16:14 | 1.0.0 | My brilliant solution | N/A | N/A | 0.56905 | N/A | N/A | N/A | |
496 | [anonymized] | 2019-01-03 11:37 | 1.0.0 | brylantowe rozwiazanie2 | 0.52337 | N/A | N/A | N/A | 0.52448 | N/A | |
495 | [anonymized] | 2019-01-03 11:31 | 1.0.0 | brylantowe rozwiazanie | 0.51297 | N/A | N/A | N/A | 0.51836 | N/A | |
494 | [anonymized] | 2018-12-21 12:40 | 1.0.0 | fixed missing result naive-bayes python scikit-learn | 0.67183 | N/A | 0.65628 | N/A | 0.65905 | N/A | |
493 | [anonymized] | 2018-12-21 12:32 | 1.0.0 | added ML binary NB solution naive-bayes python scikit-learn | 0.67183 | N/A | N/A | N/A | 0.65905 | N/A | |
492 | [anonymized] | 2018-12-11 22:32 | 1.0.0 | initial version with training limit on 1m python scikit-learn better-than-no-model-baseline | 0.67254 | N/A | 0.66604 | N/A | 0.65983 | N/A | |
491 | [anonymized] | 2018-11-29 10:07 | 1.0.0 | LinearSVC dev-0 dev-1 test-A - read submission_info.md python scikit-learn | 0.67284 | N/A | 0.66882 | N/A | 0.66309 | N/A | |
490 | [anonymized] | 2018-11-27 13:13 | 1.0.0 | LinearSVC dev-0 dev-1 test-A python scikit-learn | 0.67284 | N/A | 0.66882 | N/A | 0.66309 | N/A | |
489 | [anonymized] | 2018-11-27 12:49 | 1.0.0 | work on files stripped from CR bytes (only locally - commiting only results) | 0.67284 | N/A | N/A | N/A | 0.66309 | N/A | |
488 | [anonymized] | 2018-11-27 10:47 | 1.0.0 | fix len | 0.54147 | N/A | N/A | N/A | 0.58143 | N/A | |
487 | [anonymized] | 2018-11-27 10:44 | 1.0.0 | LinearSVC test solution | 0.54147 | N/A | N/A | N/A | N/A | N/A | |
486 | [anonymized] | 2018-05-24 13:36 | 1.0.0 | my brilliant solution | N/A | N/A | N/A | N/A | N/A | N/A | |
485 | [anonymized] | 2018-05-22 14:09 | 1.0.0 | change dev/test | 0.53623 | N/A | N/A | N/A | 0.53678 | N/A | |
483 | [anonymized] | 2018-05-22 14:05 | 1.0.0 | bla bal | N/A | N/A | N/A | N/A | N/A | N/A | |
484 | [anonymized] | 2018-05-22 13:56 | 1.0.0 | my brilliant solution | N/A | N/A | N/A | N/A | N/A | N/A | |
482 | [anonymized] | 2018-05-22 12:59 | 1.0.0 | my brilliant solution naive bayes | N/A | N/A | N/A | N/A | N/A | N/A | |
473 | [anonymized] | 2018-05-20 22:12 | 1.0.0 | naive | 0.99640 | N/A | 0.99542 | N/A | 0.59369 | N/A | |
481 | [anonymized] | 2018-05-20 21:58 | 1.0.0 | 'bayes' | N/A | N/A | N/A | N/A | N/A | N/A | |
471 | [anonymized] | 2018-05-20 18:11 | 1.0.0 | naive bayes 2 | N/A | N/A | N/A | N/A | 0.59369 | N/A | |
470 | [anonymized] | 2018-05-20 17:04 | 1.0.0 | naive bayes | N/A | N/A | N/A | N/A | N/A | N/A | |
480 | [anonymized] | 2018-05-20 16:36 | 1.0.0 | Zadanie 7 | 0.66317 | N/A | 0.64740 | N/A | 0.65388 | N/A | |
472 | [anonymized] | 2018-05-20 15:57 | 1.0.0 | naive bayes | 0.67005 | N/A | N/A | N/A | 0.65872 | N/A | |
474 | [anonymized] | 2018-05-20 14:38 | 1.0.0 | my solution | 0.66317 | N/A | 0.64740 | N/A | 0.65388 | N/A | |
479 | [anonymized] | 2018-05-18 08:55 | 1.0.0 | NaiveBays | 0.53623 | N/A | N/A | N/A | 0.53678 | N/A | |
477 | [anonymized] | 2018-05-18 08:53 | 1.0.0 | naive_bayes | N/A | N/A | N/A | N/A | 0.53678 | N/A | |
476 | [anonymized] | 2018-05-18 08:32 | 1.0.0 | my solution -nb | N/A | N/A | N/A | N/A | 0.53678 | N/A | |
469 | [anonymized] | 2018-05-18 08:29 | 1.0.0 | NaiveBayse | N/A | N/A | N/A | N/A | 0.53678 | N/A | |
478 | [anonymized] | 2018-05-18 07:44 | 1.0.0 | naive bayes | N/A | N/A | N/A | N/A | 0.53678 | N/A | |
475 | [anonymized] | 2018-05-17 15:52 | 1.0.0 | polecenia | 0.61123 | N/A | N/A | N/A | 0.60185 | N/A | |
468 | [anonymized] | 2018-05-17 15:51 | 1.0.0 | my brilliant solution | 0.61123 | N/A | N/A | N/A | 0.60185 | N/A | |
467 | [anonymized] | 2018-05-17 15:46 | 1.0.0 | my brilliant solution | 0.61123 | N/A | N/A | N/A | 0.60185 | N/A | |
466 | [anonymized] | 2018-05-15 19:19 | 1.0.0 | Naive Bayes solution | 0.55897 | N/A | 0.55444 | N/A | 0.54939 | N/A | |
464 | [anonymized] | 2018-05-15 15:17 | 1.0.0 | UMZ homerwork - naive bayes | N/A | N/A | N/A | N/A | 0.59369 | N/A | |
463 | [anonymized] | 2018-05-15 14:47 | 1.0.0 | my brilliant solution | 0.61123 | N/A | N/A | N/A | 0.60185 | N/A | |
465 | [anonymized] | 2018-05-15 14:40 | 1.0.0 | Naive Bayes naive-bayes | N/A | N/A | N/A | N/A | 0.59369 | N/A | |
462 | [anonymized] | 2018-05-11 14:18 | 1.0.0 | my brilliant solution | 0.57816 | N/A | N/A | N/A | N/A | N/A | |
461 | [anonymized] | 2018-05-11 13:59 | 1.0.0 | my brilliant solution | N/A | N/A | N/A | N/A | N/A | N/A | |
460 | [anonymized] | 2018-05-11 13:51 | 1.0.0 | my brilliant solution | N/A | N/A | N/A | N/A | N/A | N/A | |
459 | [anonymized] | 2018-05-11 13:05 | 1.0.0 | my brilliant solution | N/A | N/A | N/A | N/A | N/A | N/A | |
458 | [anonymized] | 2018-02-13 21:16 | 1.0.0 | naive-bayes naive-bayes python | 0.49760 | N/A | 0.49896 | N/A | 0.60877 | N/A | |
457 | [anonymized] | 2018-02-13 20:14 | 1.0.0 | logistic-regression ready-made python ready-made logistic-regression | 0.49760 | N/A | 0.49896 | N/A | 0.60632 | N/A | |
456 | [anonymized] | 2018-02-12 23:45 | 1.0.0 | check | 0.49760 | N/A | 0.49896 | N/A | 0.49998 | N/A | |
455 | [anonymized] | 2018-02-12 23:28 | 1.0.0 | check | 0.49760 | N/A | 0.49896 | N/A | N/A | N/A | |
454 | [anonymized] | 2018-02-12 23:23 | 1.0.0 | check | 0.49760 | N/A | 0.49896 | N/A | N/A | N/A | |
453 | [anonymized] | 2018-02-12 22:59 | 1.0.0 | check | 0.49760 | N/A | 0.49896 | N/A | N/A | N/A | |
452 | [anonymized] | 2018-02-06 23:17 | 1.0.0 | logistic-regression ready-made python ready-made logistic-regression | 0.49760 | N/A | 0.49896 | N/A | 0.50030 | N/A | |
451 | [anonymized] | 2018-02-06 22:59 | 1.0.0 | logistic-regression ready-made | 0.49656 | N/A | 0.49866 | N/A | 0.50000 | N/A | |
450 | [anonymized] | 2018-02-06 22:33 | 1.0.0 | logistic-regression ready-made | 0.49656 | N/A | 0.49866 | N/A | 0.50001 | N/A | |
449 | [anonymized] | 2018-02-06 22:06 | 1.0.0 | logistic-regression ready-made | 0.49656 | N/A | 0.49866 | N/A | 0.49753 | N/A | |
448 | [anonymized] | 2018-02-06 21:09 | 1.0.0 | logistic-regression ready-made | N/A | N/A | N/A | N/A | N/A | N/A | |
447 | [anonymized] | 2018-02-06 21:05 | 1.0.0 | logistic-regression ready-made | N/A | N/A | N/A | N/A | N/A | N/A | |
446 | [anonymized] | 2018-02-06 21:01 | 1.0.0 | logistic-regression ready-made | N/A | N/A | N/A | N/A | N/A | N/A | |
445 | [anonymized] | 2018-02-06 20:46 | 1.0.0 | logistic-regression ready-made | N/A | N/A | N/A | N/A | N/A | N/A | |
444 | [anonymized] | 2018-02-06 20:41 | 1.0.0 | logistic-regression ready-made | N/A | N/A | N/A | N/A | N/A | N/A | |
440 | [anonymized] | 2018-01-30 19:34 | 1.0.0 | regr ready-made linear-regression | 0.66486 | N/A | N/A | N/A | 0.50109 | N/A | |
