# RetroC2 temporal classification challenge

Guess the publication year of a Polish text. [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 RMSE | dev-1 RMSE | test-A RMSE | |
---|---|---|---|---|---|---|---|---|

23 | s444383 | 2022-06-21 10:15 | 1.0.0 | new prediction | 21.295 | 21.579 | 23.268 | |

136 | [anonymized] | 2022-06-07 21:46 | 1.0.0 | s478874 linear-regression | 66.030 | 42.681 | 52.826 | |

28 | [anonymized] | 2022-05-29 20:41 | 1.0.0 | Prześlij pliki do '' linear-regression | 21.698 | 21.996 | 23.507 | |

29 | s444455 | 2022-05-26 22:04 | 1.0.0 | regresja self-made linear-regression | 21.698 | 21.996 | 23.507 | |

22 | s444383 | 2022-05-25 06:55 | 1.0.0 | new prediction linear-regression | 21.295 | 21.579 | 23.268 | |

21 | s444383 | 2022-05-20 13:36 | 1.0.0 | changes | N/A | N/A | 23.268 | |

53 | s443930 | 2022-05-19 21:03 | 1.0.0 | s443930 linear-regression | 24.659 | 25.312 | 26.515 | |

27 | s444415 | 2022-05-19 15:06 | 1.0.0 | 444415 linear-regression | 21.698 | 21.996 | 23.507 | |

49 | Martyna Druminska | 2022-05-19 11:42 | 1.0.0 | my brilliant solution self-made linear-regression | 22.747 | N/A | 24.816 | |

62 | Adam Wojdyła | 2022-05-18 00:10 | 1.0.0 | 4444507 self-made linear-regression | 29.731 | 26.882 | 28.143 | |

57 | Adam Wojdyła | 2022-05-18 00:07 | 1.0.0 | 4444507 self-made linear-regression | 27.500 | 26.100 | 26.964 | |

50 | s478839 | 2022-05-17 23:12 | 1.0.0 | s478839 self-made linear-regression | 22.768 | 22.983 | 24.865 | |

48 | [anonymized] | 2022-05-17 22:07 | 1.0.0 | s478831 linear-regression | 22.747 | 22.932 | 24.816 | |

35 | s444018 | 2022-05-17 21:32 | 1.0.0 | s444018 self-made linear-regression | 22.025 | 22.096 | 23.855 | |

26 | s444501 | 2022-05-17 21:30 | 1.0.0 | s444501 linear-regression | 21.675 | 21.941 | 23.461 | |

41 | Marcin Kostrzewski | 2022-05-17 21:28 | 1.0.0 | Mean publication year, stop words removed. Trained on 50000 examples linear-regression scikit-learn stop-words | 22.477 | 22.581 | 24.204 | |

52 | s478815 | 2022-05-17 21:27 | 1.0.0 | 478815 linear-regression | 24.287 | 24.843 | 26.113 | |

181 | s478815 | 2022-05-17 21:08 | 1.0.0 | 478815 self-made | N/A | N/A | N/A | |

38 | s444356 | 2022-05-17 20:52 | 1.0.0 | s444356 linear-regression | 24.614 | 22.163 | 24.032 | |

180 | s478815 | 2022-05-17 20:29 | 1.0.0 | 478815 self-made linear-regression | 66.062 | N/A | N/A | |

179 | s478815 | 2022-05-17 20:12 | 1.0.0 | 478815 self-made | 63.895 | N/A | N/A | |

24 | s444417 | 2022-05-17 19:00 | 1.0.0 | linear regression self-made linear-regression | 21.574 | 21.818 | 23.438 | |

33 | Kamil Guttmann | 2022-05-17 18:42 | 1.0.0 | s444380 linear regression tf-idf linear-regression | 21.728 | 22.105 | 23.595 | |