443 | [anonymized] | 2018-01-30 19:31 | 1.0.0 | naibe bayss naive-bayes | 0.66486 | N/A | N/A | N/A | 0.50109 | N/A | |
441 | [anonymized] | 2018-01-30 19:26 | 1.0.0 | naibe bays | 0.66486 | N/A | N/A | N/A | N/A | N/A | |
442 | [anonymized] | 2018-01-29 16:12 | 1.0.0 | regression ready make logistic-regression | 0.66486 | N/A | N/A | N/A | 0.49973 | N/A | |
439 | [anonymized] | 2018-01-19 08:25 | 1.0.0 | zadanie 008 z kodem programu v1.3 ready-made logistic-regression | 0.51239 | N/A | 0.50649 | N/A | 0.50882 | N/A | |
438 | [anonymized] | 2018-01-18 20:49 | 1.0.0 | zadanie 008 z kodem programu ready-made logistic-regression | N/A | N/A | N/A | N/A | N/A | N/A | |
436 | [anonymized] | 2018-01-14 14:00 | 1.0.0 | Add code Task 5. | 0.60010 | N/A | 0.59288 | N/A | 0.59835 | N/A | |
437 | [anonymized] | 2018-01-13 20:28 | 1.0.0 | regresja logistyczna ready-made logistic-regression | 0.63434 | N/A | 0.61847 | N/A | 0.62400 | N/A | |
435 | [anonymized] | 2018-01-09 12:59 | 1.0.0 | KenLM kenlm | 0.53752 | N/A | N/A | N/A | 0.61203 | N/A | |
434 | [anonymized] | 2018-01-08 17:50 | 1.0.0 | fix | N/A | N/A | N/A | N/A | N/A | N/A | |
433 | [anonymized] | 2018-01-08 17:29 | 1.0.0 | fix | N/A | N/A | N/A | N/A | N/A | N/A | |
432 | [anonymized] | 2018-01-07 18:48 | 1.0.0 | nb_ready naive-bayes | N/A | N/A | N/A | N/A | 0.50063 | N/A | |
425 | [anonymized] | 2018-01-07 17:48 | 1.0.0 | logreg_ready ready-made logistic-regression | N/A | N/A | N/A | N/A | 0.50025 | N/A | |
431 | [anonymized] | 2018-01-07 10:16 | 1.0.0 | logreg_ready ready-made logistic-regression | N/A | N/A | N/A | N/A | N/A | N/A | |
430 | [anonymized] | 2018-01-07 10:13 | 1.0.0 | logreg_ready ready-made logistic-regression | N/A | N/A | N/A | N/A | N/A | N/A | |
429 | [anonymized] | 2018-01-07 10:08 | 1.0.0 | solution1 ready-made logistic-regression | N/A | N/A | N/A | N/A | N/A | N/A | |
427 | [anonymized] | 2018-01-06 12:30 | 1.0.0 | naive bayes przy uzyciu wektorow czestosci slow naive-bayes | 0.65968 | N/A | 0.64131 | N/A | 0.64733 | N/A | |
426 | kaczla | 2018-01-04 19:25 | 1.0.0 | LSTM neural-network | 0.70125 | N/A | 0.69679 | N/A | 0.69214 | N/A | |
428 | kaczla | 2018-01-04 19:12 | 1.0.0 | KenLM kenlm | 0.67077 | N/A | 0.66102 | N/A | 0.65053 | N/A | |
424 | [anonymized] | 2017-12-30 21:40 | 1.0.0 | Done self-made naive-bayes self-made | 0.66918 | N/A | 0.64976 | N/A | 0.65531 | N/A | |
369 | [anonymized] | 2017-12-30 21:29 | 1.0.0 | Poprawilem zgodnosc linii naive-bayes | 0.66918 | N/A | 0.64976 | N/A | 0.65531 | N/A | |
422 | [anonymized] | 2017-12-29 08:48 | 1.0.0 | Gotowe naive-bayes | N/A | N/A | N/A | N/A | N/A | N/A | |
421 | [anonymized] | 2017-12-23 12:58 | 1.0.0 | naive bayes przegenerowano test-A/out.tsv dla wiekszego slownika czestosci slow naive-bayes | N/A | N/A | N/A | N/A | 0.59832 | N/A | |
415 | [anonymized] | 2017-12-23 12:54 | 1.0.0 | naive bayes przegenerowano test-A/out.tsv dla wiekszego slownika czestosci slow | N/A | N/A | N/A | N/A | 0.50727 | N/A | |
412 | [anonymized] | 2017-12-23 12:51 | 1.0.0 | naive bayes poprawiony out.tsv w test-A | N/A | N/A | N/A | N/A | 0.53115 | N/A | |
420 | [anonymized] | 2017-12-23 12:43 | 1.0.0 | naive bayes naive-bayes | N/A | N/A | N/A | N/A | N/A | N/A | |
419 | [anonymized] | 2017-12-20 20:48 | 1.0.0 | correct path | N/A | N/A | N/A | N/A | N/A | N/A | |
418 | [anonymized] | 2017-12-20 20:46 | 1.0.0 | logistic-regression ready-made | N/A | N/A | N/A | N/A | N/A | N/A | |
417 | [anonymized] | 2017-12-20 10:57 | 1.0.0 | my brilliant solution naive-bayes | 0.49738 | N/A | N/A | N/A | 0.49782 | N/A | |
416 | [anonymized] | 2017-12-17 16:50 | 1.0.0 | Naive bayes naive-bayes | N/A | N/A | N/A | N/A | 0.50726 | N/A | |
414 | [anonymized] | 2017-12-17 16:41 | 1.0.0 | Naive bayes | N/A | N/A | N/A | N/A | 0.50728 | N/A | |
411 | [anonymized] | 2017-12-17 15:44 | 1.0.0 | naive-bayes naive-bayes | 0.53840 | N/A | 0.50229 | N/A | 0.57380 | N/A | |
413 | [anonymized] | 2017-12-17 15:33 | 1.0.0 | naive bayes | N/A | N/A | N/A | N/A | N/A | N/A | |
409 | [anonymized] | 2017-12-16 13:18 | 1.0.0 | Logistic regression, ready-made ready-made logistic-regression | 0.52489 | N/A | 0.52753 | N/A | 0.52919 | N/A | |
410 | [anonymized] | 2017-12-16 12:08 | 1.0.0 | Naive Bayes naive-bayes | 0.51505 | N/A | 0.51576 | N/A | 0.52003 | N/A | |
368 | testowe3 | 2017-12-14 23:47 | 1.0.0 | test commit 2 naive-bayes self-made | N/A | N/A | N/A | N/A | N/A | N/A | |
367 | testowe3 | 2017-12-14 23:43 | 1.0.0 | test commit 2 naive-bayes | N/A | N/A | N/A | N/A | N/A | N/A | |
408 | [anonymized] | 2017-12-11 13:39 | 1.0.0 | Word2Vec + logistic regression (fix newlines) python ready-made logistic-regression | 0.51816 | N/A | N/A | N/A | 0.51148 | N/A | |
407 | [anonymized] | 2017-12-11 13:10 | 1.0.0 | Word2Vec + logistic regression python ready-made logistic-regression | 0.51816 | N/A | N/A | N/A | N/A | N/A | |
406 | [anonymized] | 2017-12-06 22:52 | 1.0.0 | TF-IDF - logistic regression | N/A | N/A | N/A | N/A | 0.50000 | N/A | |
405 | [anonymized] | 2017-12-06 22:46 | 1.0.0 | TF-IDF - logistic regression | N/A | N/A | N/A | N/A | N/A | N/A | |
423 | [anonymized] | 2017-12-06 09:56 | 1.0.0 | Word2Vec on 200k words ready-made logistic-regression | N/A | N/A | N/A | N/A | 0.59513 | N/A | |
404 | [anonymized] | 2017-12-05 23:43 | 1.0.0 | Logistic word2vec | N/A | N/A | N/A | N/A | 0.58870 | N/A | |
388 | [anonymized] | 2017-12-05 23:30 | 1.0.0 | naive bayes with word2Vec naive-bayes | N/A | N/A | N/A | N/A | 0.55990 | N/A | |
403 | [anonymized] | 2017-12-05 23:03 | 1.0.0 | old fashioned word2vec | N/A | N/A | N/A | N/A | 0.57788 | N/A | |
402 | [anonymized] | 2017-12-05 22:56 | 1.0.0 | It being wasted | N/A | N/A | N/A | N/A | 0.51272 | N/A | |
401 | [anonymized] | 2017-12-05 22:37 | 1.0.0 | Bigger word2vec model | N/A | N/A | N/A | N/A | 0.51033 | N/A | |
399 | [anonymized] | 2017-12-05 20:33 | 1.0.0 | Test with bigger train model | N/A | N/A | N/A | N/A | 0.51004 | N/A | |
397 | [anonymized] | 2017-12-05 20:11 | 1.0.0 | Attempt with small train and trained word2Vec models | N/A | N/A | N/A | N/A | 0.51042 | N/A | |
393 | [anonymized] | 2017-12-03 22:17 | 1.0.0 | Add working app.py file self-made logistic-regression | N/A | N/A | N/A | N/A | N/A | N/A | |
389 | [anonymized] | 2017-12-03 21:43 | 1.0.0 | 05 naive bayes v1 naive-bayes | N/A | N/A | N/A | N/A | 0.49915 | N/A | |
387 | kaczla | 2017-12-03 20:31 | 1.0.0 | Naive Bayes naive-bayes | 0.66092 | N/A | 0.64342 | N/A | 0.65071 | N/A | |
386 | [anonymized] | 2017-12-03 19:32 | 1.0.0 | Logistic regression, ready-made ready-made logistic-regression | 0.50216 | N/A | 0.50116 | N/A | 0.50305 | N/A | |
378 | [anonymized] | 2017-12-03 18:39 | 1.0.0 | naive bayes naive-bayes | N/A | N/A | N/A | N/A | N/A | N/A | |
395 | [anonymized] | 2017-12-03 18:34 | 1.0.0 | logistic regresion still M self-made logistic-regression | N/A | N/A | N/A | N/A | N/A | N/A | |
392 | [anonymized] | 2017-12-03 18:19 | 1.0.0 | logistic regresion self-made logistic-regression | N/A | N/A | N/A | N/A | N/A | N/A | |
385 | [anonymized] | 2017-12-03 16:29 | 1.0.0 | lm kenlm | 0.93282 | N/A | 0.75279 | N/A | 0.61190 | N/A | |
379 | [anonymized] | 2017-12-03 16:18 | 1.0.0 | Logistic regression self-made logistic-regression | 0.53592 | N/A | 0.51831 | N/A | 0.51619 | N/A | |
383 | [anonymized] | 2017-12-03 16:05 | 1.0.0 | test | 0.53592 | N/A | 0.51831 | N/A | 0.51619 | N/A | |
390 | [anonymized] | 2017-12-03 14:13 | 1.0.0 | Naive Bayes naive-bayes | N/A | N/A | N/A | N/A | N/A | N/A | |
391 | [anonymized] | 2017-12-03 13:48 | 1.0.0 | LogReg ready-made logistic-regression | N/A | N/A | N/A | N/A | N/A | N/A | |
400 | [anonymized] | 2017-12-03 13:41 | 1.0.0 | LogReg self-made logistic-regression | N/A | N/A | N/A | N/A | N/A | N/A | |
381 | [anonymized] | 2017-12-03 10:29 | 1.0.0 | Task 5. naive-bayes | 0.60010 | N/A | 0.59288 | N/A | 0.59835 | N/A | |
382 | [anonymized] | 2017-12-03 01:59 | 1.0.0 | Naive Bayes naive-bayes | 0.50217 | N/A | 0.50103 | N/A | 0.50391 | N/A | |
376 | [anonymized] | 2017-12-03 01:13 | 1.0.0 | Naive Bayes | 0.50217 | N/A | N/A | N/A | N/A | N/A | |
374 | [anonymized] | 2017-12-01 13:44 | 1.0.0 | Logistic regression, self-made self-made logistic-regression | 0.52586 | N/A | 0.52755 | N/A | 0.52832 | N/A | |
398 | [anonymized] | 2017-11-30 23:11 | 1.0.0 | word2vec | N/A | N/A | N/A | N/A | 0.59523 | N/A | |
394 | [anonymized] | 2017-11-30 20:23 | 1.0.0 | naive-bayes naive-bayes ready-made | 0.62018 | N/A | 0.60296 | N/A | 0.61088 | N/A | |
380 | [anonymized] | 2017-11-30 19:51 | 1.0.0 | Logistic regression, self-made self-made logistic-regression | 0.50000 | N/A | 0.50000 | N/A | 0.50000 | N/A | |
371 | [anonymized] | 2017-11-30 19:49 | 1.0.0 | LogR readymade ready-made logistic-regression | N/A | N/A | 0.49985 | N/A | 0.49999 | N/A | |
377 | [anonymized] | 2017-11-30 18:01 | 1.0.0 | Logistic regression, self-made self-made logistic-regression | 0.50000 | N/A | 0.50000 | N/A | N/A | N/A | |
375 | [anonymized] | 2017-11-30 12:37 | 1.0.0 | Naive bayes on text length naive-bayes ready-made | 0.53752 | N/A | N/A | N/A | 0.50117 | N/A | |
373 | [anonymized] | 2017-11-30 10:34 | 1.0.0 | G Naive-Bayes naive-bayes | N/A | N/A | N/A | N/A | N/A | N/A | |
372 | [anonymized] | 2017-11-29 14:48 | 1.0.0 | Attempt with word2vec | N/A | N/A | N/A | N/A | 0.57634 | N/A | |
384 | [anonymized] | 2017-11-26 23:38 | 1.0.0 | LR | N/A | N/A | N/A | N/A | N/A | N/A | |
370 | [anonymized] | 2017-11-26 23:17 | 1.0.0 | 04b logistic regression ready-made v3 ready-made logistic-regression | N/A | N/A | N/A | N/A | 0.50086 | N/A | |
396 | [anonymized] | 2017-11-26 23:02 | 1.0.0 | 04b logistic regression ready-made v2 ready-made logistic-regression | N/A | N/A | N/A | N/A | N/A | N/A | |
366 | [anonymized] | 2017-11-26 22:40 | 1.0.0 | LR ready-made logistic-regression | N/A | N/A | N/A | N/A | 0.49918 | N/A | |
364 | [anonymized] | 2017-11-26 22:35 | 1.0.0 | Task 4. logistic-regression | 0.60779 | N/A | 0.60323 | N/A | 0.60482 | N/A | |
363 | [anonymized] | 2017-11-26 22:34 | 1.0.0 | logistic regression from sklearn ready-made logistic-regression | N/A | N/A | N/A | N/A | 0.49915 | N/A | |
362 | [anonymized] | 2017-11-26 21:36 | 1.0.0 | LR test | N/A | N/A | N/A | N/A | N/A | N/A | |
361 | [anonymized] | 2017-11-26 21:20 | 1.0.0 | Logistic regression | N/A | N/A | N/A | N/A | N/A | N/A | |
360 | [anonymized] | 2017-11-26 21:09 | 1.0.0 | Logistic regression | N/A | N/A | N/A | N/A | N/A | N/A | |
359 | [anonymized] | 2017-11-26 20:44 | 1.0.0 | logistic regression test | N/A | N/A | N/A | N/A | N/A | N/A | |
358 | [anonymized] | 2017-11-26 20:02 | 1.0.0 | Logistic regression self-made python, correct outs file self-made logistic-regression | 0.53950 | N/A | 0.50202 | N/A | 0.57821 | N/A | |
354 | [anonymized] | 2017-11-26 19:46 | 1.0.0 | Logisitc regression, self made, python self-made logistic-regression | 0.66180 | N/A | 0.65658 | N/A | 0.65381 | N/A | |
356 | [anonymized] | 2017-11-26 19:29 | 1.0.0 | Logistic regression python | N/A | N/A | N/A | N/A | N/A | N/A | |
355 | [anonymized] | 2017-11-26 19:20 | 1.0.0 | my bad solution 2 | N/A | N/A | N/A | N/A | N/A | N/A | |
357 | [anonymized] | 2017-11-26 19:17 | 1.0.0 | my bad solution self-made logistic-regression | N/A | N/A | N/A | N/A | N/A | N/A | |
333 | [anonymized] | 2017-11-25 19:29 | 1.0.0 | test | 0.51683 | N/A | 0.51195 | N/A | 0.51120 | N/A | |
353 | [anonymized] | 2017-11-25 12:20 | 1.0.0 | Selfmade Logical Regression self-made logistic-regression | N/A | N/A | N/A | N/A | N/A | N/A | |
341 | [anonymized] | 2017-11-24 19:32 | 1.0.0 | logistic regression ready-made logistic-regression | N/A | N/A | N/A | N/A | 0.61262 | N/A | |
347 | [anonymized] | 2017-11-23 20:02 | 1.0.0 | Self made Logistic Regression self-made logistic-regression | N/A | N/A | N/A | N/A | N/A | N/A | |
351 | [anonymized] | 2017-11-23 18:20 | 1.0.0 | Linear regression on length | 0.53752 | N/A | N/A | N/A | 0.50162 | N/A | |
352 | [anonymized] | 2017-11-23 18:10 | 1.0.0 | Logical regression on length ready-made logistic-regression | 0.53752 | N/A | N/A | N/A | 0.50185 | N/A | |
350 | [anonymized] | 2017-11-23 17:53 | 1.0.0 | Logical regression on length | 0.53752 | N/A | N/A | N/A | 0.50087 | N/A | |
349 | [anonymized] | 2017-11-23 17:30 | 1.0.0 | Logical regression on length | 0.53752 | N/A | N/A | N/A | 0.50087 | N/A | |
348 | [anonymized] | 2017-11-23 17:29 | 1.0.0 | Logical regression on length | 0.53752 | N/A | N/A | N/A | N/A | N/A | |
346 | [anonymized] | 2017-11-23 17:28 | 1.0.0 | Logical regression on length | 0.53752 | N/A | N/A | N/A | N/A | N/A | |
345 | [anonymized] | 2017-11-23 15:29 | 1.0.0 | Logical regression files | 0.53752 | N/A | N/A | N/A | 0.50762 | N/A | |
344 | [anonymized] | 2017-11-20 17:45 | 1.0.0 | Self made ngrams (ruby) self-made n-grams | 0.53752 | N/A | N/A | N/A | 0.50762 | N/A | |
343 | [anonymized] | 2017-11-20 17:06 | 1.0.0 | Normalized by occurance | 0.53752 | N/A | N/A | N/A | 0.50762 | N/A | |
342 | [anonymized] | 2017-11-20 16:29 | 1.0.0 | Normalization optimalization | 0.53752 | N/A | N/A | N/A | 0.51110 | N/A | |