51 | s478873 | 2022-05-17 11:14 | 1.0.0 | s478873 | 23.342 | 23.852 | 25.479 | |

56 | [anonymized] | 2022-05-17 09:00 | 1.0.0 | 444421 linear-regression | 24.925 | 25.599 | 26.884 | |

39 | Mikołaj Pokrywka | 2022-05-17 06:43 | 1.0.0 | 444463 linear-regression | 24.613 | 22.162 | 24.033 | |

55 | s478873 | 2022-05-16 22:56 | 1.0.0 | s478873 self-made linear-regression | 24.673 | 25.366 | 26.566 | |

47 | s409771 | 2022-05-16 17:35 | 1.0.0 | first solution linear-regression | 24.077 | 22.447 | 24.782 | |

68 | s478840 | 2022-05-16 12:56 | 1.0.0 | s478840 linear-regression | 27.180 | 27.811 | 28.809 | |

30 | [anonymized] | 2022-05-14 17:03 | 1.0.0 | 478841 self-made linear-regression | 21.698 | 21.996 | 23.507 | |

32 | s444354 | 2022-05-14 01:44 | 1.0.0 | s444354 self-made linear-regression | 21.702 | 21.998 | 23.515 | |

63 | s478846 | 2022-05-11 13:27 | 1.0.0 | First solution linear-regression | 28.516 | 26.476 | 28.166 | |

37 | s444452 | 2022-05-09 19:43 | 1.0.0 | s444452 self-made linear-regression | 22.181 | 22.201 | 23.988 | |

36 | s444386 | 2022-05-09 13:38 | 1.0.0 | linear regresion 444386 linear-regression | 22.072 | 22.194 | 23.951 | |

25 | s478855 | 2022-05-08 17:27 | 1.0.0 | s478855 self-made linear-regression | 21.668 | 21.944 | 23.456 | |

31 | s444476 | 2022-05-01 11:59 | 1.0.0 | s444476 linear-regression | 21.698 | 21.996 | 23.507 | |

54 | ked | 2022-04-29 14:01 | 1.0.0 | s449288 - simple linear regression with 10% of train dataset linear-regression | 25.237 | 25.579 | 26.550 | |

46 | p/tlen | 2021-05-05 09:07 | 1.0.0 | linear regression with PyTorch batch-size=1 epochs=1 hash-bit-size=18 token-root-len=7 linear-regression pytorch-nn adam | 23.183 | 22.192 | 24.733 | |

79 | p/tlen | 2021-05-04 20:53 | 1.0.0 | linear regression with PyTorch batch-size=1 epochs=1 hash-bit-size=18 learning-rate=3.2e-2 token-root-len=7 linear-regression pytorch-nn | 38.639 | 33.722 | 36.429 | |

92 | p/tlen | 2021-05-04 20:41 | 1.0.0 | linear regression with PyTorch batch-size=1 epochs=1 hash-bit-size=18 learning-rate=3.2e-2 token-root-len=7 linear-regression pytorch-nn | 42.860 | 37.115 | 39.378 | |

82 | p/tlen | 2021-05-04 19:58 | 1.0.0 | linear regression with PyTorch batch-size=1 epochs=1 hash-bit-size=18 learning-rate=3.2e-2 token-root-len=7 linear-regression pytorch-nn | 45.891 | 33.055 | 37.160 | |

15 | kubapok | 2020-06-30 22:45 | 1.0.0 | fasttext as classification problem Mikolaj Bachorz experiment reproduction 50 epochs devs included | 11.250 | 10.806 | 20.805 | |

40 | kubapok | 2020-06-30 21:19 | 1.0.0 | fasttext as classification problem Mikolaj Bachorz experiment reproduction 50 epochs | 21.670 | 19.827 | 24.101 | |

69 | kubapok | 2020-06-30 13:35 | 1.0.0 | fasttext as classification problem Mikolaj Bachorz experiment reproduction | 33.336 | 25.245 | 30.931 | |