365 | kaczla | 2017-11-20 16:16 | 1.0.0 | Logistic regression self-made logistic-regression | 0.66180 | N/A | 0.65658 | N/A | 0.65381 | N/A | |
340 | [anonymized] | 2017-11-20 16:12 | 1.0.0 | Trained on entire train | 0.53752 | N/A | N/A | N/A | 0.51110 | N/A | |
339 | [anonymized] | 2017-11-20 15:56 | 1.0.0 | Remove trash | 0.53752 | N/A | N/A | N/A | 0.50793 | N/A | |
316 | [anonymized] | 2017-11-20 15:52 | 1.0.0 | Add build model time counter | 0.53752 | N/A | N/A | N/A | 0.50793 | N/A | |
338 | [anonymized] | 2017-11-20 15:48 | 1.0.0 | Code improvements | 0.53752 | N/A | N/A | N/A | 0.50793 | N/A | |
337 | [anonymized] | 2017-11-20 14:08 | 1.0.0 | Add helpers | 0.53752 | N/A | N/A | N/A | 0.50793 | N/A | |
336 | [anonymized] | 2017-11-19 23:28 | 1.0.0 | Scaled | 0.53752 | N/A | N/A | N/A | 0.50793 | N/A | |
335 | [anonymized] | 2017-11-19 23:22 | 1.0.0 | Self-made ngrams (ruby) self-made | 0.53752 | N/A | N/A | N/A | 0.55832 | N/A | |
334 | [anonymized] | 2017-11-19 22:13 | 1.0.0 | Self-made ngrams (ruby) self-made | 0.53752 | N/A | N/A | N/A | 0.52906 | N/A | |
332 | [anonymized] | 2017-11-19 21:42 | 1.0.0 | Scaled to 1000000 | 0.53752 | N/A | N/A | N/A | 0.50323 | N/A | |
331 | [anonymized] | 2017-11-19 21:36 | 1.0.0 | Self made ngrams (ruby) scaled 1 to 10 | 0.53752 | N/A | N/A | N/A | 0.50373 | N/A | |
330 | [anonymized] | 2017-11-19 21:22 | 1.0.0 | Add normalization (ruby ngrams) | 0.53752 | N/A | N/A | N/A | 0.51275 | N/A | |
329 | [anonymized] | 2017-11-19 20:53 | 1.0.0 | Self made ngrams (ruby) | 0.53752 | N/A | N/A | N/A | 0.50780 | N/A | |
328 | [anonymized] | 2017-11-19 20:47 | 1.0.0 | Self-made ngrams (ruby) self-made | 0.53752 | N/A | N/A | N/A | 0.51515 | N/A | |
327 | [anonymized] | 2017-11-19 20:40 | 1.0.0 | Self made n-grams (ruby) | 0.53752 | N/A | N/A | N/A | 0.51755 | N/A | |
326 | [anonymized] | 2017-11-19 20:26 | 1.0.0 | Commiting splitter | 0.53752 | N/A | N/A | N/A | 0.52828 | N/A | |
325 | [anonymized] | 2017-11-19 20:23 | 1.0.0 | Self-made ngrams (ruby) | 0.53752 | N/A | N/A | N/A | 0.52828 | N/A | |
324 | [anonymized] | 2017-11-19 20:22 | 1.0.0 | Self-made ngrams (ruby) | 0.53752 | N/A | N/A | N/A | N/A | N/A | |
323 | [anonymized] | 2017-11-19 16:31 | 1.0.0 | Self n-grams | 0.53752 | N/A | N/A | N/A | N/A | N/A | |
322 | [anonymized] | 2017-11-19 01:32 | 1.0.0 | Self made ngrams | 0.53752 | N/A | N/A | N/A | 0.51746 | N/A | |
321 | [anonymized] | 2017-11-19 01:28 | 1.0.0 | Self made ngrams | 0.53752 | N/A | N/A | N/A | N/A | N/A | |
318 | [anonymized] | 2017-11-19 01:27 | 1.0.0 | Self made ngrams | 0.53752 | N/A | N/A | N/A | N/A | N/A | |
320 | [anonymized] | 2017-11-19 01:27 | 1.0.0 | Self made ngrams | 0.53752 | N/A | N/A | N/A | N/A | N/A | |
319 | [anonymized] | 2017-11-19 01:25 | 1.0.0 | Self made ngrams (ruby) | 0.53752 | N/A | N/A | N/A | N/A | N/A | |
317 | [anonymized] | 2017-11-13 17:27 | 1.0.0 | Ruby | 0.53752 | N/A | N/A | N/A | 0.52835 | N/A | |
315 | [anonymized] | 2017-06-21 17:05 | 1.0.0 | keras, tragiczne parametry neural-network | 0.64476 | N/A | 0.64158 | N/A | 0.64147 | N/A | |
313 | [anonymized] | 2017-06-12 13:28 | 1.0.0 | prosty model jezyka, unix, vol8 self-made ready-made lm | 0.57682 | N/A | 0.56149 | N/A | 0.56657 | N/A | |
312 | [anonymized] | 2017-06-12 13:25 | 1.0.0 | prosty model jezyka, unix, vol6 - nowe ratio, test #7 | 0.57682 | N/A | 0.56149 | N/A | 0.56277 | N/A | |
311 | [anonymized] | 2017-06-12 13:19 | 1.0.0 | prosty model jezyka, unix, vol6 - nowe ratio | 0.57457 | N/A | 0.56149 | N/A | 0.56277 | N/A | |
310 | [anonymized] | 2017-06-12 13:16 | 1.0.0 | prosty model jezyka, unix, vol6 - nowe ratio, test #4 (zakres) | 0.57457 | N/A | 0.50106 | N/A | N/A | N/A | |
309 | [anonymized] | 2017-06-12 13:14 | 1.0.0 | prosty model jezyka, unix, vol6 - nowe ratio, test #3 | 0.57459 | N/A | 0.50106 | N/A | N/A | N/A | |
308 | [anonymized] | 2017-06-12 13:12 | 1.0.0 | prosty model jezyka, unix, vol6 - nowe ratio, test #2 | 0.53362 | N/A | 0.50106 | N/A | N/A | N/A | |
307 | [anonymized] | 2017-06-12 13:11 | 1.0.0 | prosty model jezyka, unix, vol6 - nowe ratio, test #1 | 0.53072 | N/A | 0.50106 | N/A | N/A | N/A | |
306 | [anonymized] | 2017-06-12 13:09 | 1.0.0 | prosty model jezyka, unix, vol6 - nowe ratio | 0.46553 | N/A | 0.50106 | N/A | N/A | N/A | |
305 | [anonymized] | 2017-06-12 12:56 | 1.0.0 | prosty model jezyka, vol5, unix | 0.50741 | N/A | 0.50106 | N/A | N/A | N/A | |
304 | [anonymized] | 2017-06-12 12:46 | 1.0.0 | prosty model jezyka, vol4, unix | 0.51963 | N/A | 0.50106 | N/A | N/A | N/A | |
314 | [anonymized] | 2017-06-12 12:46 | 1.0.0 | Bernoulli naive-bayes bernoulli python self-made | N/A | N/A | N/A | N/A | 0.64037 | N/A | |
303 | [anonymized] | 2017-06-12 12:44 | 1.0.0 | prosty model jezyka, vol3, unix | 0.51971 | N/A | 0.50106 | N/A | N/A | N/A | |
302 | [anonymized] | 2017-06-12 12:41 | 1.0.0 | prosty model jezyka, vol2, unix | 0.48608 | N/A | 0.50106 | N/A | N/A | N/A | |
301 | [anonymized] | 2017-06-12 12:38 | 1.0.0 | prosty model jezyka, vol1, unix | 0.48013 | N/A | 0.50106 | N/A | N/A | N/A | |
300 | [anonymized] | 2017-06-12 09:31 | 1.0.0 | Naive Bayes - Bernoulli naive-bayes bernoulli python self-made | N/A | N/A | N/A | N/A | 0.63972 | N/A | |
299 | [anonymized] | 2017-06-11 23:28 | 1.0.0 | prosty model jezyka v4 | 0.50977 | N/A | 0.50106 | N/A | N/A | N/A | |
297 | [anonymized] | 2017-06-11 23:20 | 1.0.0 | prosty model jezyka v3 | 0.50977 | N/A | 0.50147 | N/A | 0.50155 | N/A | |
296 | [anonymized] | 2017-06-11 23:18 | 1.0.0 | prosty model jezyka v2 | N/A | N/A | 0.50147 | N/A | 0.50155 | N/A | |
295 | [anonymized] | 2017-06-11 23:13 | 1.0.0 | prosty model jezyka v1 | N/A | N/A | 0.50147 | N/A | 0.50155 | N/A | |
298 | wirus wirus | 2017-06-11 19:34 | 1.0.0 | CNN, embeddings with more dimensions | 0.68324 | N/A | 0.67808 | N/A | 0.67507 | N/A | |
293 | wirus wirus | 2017-06-11 05:26 | 1.0.0 | simple convolutional network neural-network cnn | 0.68111 | N/A | 0.67355 | N/A | 0.67189 | N/A | |
294 | [anonymized] | 2017-06-04 13:45 | 1.0.0 | lm self-made ready-made lm | N/A | N/A | N/A | N/A | 0.62377 | N/A | |
291 | [anonymized] | 2017-06-04 13:29 | 1.0.0 | lm | N/A | N/A | N/A | N/A | N/A | N/A | |
290 | [anonymized] | 2017-06-04 11:14 | 1.0.0 | em | 0.66730 | N/A | 0.64996 | N/A | 0.65499 | N/A | |
292 | kaczla | 2017-05-29 04:25 | 1.0.0 | LSTM - remove one layer, simple lemmatizer neural-network | 0.67703 | N/A | 0.67424 | N/A | 0.67083 | N/A | |
285 | kaczla | 2017-05-27 17:08 | 1.0.0 | LSTM - remove one layer, simple lemmatizer neural-network | 0.64777 | N/A | 0.64211 | N/A | 0.64444 | N/A | |