141 | [anonymized] | 2020-06-24 00:12 | 1.0.0 | xgboost solution ready-made xgboost | 54.560 | 52.444 | 54.317 | |

161 | [anonymized] | 2020-06-18 11:49 | 1.0.0 | xgboost skrypt + pliki out ready-made xgboost | 66.925 | 64.530 | 67.634 | |

1 | kubapok | 2020-06-02 17:12 | 1.0.0 | linear layer on top of polish roberta- Adam lr 1e-07 | 12.260 | 12.317 | 13.328 | |

2 | kubapok | 2020-05-28 15:09 | 1.0.0 | linear layer on top of polish roberta (both finetuned 2 epochs) | 13.671 | 13.723 | 15.606 | |

14 | kubapok | 2020-05-24 18:27 | 1.0.0 | xgb on top of polish roberta mean token | 17.685 | 17.286 | 20.458 | |

8 | [anonymized] | 2020-05-18 08:34 | 1.0.0 | v3 | 0.913 | 1.048 | 18.923 | |

5 | [anonymized] | 2020-05-18 05:20 | 1.0.0 | v2 fasttext | 1.084 | 1.216 | 17.989 | |

3 | kubapok | 2020-01-28 19:54 | 1.0.0 | ensemble of 4, bilstm | 15.326 | 15.060 | 17.262 | |

7 | kubapok | 2020-01-28 19:52 | 1.0.0 | ensemble of 4, bilstm | 16.342 | 16.339 | 18.758 | |

6 | kubapok | 2020-01-19 22:49 | 1.0.0 | keras bilstm lstm | 16.342 | 16.339 | 18.758 | |

34 | kubapok | 2020-01-12 21:38 | 1.0.0 | keras fasttext like | 22.069 | 20.969 | 23.782 | |

17 | kubapok | 2020-01-09 21:38 | 1.0.0 | tfidf baseline maxminclipping linear-regression tf-idf | 21.327 | 21.222 | 23.114 | |

18 | kubapok | 2020-01-09 16:16 | 1.0.0 | tfidf lr baseline tf-idf | 21.673 | 21.317 | 23.167 | |

150 | [anonymized] | 2019-07-21 14:25 | 1.0.0 | CNN 30,31,32 | 72.250 | 52.565 | 57.806 | |

154 | [anonymized] | 2019-07-16 18:50 | 1.0.0 | CNN, lr 0.001, 100 filters, [4,5,6,7] filter sizes, dropout 0.5 | 76.269 | N/A | 59.799 | |

153 | [anonymized] | 2019-07-16 18:17 | 1.0.0 | CNN, lr 0.001, 100 filters, [4,5,6,7] filter sizes, dropout 0.5 | N/A | N/A | 59.799 | |

127 | [anonymized] | 2019-07-13 20:29 | 1.0.0 | Feedforward, word embeddings NKJP + Wikipedia, model01 | 61.224 | 43.289 | 47.333 | |

126 | [anonymized] | 2019-07-13 20:15 | 1.0.0 | Feedforward, word embeddings NKJP + Wikipedia, model01 | 61.224 | N/A | 47.333 | |

125 | [anonymized] | 2019-07-13 19:20 | 1.0.0 | Feedforward, word embeddings NKJP + Wikipedia, model48 | N/A | N/A | 47.333 | |

119 | [anonymized] | 2019-07-13 19:06 | 1.0.0 | Feedforwar, word embeddings NKJP + Wikipedia | N/A | N/A | 47.045 | |

166 | [anonymized] | 2019-07-13 12:19 | 1.0.0 | Char CNN 30e, 0.001lr | 69.770 | 45.606 | 74.298 | |

148 | [anonymized] | 2019-06-12 14:49 | 1.0.0 | wordlist 4 | 63.600 | 55.405 | 56.295 | |

130 | [anonymized] | 2019-06-12 14:40 | 1.0.0 | wordlist 4 | 57.742 | 51.007 | 51.853 | |