282 | kaczla | 2017-05-25 19:55 | 1.0.0 | LSTM - decrease batch_size, 5 RNNs neural-network | 0.70343 | N/A | 0.69886 | N/A | 0.69348 | N/A | |
283 | kaczla | 2017-05-24 18:04 | 1.0.0 | LSTM - decrease batch_size, 3 RNNs neural-network | 0.70125 | N/A | 0.69679 | N/A | 0.69214 | N/A | |
289 | kaczla | 2017-05-23 05:32 | 1.0.0 | LSTM - remove one layer, 3 RNNs neural-network | 0.70082 | N/A | 0.69814 | N/A | 0.69063 | N/A | |
288 | kaczla | 2017-05-19 04:25 | 1.0.0 | LSTM - remove one layer, decrease batch_size, epoch = 2 neural-network | 0.69495 | N/A | 0.69329 | N/A | 0.68734 | N/A | |
287 | kaczla | 2017-05-18 17:54 | 1.0.0 | LSTM - remove one layer, decrease batch_size, epoch = 3 neural-network | 0.68841 | N/A | 0.68476 | N/A | 0.68000 | N/A | |
286 | kaczla | 2017-05-16 10:23 | 1.0.0 | LSTM - epoch = 3 neural-network | 0.68501 | N/A | 0.68359 | N/A | 0.67617 | N/A | |
268 | kaczla | 2017-05-15 04:27 | 1.0.0 | LSTM - decrease batch_size neural-network | 0.69484 | N/A | 0.69201 | N/A | 0.68766 | N/A | |
281 | kaczla | 2017-05-15 04:25 | 1.0.0 | LSTM - decrease batch_size | 0.69364 | N/A | 0.69189 | N/A | 0.68599 | N/A | |
271 | kaczla | 2017-05-15 04:21 | 1.0.0 | LSTM - remove one layer neural-network | 0.69364 | N/A | 0.69189 | N/A | 0.68599 | N/A | |
280 | [anonymized] | 2017-05-14 22:05 | 1.0.0 | Bpe smalltrain | 0.56843 | N/A | 0.64794 | N/A | N/A | N/A | |
279 | [anonymized] | 2017-05-14 22:02 | 1.0.0 | Keras smalltrain | 0.56843 | N/A | 0.64794 | N/A | 0.49932 | N/A | |
278 | kaczla | 2017-05-14 15:48 | 1.0.0 | LSTM - remove one layer neural-network | 0.69364 | N/A | 0.69189 | N/A | 0.68599 | N/A | |
276 | [anonymized] | 2017-05-11 20:01 | 1.0.0 | Trigram hard keywords that occured at least 13 times, when can't decide on hard keywords "F" is assigned, Answers based on hard keywords percentage: dev-0 6%, dev-1 7%, test-A 6% python self-made | 0.51779 | N/A | 0.51906 | N/A | 0.51526 | N/A | |
277 | [anonymized] | 2017-05-11 19:54 | 1.0.0 | Trigram hard keywords that occured at least 13 times, when can't decide on hard keywords naive bayes is used, Answers based on hard keywords percentage: dev-0 6%, dev-1 7%, test-A 6% python self-made | 0.67116 | N/A | 0.65394 | N/A | 0.65709 | N/A | |
274 | [anonymized] | 2017-05-11 16:56 | 1.0.0 | Bigram hard keywords that occured at least 17 times, when can't decide on hard keywords "F" is assigned, Based on hard keywords percentage: dev-0 12%, dev-1 13%, test-A 14% python self-made | 0.53133 | N/A | 0.53295 | N/A | 0.53057 | N/A | |
275 | [anonymized] | 2017-05-11 16:42 | 1.0.0 | Bigram hard keywords that occured at least 17 times, when can't decide on hard keywords naive bayes is used, Based on hard keywords percentage: dev-0 12%, dev-1 13%, test-A 14% python self-made | 0.67223 | N/A | 0.65568 | N/A | 0.65883 | N/A | |
272 | [anonymized] | 2017-05-11 14:40 | 1.0.0 | Bigram hard keywords that occured at least 5 times, when can't decide on hard keywords naive bayes is used, Based on hard keywords percantage: dev-0 59%, dev-1 57%, test-A 56% python self-made | 0.64618 | N/A | 0.63862 | N/A | 0.63857 | N/A | |
273 | [anonymized] | 2017-05-11 13:13 | 1.0.0 | Bigram hard keywords that occured at least 5 times, when can't decide on hard keywords assings "F", Based on hard keywords percantage: dev-0 59%, dev-1 57%, test-A 56% python self-made | 0.59141 | N/A | 0.58698 | N/A | 0.58315 | N/A | |
269 | [anonymized] | 2017-05-04 19:08 | 1.0.0 | 1st try | N/A | N/A | N/A | N/A | 0.59865 | N/A | |
267 | [anonymized] | 2017-04-28 17:45 | 1.0.0 | Hard keywords based solution ver 2. If can't decide based on hard keywords naive bayes is used. Percentage of answers based on keywords: dev-0 10%, dev-1 9%, test-A 8%. Only words with count 3 and bigger are considered in hard keyword based approach. python self-made | 0.66285 | N/A | 0.64877 | N/A | 0.65190 | N/A | |
270 | [anonymized] | 2017-04-28 17:26 | 1.0.0 | Hard keywords based solution ver 1. If can't decide based on hard keywords naive bayes is used. Percentage of answers based on keywords: dev-0 22%, dev-1 19%, test-A 20% python self-made | 0.65111 | N/A | 0.64067 | N/A | 0.64489 | N/A | |
284 | wirus wirus | 2017-04-25 19:49 | 1.0.0 | 5 RNNs combined | 0.70079 | N/A | 0.69568 | N/A | 0.69044 | N/A | |
265 | wirus wirus | 2017-04-24 05:36 | 1.0.0 | fasttext combined with KenLM | 0.71653 | N/A | 0.70503 | N/A | 0.69295 | N/A | |
266 | wirus wirus | 2017-04-23 17:02 | 1.0.0 | LSTM (by Nozdi) | 0.69433 | N/A | 0.68978 | N/A | 0.68382 | N/A | |
260 | wirus wirus | 2017-04-23 10:35 | 1.0.0 | fasttext word 2-ngrams, 10x buckets, character 3-6-ngrams | 0.70222 | N/A | 0.69351 | N/A | 0.68632 | N/A | |
259 | wirus wirus | 2017-04-23 08:15 | 1.0.0 | fasttext word 2-ngrams, 10x buckets, character 3-6-ngrams | 0.70222 | N/A | N/A | N/A | N/A | N/A | |
257 | wirus wirus | 2017-04-23 06:53 | 1.0.0 | fasttext word 2-ngrams, 10x buckets, character 3-6-ngrams | 0.69423 | N/A | 0.68672 | N/A | 0.67830 | N/A | |
264 | wirus wirus | 2017-04-22 20:26 | 1.0.0 | fasttext with word 2-grams and 10x buckets ready-made fasttext | 0.69322 | N/A | 0.68578 | N/A | 0.67851 | N/A | |
263 | wirus wirus | 2017-04-22 19:42 | 1.0.0 | fasttext with word 2-grams ready-made fasttext | 0.68593 | N/A | 0.67887 | N/A | 0.67183 | N/A | |
262 | wirus wirus | 2017-04-22 19:34 | 1.0.0 | fasttext (baseline) ready-made fasttext | 0.67711 | N/A | 0.66870 | N/A | 0.66623 | N/A | |
261 | kaczla | 2017-04-15 16:18 | 1.0.0 | Vowpal Wabbit vowpal-wabbit ready-made | 0.67142 | N/A | 0.66639 | N/A | 0.66109 | N/A | |
258 | kaczla | 2017-04-10 13:26 | 1.0.0 | KenLM ready-made kenlm lm | 0.67077 | N/A | 0.66102 | N/A | 0.65053 | N/A | |
256 | kaczla | 2017-04-10 13:07 | 1.0.0 | Vowpal Wabbit vowpal-wabbit ready-made | 0.67013 | N/A | 0.66531 | N/A | 0.66036 | N/A | |
247 | [anonymized] | 2017-04-04 15:19 | 1.0.0 | bayes with simple stemming fix naive-bayes python self-made | 0.65368 | N/A | 0.63479 | N/A | 0.64012 | N/A | |
249 | [anonymized] | 2017-04-04 13:48 | 1.0.0 | bayes with simple stemming | 0.56540 | N/A | 0.56040 | N/A | 0.56282 | N/A | |
252 | [anonymized] | 2017-04-03 21:08 | 1.0.0 | bayes tf-idf (classic) naive-bayes python self-made | 0.59090 | N/A | 0.58922 | N/A | 0.58420 | N/A | |
253 | [anonymized] | 2017-04-03 20:54 | 1.0.0 | dev-0 tf-idf test (big change) | 0.54156 | N/A | 0.66063 | N/A | 0.65417 | N/A | |
254 | [anonymized] | 2017-04-03 20:07 | 1.0.0 | dev-0 tf-idf test (small change) | 0.58224 | N/A | 0.66063 | N/A | 0.65417 | N/A | |
235 | [anonymized] | 2017-04-01 17:45 | 1.0.0 | logistic regression 40 epoch | 0.66230 | N/A | 0.66063 | N/A | 0.65417 | N/A | |
232 | [anonymized] | 2017-04-01 13:38 | 1.0.0 | dev-0 tf-idf test | 0.59090 | N/A | 0.66089 | N/A | 0.65494 | N/A | |
228 | kaczla | 2017-03-31 21:52 | 1.0.0 | Vowpal Wabbit vowpal-wabbit ready-made | 0.65301 | N/A | 0.64660 | N/A | 0.64337 | N/A | |
239 | [anonymized] | 2017-03-31 17:30 | 1.0.0 | logistic regression 20 epoch python self-made logistic-regression | 0.66397 | N/A | 0.66089 | N/A | 0.65494 | N/A | |
248 | kaczla | 2017-03-27 20:29 | 1.0.0 | Logistic regression python self-made logistic-regression | 0.66180 | N/A | 0.65658 | N/A | 0.65381 | N/A | |
250 | [anonymized] | 2017-03-27 20:11 | 1.0.0 | logistic regression python self-made logistic-regression | N/A | N/A | N/A | N/A | 0.59865 | N/A | |
255 | [anonymized] | 2017-03-27 18:29 | 1.0.0 | logistic regression 10 epoch python self-made logistic-regression | 0.66355 | N/A | 0.66069 | N/A | 0.65399 | N/A | |
236 | [anonymized] | 2017-03-27 16:03 | 1.0.0 | logistic regression 1 epoch python self-made logistic-regression | 0.65032 | N/A | 0.64632 | N/A | 0.63895 | N/A | |
251 | [anonymized] | 2017-03-27 13:21 | 1.0.0 | Regresja python self-made logistic-regression | N/A | N/A | N/A | N/A | 0.62472 | N/A | |
226 | [anonymized] | 2017-03-27 13:20 | 1.0.0 | Regresja | N/A | N/A | N/A | N/A | N/A | N/A | |
225 | [anonymized] | 2017-03-27 13:19 | 1.0.0 | Regresja | N/A | N/A | N/A | N/A | N/A | N/A | |
224 | [anonymized] | 2017-03-27 13:14 | 1.0.0 | Regresja | N/A | N/A | N/A | N/A | 0.63928 | N/A | |
245 | [anonymized] | 2017-03-27 13:07 | 1.0.0 | reg logistyczna 10 epok - shuffle self-made logistic-regression | 0.63823 | N/A | 0.63671 | N/A | 0.62985 | N/A | |
230 | [anonymized] | 2017-03-27 11:08 | 1.0.0 | without feature engineering, Adaptive Moment Estimation, 49 epoch. discriminative better than generative python self-made logistic-regression | 0.67127 | N/A | 0.66687 | N/A | 0.66120 | N/A | |
244 | [anonymized] | 2017-03-27 10:32 | 1.0.0 | reg logistyczna 10 epok self-made logistic-regression | 0.62059 | N/A | 0.61890 | N/A | 0.61450 | N/A | |
243 | [anonymized] | 2017-03-26 23:22 | 1.0.0 | reg logistyczna 1 epoka self-made logistic-regression | 0.59625 | N/A | 0.59012 | N/A | 0.58915 | N/A | |
242 | [anonymized] | 2017-03-26 23:17 | 1.0.0 | reg logistyczna 1 epoka, mały zbiór uczący v2 | 0.66669 | N/A | 0.64823 | N/A | 0.58915 | N/A | |
241 | [anonymized] | 2017-03-26 22:51 | 1.0.0 | reg logistyczna 1 epoka, mały zbiór uczący | 0.66669 | N/A | 0.64823 | N/A | 0.50767 | N/A | |
240 | [anonymized] | 2017-03-23 08:23 | 1.0.0 | 22 epoch, simple SGD with stupid annealing, need to make better SGD, without feature engineering python self-made logistic-regression | 0.66878 | N/A | 0.66422 | N/A | 0.65814 | N/A | |
233 | [anonymized] | 2017-03-20 19:43 | 1.0.0 | Bernoulli Naive Bayes 1 naive-bayes bernoulli python self-made | 0.65483 | N/A | 0.63717 | N/A | 0.64269 | N/A | |
234 | [anonymized] | 2017-03-20 16:28 | 1.0.0 | Logistic Haskell haskell self-made logistic-regression | 0.61675 | N/A | 0.61432 | N/A | 0.61065 | N/A | |
231 | [anonymized] | 2017-03-16 17:27 | 1.0.0 | bayes + tf_idf | 0.59461 | N/A | 0.59014 | N/A | 0.58846 | N/A | |
238 | [anonymized] | 2017-03-16 12:37 | 1.0.0 | corrected bayes naive-bayes multinomial python self-made | 0.66665 | N/A | 0.64844 | N/A | 0.65369 | N/A | |
237 | [anonymized] | 2017-03-15 14:05 | 1.0.0 | sckit-learn naive bayes naive-bayes python ready-made scikit-learn | 0.66680 | N/A | 0.64842 | N/A | 0.65394 | N/A | |
223 | [anonymized] | 2017-03-13 08:36 | 1.0.0 | TurboHaskell 2010 v2 | 0.66435 | N/A | 0.70540 | N/A | 0.65029 | N/A | |
229 | [anonymized] | 2017-03-11 15:54 | 1.0.0 | TurboHaskell 2010 naive-bayes multinomial haskell self-made | 0.66912 | N/A | 0.64996 | N/A | 0.65531 | N/A | |
222 | [anonymized] | 2017-03-11 03:25 | 1.0.0 | Test | 0.58665 | N/A | 0.58153 | N/A | 0.57822 | N/A | |
221 | [anonymized] | 2017-03-11 02:59 | 1.0.0 | Test | 0.59857 | N/A | 0.59280 | N/A | 0.58933 | N/A | |
220 | [anonymized] | 2017-03-11 02:15 | 1.0.0 | Test | 0.62323 | N/A | 0.61270 | N/A | 0.60889 | N/A | |
219 | [anonymized] | 2017-03-11 01:16 | 1.0.0 | Test | 0.59528 | N/A | 0.59049 | N/A | 0.58699 | N/A | |
218 | [anonymized] | 2017-03-11 00:44 | 1.0.0 | Test | 0.63650 | N/A | 0.62513 | N/A | 0.62066 | N/A | |
217 | [anonymized] | 2017-03-11 00:26 | 1.0.0 | Test | 0.63455 | N/A | 0.62364 | N/A | 0.61931 | N/A | |
216 | [anonymized] | 2017-03-11 00:19 | 1.0.0 | Test | 0.63425 | N/A | 0.62240 | N/A | 0.61862 | N/A | |
215 | [anonymized] | 2017-03-10 23:48 | 1.0.0 | Test | N/A | N/A | 0.52997 | N/A | N/A | N/A | |
214 | [anonymized] | 2017-03-09 17:44 | 1.0.0 | Test | 0.66364 | N/A | 0.64468 | N/A | 0.64945 | N/A | |
246 | [anonymized] | 2017-03-09 17:38 | 1.0.0 | Naive Bayes naive-bayes multinomial self-made perl | 0.66521 | N/A | 0.64534 | N/A | 0.65043 | N/A | |
207 | [anonymized] | 2017-03-09 17:18 | 1.0.0 | Test | 0.64469 | N/A | 0.62934 | N/A | 0.62802 | N/A | |
206 | [anonymized] | 2017-03-09 17:03 | 1.0.0 | Yolo | 0.64314 | N/A | 0.62835 | N/A | 0.62709 | N/A | |
205 | [anonymized] | 2017-03-09 16:23 | 1.0.0 | Test | 0.63938 | N/A | 0.62525 | N/A | 0.62369 | N/A | |
204 | [anonymized] | 2017-03-09 16:16 | 1.0.0 | Test | 0.64379 | N/A | 0.62851 | N/A | 0.62740 | N/A | |
203 | [anonymized] | 2017-03-09 15:51 | 1.0.0 | Test | 0.64366 | N/A | 0.62845 | N/A | 0.62752 | N/A | |
202 | [anonymized] | 2017-03-09 15:24 | 1.0.0 | Test | 0.64358 | N/A | 0.62858 | N/A | 0.62751 | N/A | |
201 | [anonymized] | 2017-03-09 14:53 | 1.0.0 | Yolo | 0.64420 | N/A | 0.62867 | N/A | 0.62784 | N/A | |
200 | [anonymized] | 2017-03-09 14:33 | 1.0.0 | Test | 0.54233 | N/A | 0.53734 | N/A | 0.53638 | N/A | |
199 | [anonymized] | 2017-03-07 02:31 | 1.0.0 | Haskell na resorach | 0.66344 | N/A | 0.64638 | N/A | 0.64971 | N/A | |
171 | [anonymized] | 2017-03-02 23:49 | 1.0.0 | I can see that I'll have to teach you how to be villains! naive-bayes multinomial self-made regexp lisp | 0.56843 | N/A | 0.64794 | N/A | 0.65479 | N/A | |
198 | [anonymized] | 2017-03-02 23:35 | 1.0.0 | Throw it at him, not me! | 0.56843 | N/A | 0.64794 | N/A | 0.65375 | N/A | |
197 | [anonymized] | 2017-03-02 23:16 | 1.0.0 | Back to old corpora | 0.56843 | N/A | 0.64794 | N/A | 0.65450 | N/A | |
196 | [anonymized] | 2017-03-02 23:00 | 1.0.0 | Change of preprocessing | 0.56843 | N/A | 0.64794 | N/A | 0.65031 | N/A | |
195 | [anonymized] | 2017-03-02 21:48 | 1.0.0 | Próba raz dwa czy | 0.56843 | N/A | 0.64794 | N/A | 0.64935 | N/A | |
194 | [anonymized] | 2017-03-02 13:01 | 1.0.0 | Test | N/A | N/A | N/A | N/A | 0.62362 | N/A | |