139 | [anonymized] | 2019-06-12 14:16 | 1.0.0 | wordlist 4 | 64.766 | 50.656 | 53.026 | |

142 | [anonymized] | 2019-06-12 14:12 | 1.0.0 | wordlist 4 | 63.817 | 53.179 | 54.668 | |

140 | [anonymized] | 2019-06-12 13:56 | 1.0.0 | wordlist 4 | 64.448 | 51.292 | 53.421 | |

157 | [anonymized] | 2019-06-12 12:20 | 1.0.0 | wordlist 4 | 65.050 | 63.803 | 63.186 | |

133 | [anonymized] | 2019-06-12 12:00 | 1.0.0 | wordlist 4 | 65.818 | 49.338 | 52.340 | |

131 | [anonymized] | 2019-06-12 11:27 | 1.0.0 | wordlist 4 | 66.698 | 48.749 | 52.150 | |

168 | [anonymized] | 2019-06-12 11:13 | 1.0.0 | wordlist + random choice 2 | 83.138 | 78.207 | 79.643 | |

91 | [anonymized] | 2019-06-10 15:23 | 1.0.0 | graf self-made linear-regression graph | 48.238 | 41.755 | 39.246 | |

124 | [anonymized] | 2019-06-08 11:23 | 1.0.0 | Bayes to predict some time range fix naive-bayes | 41.561 | 50.584 | 47.205 | |

58 | [anonymized] | 2019-06-04 15:41 | 1.0.0 | Vowpal Wabbit quadratic model + graph v2 vowpal-wabbit graph | 27.479 | 23.632 | 27.297 | |

66 | [anonymized] | 2019-06-04 15:05 | 1.0.0 | Vowpal Wabbit quadratic model + graph vowpal-wabbit graph | 28.930 | 25.384 | 28.401 | |

59 | [anonymized] | 2019-06-04 12:03 | 1.0.0 | vw first encounter(loss function, -b 27, passes=20, quadratic model) vowpal-wabbit graph | 26.939 | 23.654 | 27.384 | |

42 | [anonymized] | 2019-06-01 15:50 | 1.0.0 | tf ready-made linear-regression tf | 23.149 | 21.791 | 24.291 | |

118 | [anonymized] | 2019-05-29 06:10 | 1.0.0 | Test feedforward | 69.770 | 45.606 | 46.987 | |

165 | [anonymized] | 2019-05-27 14:04 | 1.0.0 | Bayes to predict some time range naive-bayes | 65.137 | 82.283 | 71.060 | |

167 | [anonymized] | 2019-05-27 13:06 | 1.0.0 | naive bayes naive-bayes | 69.913 | 89.059 | 77.466 | |

129 | [anonymized] | 2019-05-22 08:38 | 1.0.0 | BiLSTM 30epochs 22nd new tokenizer | 69.770 | 45.606 | 50.381 | |

178 | [anonymized] | 2019-05-22 08:36 | 1.0.0 | BiLSTM 3- ep0 epochs 22nd new tokenizer | 69.770 | 45.606 | N/A | |

155 | [anonymized] | 2019-05-21 19:00 | 1.0.0 | BiLSTM 3- epochs 22nd new tokenizer | 69.770 | 45.606 | 62.489 | |

61 | [anonymized] | 2019-05-17 18:45 | 1.0.0 | Vowpal Wabbit - linear regression + graph vowpal-wabbit graph | 29.092 | 24.445 | 27.808 | |

45 | [anonymized] | 2019-05-11 22:40 | 1.0.0 | BiLSTM, 30epochs, model 28th | 69.770 | 45.606 | 24.605 | |

73 | [anonymized] | 2019-05-09 23:19 | 1.0.0 | ready-made tf-df: a fix ready-made linear-regression tf-idf | 29.817 | 26.292 | 33.388 | |