193 | [anonymized] | 2017-03-02 12:22 | 1.0.0 | Yolo | N/A | N/A | N/A | N/A | 0.50288 | N/A | |
192 | [anonymized] | 2017-03-02 12:11 | 1.0.0 | Yolo | N/A | N/A | N/A | N/A | 0.50381 | N/A | |
179 | [anonymized] | 2017-03-02 12:08 | 1.0.0 | Yolo | N/A | N/A | N/A | N/A | 0.00000 | N/A | |
178 | [anonymized] | 2017-03-02 11:15 | 1.0.0 | Now look at this net that I just found; when I say go... | 0.56843 | N/A | 0.64794 | N/A | 0.65331 | N/A | |
168 | [anonymized] | 2017-03-02 10:54 | 1.0.0 | Now look at this net that I just found | 0.56843 | N/A | N/A | N/A | 0.65331 | N/A | |
167 | [anonymized] | 2017-03-02 10:44 | 1.0.0 | Now look at this net | 0.56843 | N/A | N/A | N/A | 0.34669 | N/A | |
166 | [anonymized] | 2017-03-02 08:19 | 1.0.0 | Yolo | N/A | N/A | N/A | N/A | 0.50288 | N/A | |
165 | [anonymized] | 2017-03-02 08:10 | 1.0.0 | Yolo | N/A | N/A | N/A | N/A | 0.50374 | N/A | |
213 | [anonymized] | 2017-03-01 11:40 | 1.0.0 | bayes3 naive-bayes multinomial python self-made | 0.50157 | N/A | 0.50408 | N/A | 0.49981 | N/A | |
189 | [anonymized] | 2017-03-01 11:04 | 1.0.0 | bayes2 | 0.49982 | N/A | 0.50048 | N/A | 0.49941 | N/A | |
188 | [anonymized] | 2017-03-01 07:13 | 1.0.0 | Haskell | 0.63596 | N/A | 0.61912 | N/A | 0.62383 | N/A | |
227 | [anonymized] | 2017-02-28 23:51 | 1.0.0 | something is no yes :X naive-bayes multinomial python self-made | N/A | N/A | N/A | N/A | 0.63928 | N/A | |
187 | [anonymized] | 2017-02-28 22:42 | 1.0.0 | test | N/A | N/A | N/A | N/A | N/A | N/A | |
186 | [anonymized] | 2017-02-28 21:47 | 1.0.0 | something is no yes :X | N/A | N/A | N/A | N/A | N/A | N/A | |
185 | [anonymized] | 2017-02-28 21:37 | 1.0.0 | bayes1 | N/A | N/A | N/A | N/A | N/A | N/A | |
184 | [anonymized] | 2017-02-28 21:13 | 1.0.0 | bayes solution1 | 0.50033 | N/A | 0.50155 | N/A | 0.50085 | N/A | |
183 | [anonymized] | 2017-02-28 19:32 | 1.0.0 | naiwen bajesen, changed equation | 0.66582 | N/A | 0.64740 | N/A | 0.65173 | N/A | |
170 | [anonymized] | 2017-02-28 19:19 | 1.0.0 | naiwen bajesen naive-bayes multinomial python self-made | 0.66600 | N/A | 0.64745 | N/A | 0.65224 | N/A | |
208 | kaczla | 2017-02-28 18:33 | 1.0.0 | Rozwiązanie naive-bayes multinomial python self-made | 0.66092 | N/A | 0.64342 | N/A | 0.65071 | N/A | |
182 | [anonymized] | 2017-02-28 17:06 | 1.0.0 | Rozwiązanie 3 naive-bayes multinomial self-made java | 0.66669 | N/A | 0.64823 | N/A | 0.65482 | N/A | |
180 | [anonymized] | 2017-02-28 16:44 | 1.0.0 | Rozwiązanie 2 | N/A | N/A | N/A | N/A | 0.50006 | N/A | |
177 | [anonymized] | 2017-02-28 15:35 | 1.0.0 | Swag | 0.61095 | N/A | 0.59919 | N/A | 0.60005 | N/A | |
176 | [anonymized] | 2017-02-28 15:04 | 1.0.0 | Yolo | N/A | N/A | N/A | N/A | 0.62326 | N/A | |
175 | [anonymized] | 2017-02-28 10:44 | 1.0.0 | Yolo | N/A | N/A | N/A | N/A | 0.62268 | N/A | |
181 | [anonymized] | 2017-02-27 23:17 | 1.0.0 | Rozwiązanie 1 | N/A | N/A | N/A | N/A | N/A | N/A | |
174 | [anonymized] | 2017-02-27 17:57 | 1.0.0 | First | N/A | N/A | N/A | N/A | 0.53074 | N/A | |
173 | [anonymized] | 2017-02-27 17:44 | 1.0.0 | First | N/A | N/A | N/A | N/A | 0.53376 | N/A | |
172 | [anonymized] | 2017-02-27 17:31 | 1.0.0 | First | N/A | N/A | N/A | N/A | 0.52212 | N/A | |
212 | [anonymized] | 2017-02-27 17:22 | 1.0.0 | moje rozwiazanie 1 stupid python self-made | 0.50123 | N/A | N/A | N/A | 0.50068 | N/A | |
209 | [anonymized] | 2017-02-27 16:23 | 1.0.0 | regexPro stupid python self-made regexp | 0.50033 | N/A | 0.50155 | N/A | 0.50085 | N/A | |
211 | [anonymized] | 2017-02-27 16:21 | 1.0.0 | test stupid python self-made regexp | 0.50241 | N/A | 0.50147 | N/A | 0.50155 | N/A | |
190 | [anonymized] | 2017-02-24 08:31 | 1.0.0 | Simple regexp solution stupid self-made regexp | 0.52190 | N/A | 0.51948 | N/A | 0.51246 | N/A | |
169 | [anonymized] | 2017-02-21 16:58 | 1.0.0 | test simple solution | 0.52869 | N/A | 0.53085 | N/A | 0.52200 | N/A | |
210 | wirus wirus | 2017-01-26 10:08 | 1.0.0 | KenLM + Vowpal Wabbit vowpal-wabbit | 0.71473 | N/A | 0.70513 | N/A | 0.69379 | N/A | |
191 | [anonymized] | 2017-01-08 20:31 | 1.0.0 | Punct split v2 kenlm | 0.66486 | N/A | 0.65639 | N/A | 0.64260 | N/A | |
164 | [anonymized] | 2017-01-08 15:16 | 1.0.0 | KenLM punctuation.split | 0.64351 | N/A | 0.63973 | N/A | 0.62437 | N/A | |
163 | [anonymized] | 2016-12-27 14:04 | 1.0.0 | Train LM 3 grams & tokenize | 0.99425 | N/A | 0.63660 | N/A | 0.64909 | N/A | |
162 | [anonymized] | 2016-12-27 14:00 | 1.0.0 | LM 4grams female | 0.99425 | N/A | 0.63660 | N/A | 0.62213 | N/A | |
161 | [anonymized] | 2016-12-27 13:55 | 1.0.0 | Train LM improvement | 0.99425 | N/A | 0.63660 | N/A | 0.53150 | N/A | |
159 | [anonymized] | 2016-12-27 13:46 | 1.0.0 | Train LM improvement | 0.99425 | N/A | 0.63660 | N/A | 0.58043 | N/A | |
158 | [anonymized] | 2016-12-27 10:22 | 1.0.0 | Kenml devs & train LM & remove punct kenlm | 0.99425 | N/A | 0.63660 | N/A | 0.65591 | N/A | |
157 | [anonymized] | 2016-12-27 10:17 | 1.0.0 | Kenml devs & train LM | 0.99425 | N/A | 0.63660 | N/A | 0.65591 | N/A | |
155 | [anonymized] | 2016-12-27 01:21 | 1.0.0 | 2 w nocy -> wystarczy | 0.98007 | N/A | 0.97880 | N/A | 0.64758 | N/A | |
153 | [anonymized] | 2016-12-27 01:16 | 1.0.0 | 2 w nocy -> wystarczy | 0.98007 | N/A | 0.97880 | N/A | 0.53478 | N/A | |
160 | [anonymized] | 2016-12-27 01:09 | 1.0.0 | kenml & dict v2 | 0.98007 | N/A | 0.97880 | N/A | 0.63106 | N/A | |
152 | [anonymized] | 2016-12-27 00:57 | 1.0.0 | kenml & dict | 0.98007 | N/A | 0.97880 | N/A | 0.59847 | N/A | |
154 | [anonymized] | 2016-12-27 00:43 | 1.0.0 | kenml train LM | 0.98007 | N/A | 0.97880 | N/A | 0.64909 | N/A | |
151 | [anonymized] | 2016-12-27 00:39 | 1.0.0 | kenml v4 | 0.98007 | N/A | 0.97880 | N/A | 0.64758 | N/A | |
150 | [anonymized] | 2016-12-27 00:32 | 1.0.0 | Kenml v3 | 0.98007 | N/A | 0.97880 | N/A | 0.64758 | N/A | |
149 | [anonymized] | 2016-12-27 00:19 | 1.0.0 | Kenml v2 | 0.98007 | N/A | 0.97880 | N/A | 0.62256 | N/A | |
148 | [anonymized] | 2016-12-27 00:04 | 1.0.0 | Kenml v2 | 0.98007 | N/A | 0.97880 | N/A | N/A | N/A | |
147 | [anonymized] | 2016-12-26 23:58 | 1.0.0 | Kenml v2 | 0.98007 | N/A | 0.97880 | N/A | N/A | N/A | |
146 | [anonymized] | 2016-12-26 23:54 | 1.0.0 | Kenml v2 | 0.98007 | N/A | 0.97880 | N/A | N/A | N/A | |
156 | [anonymized] | 2016-12-26 23:25 | 1.0.0 | Kenml v1 | 0.98007 | N/A | 0.97880 | N/A | 0.62129 | N/A | |
145 | [anonymized] | 2016-12-07 09:31 | 1.0.0 | sama | 0.51523 | N/A | N/A | N/A | 0.50463 | N/A | |
144 | [anonymized] | 2016-12-07 09:24 | 1.0.0 | v2 | 0.51523 | N/A | N/A | N/A | 0.51408 | N/A | |