44 | [anonymized] | 2019-05-09 17:51 | 1.0.0 | BiLSTM, 30epochs, model 28th | N/A | 45.606 | 24.605 | |

43 | [anonymized] | 2019-05-09 05:03 | 1.0.0 | BiLSTM, 30epochs, model 28th | N/A | N/A | 24.605 | |

64 | [anonymized] | 2019-05-06 15:08 | 1.0.0 | tfidf 3k words low range | 59.446 | N/A | 28.277 | |

60 | [anonymized] | 2019-05-06 15:05 | 1.0.0 | tfidf 50k words low reduction range ready-made linear-regression tf-idf | 59.446 | N/A | 27.708 | |

107 | [anonymized] | 2019-05-06 15:01 | 1.0.0 | My solution go.php rule-based | 57.517 | 45.142 | 42.687 | |

102 | [anonymized] | 2019-05-06 14:49 | 1.0.0 | transfer files to VM | 59.446 | N/A | 40.674 | |

101 | [anonymized] | 2019-05-06 14:08 | 1.0.0 | all documents to predict on vector 30k words TFIDF KNN ready-made knn tf-idf | 59.446 | N/A | 40.674 | |

74 | [anonymized] | 2019-05-06 13:57 | 1.0.0 | all documents to predict on vector 10k words TFIDF Linear ready-made linear-regression tf-idf | 59.446 | N/A | 34.444 | |

114 | [anonymized] | 2019-05-06 13:53 | 1.0.0 | 300 documents to predict on vector 10k words TFIDF Linear self-made linear-regression tf-idf | 59.446 | N/A | 43.571 | |

20 | [anonymized] | 2019-05-03 19:49 | 1.0.0 | BiLSTM w/o sorting | N/A | 43.734 | 23.225 | |

70 | [anonymized] | 2019-05-03 18:37 | 1.0.0 | 3000 words tf-idf self-made linear-regression tf-idf | 34.237 | 30.865 | 32.631 | |

71 | [anonymized] | 2019-05-03 17:25 | 1.0.0 | 2500 words tf-idf | 34.857 | 31.521 | 32.912 | |

72 | [anonymized] | 2019-05-03 16:45 | 1.0.0 | 2000 words tf-idf self-made linear-regression tf-idf | 35.672 | 32.114 | 33.384 | |

78 | [anonymized] | 2019-05-03 16:27 | 1.0.0 | 1000 words tf-idf | 38.925 | 35.224 | 36.004 | |

19 | [anonymized] | 2019-05-03 07:17 | 1.0.0 | BiLSTM w\o sorting | N/A | N/A | 23.225 | |

128 | [anonymized] | 2019-04-29 14:57 | 1.0.0 | self made TFIDF 1000 documents 500 word vector KNN4 self-made linear-regression knn tf-idf | 59.446 | N/A | 48.323 | |

89 | [anonymized] | 2019-04-28 20:08 | 1.0.0 | linner ready tf ready-made linear-regression tf | 51.562 | 41.400 | 39.240 | |

123 | [anonymized] | 2019-04-27 18:41 | 1.0.0 | change encoding | 59.446 | N/A | 47.094 | |

122 | [anonymized] | 2019-04-27 17:54 | 1.0.0 | tfidf - not ready | 59.446 | N/A | 47.094 | |

4 | Artur Nowakowski | 2019-04-19 07:52 | 1.0.0 | optimized word2vec + nn neural-network word2vec | 15.913 | 14.587 | 17.789 | |

13 | Artur Nowakowski | 2019-04-17 16:02 | 1.0.0 | wordvec + nn 5-fold validation | 17.644 | 16.803 | 20.232 | |

12 | Artur Nowakowski | 2019-04-17 09:20 | 1.0.0 | word2vec + nn optimized for dev1 | 18.714 | 15.851 | 19.915 | |

149 | [anonymized] | 2019-04-16 22:30 | 1.0.0 | Now with CHARTS self-made linear-regression graph | 62.496 | 57.478 | 57.119 | |

77 | [anonymized] | 2019-04-16 16:36 | 1.0.0 | simple lin reg self-made linear-regression graph | N/A | N/A | 35.958 | |

88 | [anonymized] | 2019-04-16 16:24 | 1.0.0 | Figure add self-made linear-regression graph | N/A | 40.479 | 38.431 | |

10 | Artur Nowakowski | 2019-04-16 16:13 | 1.0.0 | basic word2vec + nn solution (optimized for dev0) | 16.793 | 17.702 | 19.557 | |

87 | [anonymized] | 2019-04-16 15:36 | 1.0.0 | years in text self-made linear-regression graph | 50.710 | N/A | 38.419 | |

172 | [anonymized] | 2019-04-16 15:20 | 1.0.0 | mean year found in text | 1341469.078 | N/A | 1345919.117 | |

94 | [anonymized] | 2019-04-16 13:24 | 1.0.0 | Regresja liniowa (USA |usa |stany zjednoczone) + Lata | 48.599 | 42.924 | 39.382 | |

76 | [anonymized] | 2019-04-16 13:14 | 1.0.0 | simple linear regression self-made linear-regression graph | N/A | N/A | 35.958 | |

93 | [anonymized] | 2019-04-16 13:10 | 1.0.0 | Regresja liniowa (USA|usa|stany zjednoczone) plus lata | N/A | 42.924 | 39.382 | |

104 | [anonymized] | 2019-04-16 09:49 | 1.0.0 | linear regression self-made linear-regression graph | 61.474 | 40.228 | 41.220 | |

121 | [anonymized] | 2019-04-16 01:12 | 1.0.0 | Merge branch 'master' of ssh://gonito.net/huntekah/retroc2 self-made linear-regression graph | 59.446 | N/A | 47.094 | |

120 | [anonymized] | 2019-04-16 00:56 | 1.0.0 | Honest one variable solution, without fancy, and thus easy methods self-made linear-regression graph | 59.446 | N/A | 47.094 | |

65 | [anonymized] | 2019-04-15 20:33 | 1.0.0 | Basic ready-made solution with one column ready-made linear-regression tf-idf | 30.212 | 26.677 | 28.298 | |

109 | [anonymized] | 2019-04-15 18:40 | 1.0.0 | excel plots :) self-made linear-regression graph | 52.092 | 46.796 | 42.991 | |

99 | [anonymized] | 2019-04-15 18:39 | 1.0.0 | XXDDDD my solution self-made linear-regresion ADD CHARTS selfMadeLinearRegres_Solver.py self-made linear-regression graph | 48.699 | 42.928 | 39.904 | |

100 | [anonymized] | 2019-04-15 16:35 | 1.0.0 | one variable regression self-made linear-regression graph | N/A | N/A | 40.219 | |

108 | [anonymized] | 2019-04-15 12:46 | 1.0.0 | hope that is final one self-made linear-regression | 52.092 | 46.796 | 42.991 | |

177 | [anonymized] | 2019-04-15 12:40 | 1.0.0 | now more iterations | 52.092 | 46.796 | N/A | |

170 | [anonymized] | 2019-04-15 12:02 | 1.0.0 | forgot to add out files xd | 279.733 | 285.868 | 269.460 | |

171 | [anonymized] | 2019-04-15 11:57 | 1.0.0 | date detection and linear regression | 271.215 | 296.533 | 271.270 | |

98 | [anonymized] | 2019-04-15 11:54 | 1.0.0 | XDDD my solution self-made linear-regresion selfMadeLinearRegres_Solver.py self-made linear-regression | 48.699 | 42.928 | 39.904 | |

16 | Artur Nowakowski | 2019-04-15 11:40 | 1.0.0 | Linear regression with TF-IDF weighing scheme ready-made linear-regression tf-idf | 20.750 | 20.676 | 22.341 | |