142 | [anonymized] | 2016-12-05 22:38 | 1.0.0 | extra rules, information about each rule accuracy | 0.50095 | N/A | N/A | N/A | N/A | N/A | |
141 | [anonymized] | 2016-12-05 21:59 | 1.0.0 | silly mistake in adding stuff twice to out | 0.50091 | N/A | N/A | N/A | N/A | N/A | |
140 | [anonymized] | 2016-12-05 21:50 | 1.0.0 | Dydlojn zaliczony? | N/A | N/A | N/A | N/A | N/A | N/A | |
139 | [anonymized] | 2016-12-05 00:26 | 1.0.0 | Womendict ver.3 | 0.51991 | N/A | N/A | N/A | 0.51516 | N/A | |
138 | [anonymized] | 2016-12-05 00:03 | 1.0.0 | Womendict ver.2 | 0.52001 | N/A | N/A | N/A | 0.51494 | N/A | |
137 | [anonymized] | 2016-12-04 23:43 | 1.0.0 | Womendict ver.2 | 0.51547 | N/A | N/A | N/A | 0.51278 | N/A | |
136 | [anonymized] | 2016-12-03 23:50 | 1.0.0 | First submission - Womendict | 0.51460 | N/A | N/A | N/A | 0.51278 | N/A | |
135 | [anonymized] | 2016-12-03 23:35 | 1.0.0 | First submission - Womendict | 0.51460 | N/A | N/A | N/A | N/A | N/A | |
134 | [anonymized] | 2016-12-03 23:32 | 1.0.0 | First submission - Womendict | 0.51460 | N/A | N/A | N/A | N/A | N/A | |
133 | [anonymized] | 2016-12-03 23:06 | 1.0.0 | proste rozwiazanie | N/A | N/A | 0.51687 | N/A | 0.51754 | N/A | |
132 | [anonymized] | 2016-12-03 18:15 | 1.0.0 | First submission - Womendict | N/A | N/A | N/A | N/A | N/A | N/A | |
124 | [anonymized] | 2016-12-01 12:40 | 1.0.0 | p3 | 0.49753 | N/A | N/A | N/A | 0.50251 | N/A | |
130 | [anonymized] | 2016-12-01 12:36 | 1.0.0 | 2ga proba | 0.50351 | N/A | N/A | N/A | 0.50251 | N/A | |
105 | [anonymized] | 2016-12-01 12:29 | 1.0.0 | pp | N/A | N/A | N/A | N/A | N/A | N/A | |
129 | [anonymized] | 2016-12-01 02:45 | 1.0.0 | kenlm first attempt | 0.99640 | N/A | 0.99542 | N/A | 0.65047 | N/A | |
128 | [anonymized] | 2016-11-30 14:38 | 1.0.0 | Poprawki w ./runD.py | 0.52735 | N/A | 0.52362 | N/A | 0.52521 | N/A | |
127 | [anonymized] | 2016-11-30 13:49 | 1.0.0 | Push z plikami - wersja słownikowa | 0.52735 | N/A | 0.52362 | N/A | 0.52521 | N/A | |
125 | [anonymized] | 2016-11-30 13:46 | 1.0.0 | Test plikow | 0.52735 | N/A | 0.52362 | N/A | 0.52521 | N/A | |
126 | [anonymized] | 2016-11-30 10:32 | 1.0.0 | KenLM z Train'a* | 0.64377 | N/A | 0.52363 | N/A | 0.62182 | N/A | |
123 | [anonymized] | 2016-11-30 10:30 | 1.0.0 | KenLM z Train'a | 0.64377 | N/A | 0.52363 | N/A | 0.52520 | N/A | |
143 | [anonymized] | 2016-11-30 10:28 | 1.0.0 | merged v2 | 0.54357 | N/A | N/A | N/A | 0.53326 | N/A | |
122 | [anonymized] | 2016-11-30 10:27 | 1.0.0 | merged v1 | N/A | N/A | N/A | N/A | 0.53326 | N/A | |
121 | [anonymized] | 2016-11-30 10:26 | 1.0.0 | merged Mieszko & Maciej solution | N/A | N/A | N/A | N/A | 0.53326 | N/A | |
131 | [anonymized] | 2016-11-30 09:28 | 1.0.0 | dict v1 | 0.51523 | N/A | N/A | N/A | 0.51408 | N/A | |
120 | [anonymized] | 2016-11-30 09:26 | 1.0.0 | Słownik na Trainie | 0.52735 | N/A | 0.52363 | N/A | 0.52520 | N/A | |
118 | [anonymized] | 2016-11-28 16:23 | 1.0.0 | Women - interpunction | 0.53752 | N/A | N/A | N/A | 0.52835 | N/A | |
116 | [anonymized] | 2016-11-28 08:57 | 1.0.0 | KenLM 3gram | 0.98726 | N/A | 0.98495 | N/A | 0.58469 | N/A | |
115 | [anonymized] | 2016-11-28 07:57 | 1.0.0 | KenLM 1st Try | 0.98843 | N/A | 0.98664 | N/A | 0.58520 | N/A | |
114 | [anonymized] | 2016-11-26 14:53 | 1.0.0 | Best On test-A** | 0.61496 | N/A | 0.53644 | N/A | 0.53855 | N/A | |
113 | [anonymized] | 2016-11-26 14:48 | 1.0.0 | Best on test-A | 0.77005 | N/A | 0.73899 | N/A | 0.53038 | N/A | |
112 | [anonymized] | 2016-11-26 14:35 | 1.0.0 | Best on devs | 0.77007 | N/A | 0.73899 | N/A | 0.53038 | N/A | |
111 | [anonymized] | 2016-11-26 14:19 | 1.0.0 | _ | 0.63512 | N/A | 0.61667 | N/A | 0.52541 | N/A | |
110 | [anonymized] | 2016-11-26 13:25 | 1.0.0 | El Dictioannte finallo | 0.67131 | N/A | 0.64660 | N/A | 0.53131 | N/A | |
109 | [anonymized] | 2016-11-26 12:40 | 1.0.0 | Dic v4 cleaning + tr improve | 0.77005 | N/A | 0.73899 | N/A | 0.53040 | N/A | |
108 | [anonymized] | 2016-11-26 10:50 | 1.0.0 | Dic v3 | 0.61498 | N/A | 0.53645 | N/A | 0.53853 | N/A | |
107 | [anonymized] | 2016-11-25 19:33 | 1.0.0 | Dictionary version over 9000 small cleaning | 0.60219 | N/A | 0.53713 | N/A | 0.52968 | N/A | |
106 | [anonymized] | 2016-11-25 19:02 | 1.0.0 | Dictionary version over 9000 dev-1 | 0.59657 | N/A | 0.52953 | N/A | 0.52968 | N/A | |
104 | [anonymized] | 2016-11-25 18:58 | 1.0.0 | Dictionary version over 9000 | 0.59657 | N/A | N/A | N/A | 0.52968 | N/A | |
103 | [anonymized] | 2016-11-23 20:08 | 1.0.0 | Women dictionary v3 | 0.53190 | N/A | N/A | N/A | 0.52321 | N/A | |
102 | [anonymized] | 2016-11-23 19:56 | 1.0.0 | Women dictionary v2 | 0.52793 | N/A | N/A | N/A | 0.52035 | N/A | |
101 | [anonymized] | 2016-11-23 19:47 | 1.0.0 | Women dictionary | 0.52408 | N/A | N/A | N/A | 0.51827 | N/A | |
100 | [anonymized] | 2016-11-23 19:31 | 1.0.0 | Only men v3 | 0.51677 | N/A | N/A | N/A | 0.50993 | N/A | |
99 | [anonymized] | 2016-11-23 17:41 | 1.0.0 | Only men - bigger dictionary | 0.51156 | N/A | N/A | N/A | 0.50724 | N/A | |
117 | [anonymized] | 2016-11-23 14:06 | 1.0.0 | words v1 | N/A | N/A | N/A | N/A | N/A | N/A | |
98 | [anonymized] | 2016-11-22 23:15 | 1.0.0 | Dictionary - only women | 0.50000 | N/A | N/A | N/A | 0.50000 | N/A | |
97 | [anonymized] | 2016-11-22 23:07 | 1.0.0 | "First attempt - dictionary" | 0.50867 | N/A | N/A | N/A | 0.50562 | N/A | |
96 | [anonymized] | 2016-11-22 21:10 | 1.0.0 | test submition (all F) | 0.50000 | N/A | N/A | N/A | 0.50000 | N/A | |
95 | [anonymized] | 2016-11-22 18:29 | 1.0.0 | female + male dict | 0.53915 | N/A | N/A | N/A | 0.53150 | N/A | |
94 | [anonymized] | 2016-11-22 18:14 | 1.0.0 | male + female dict | N/A | N/A | N/A | N/A | 0.53001 | N/A | |
93 | [anonymized] | 2016-11-20 17:14 | 1.0.0 | add swears | 0.53714 | N/A | N/A | N/A | 0.53001 | N/A | |
92 | [anonymized] | 2016-11-20 17:07 | 1.0.0 | add swears | N/A | N/A | N/A | N/A | 0.53001 | N/A | |
119 | [anonymized] | 2016-11-20 09:42 | 1.0.0 | dict v4 | 0.54134 | N/A | N/A | N/A | 0.53208 | N/A | |
91 | [anonymized] | 2016-11-19 23:15 | 1.0.0 | dict v3 | 0.53816 | N/A | N/A | N/A | 0.52971 | N/A | |
90 | [anonymized] | 2016-11-19 22:18 | 1.0.0 | improve dict v2 | 0.53785 | N/A | N/A | N/A | 0.52971 | N/A | |
89 | [anonymized] | 2016-11-19 22:13 | 1.0.0 | improve dict | 0.53785 | N/A | N/A | N/A | 0.52399 | N/A | |
88 | [anonymized] | 2016-11-19 20:07 | 1.0.0 | Dictionary approach | 0.52699 | N/A | N/A | N/A | 0.52399 | N/A | |
87 | wirus wirus | 2016-11-15 09:29 | 1.0.0 | trivial baseline (only female) | 0.50000 | N/A | 0.50000 | N/A | 0.50000 | N/A |