67 | [anonymized] | 2019-04-11 18:56 | 1.0.0 | Basic ready-made solution ready-made linear-regression tf-idf | 30.723 | 26.963 | 28.668 | |

90 | [anonymized] | 2019-04-10 10:28 | 1.0.0 | wykrywanie dat top prio rule-based | 48.238 | 41.755 | 39.246 | |

176 | [anonymized] | 2019-04-09 19:11 | 1.0.0 | not many rules rule-based | N/A | N/A | N/A | |

145 | [anonymized] | 2019-04-09 16:55 | 1.0.0 | post OCR signs | 69.841 | N/A | 55.400 | |

144 | [anonymized] | 2019-04-09 16:53 | 1.0.0 | post OCR signs | 73.236 | N/A | 55.400 | |

164 | [anonymized] | 2019-04-09 16:51 | 1.0.0 | post OCR signs | 73.236 | N/A | 69.970 | |

163 | [anonymized] | 2019-04-09 16:46 | 1.0.0 | post OCR signs | 351.237 | N/A | 69.970 | |

147 | [anonymized] | 2019-04-09 16:43 | 1.0.0 | solution with simple word list2 rule-based | 63.523 | 55.172 | 56.124 | |

160 | [anonymized] | 2019-04-09 16:28 | 1.0.0 | post OCR signs | N/A | N/A | 66.046 | |

106 | [anonymized] | 2019-04-09 16:15 | 1.0.0 | My solution basic rule-based solver.py rule-based | 57.517 | 45.142 | 42.682 | |

105 | [anonymized] | 2019-04-09 16:12 | 1.0.0 | fourth solution rule-based | N/A | 43.947 | 41.235 | |

83 | [anonymized] | 2019-04-09 15:28 | 1.0.0 | bad solution 2 rule-based | N/A | N/A | 37.197 | |

152 | [anonymized] | 2019-04-09 13:07 | 1.0.0 | Bad rule-based solution rule-based | N/A | N/A | 59.748 | |

86 | [anonymized] | 2019-04-08 21:02 | 1.0.0 | better stupid solution rule-based | 50.229 | 39.773 | 38.368 | |

85 | [anonymized] | 2019-04-08 19:59 | 1.0.0 | stupid solution rule-based | 50.234 | 41.683 | 38.177 | |

103 | [anonymized] | 2019-04-08 18:10 | 1.0.0 | my very simple solution3 rule-based | 50.607 | 44.078 | 40.838 | |

96 | [anonymized] | 2019-04-08 15:24 | 1.0.0 | rulebased rule-based | N/A | N/A | 39.781 | |

112 | [anonymized] | 2019-04-08 15:23 | 1.0.0 | all rules rule-based | 54.064 | N/A | 43.101 | |

111 | [anonymized] | 2019-04-08 15:21 | 1.0.0 | slowa | 57.733 | N/A | 43.101 | |

95 | [anonymized] | 2019-04-08 12:54 | 1.0.0 | Based on a list with years rule-based | 48.666 | 43.031 | 39.695 | |

175 | [anonymized] | 2019-04-08 12:42 | 1.0.0 | Improve guessing accuracy for de0 dev1 | 48.666 | 43.031 | N/A | |

146 | [anonymized] | 2019-04-08 11:32 | 1.0.0 | my very simple solution2A | 63.411 | 49.901 | 55.583 | |

174 | [anonymized] | 2019-04-07 22:13 | 1.0.0 | my very simple solution1 | 63.411 | 49.901 | N/A | |

110 | [anonymized] | 2019-04-07 19:51 | 1.0.0 | complicated rules make Good/Bad results | 57.733 | N/A | 43.101 | |

151 | [anonymized] | 2019-04-07 18:17 | 1.0.0 | most popular words in 10-year periods java rule-based | 51.117 | 54.080 | 57.981 | |

132 | [anonymized] | 2019-04-07 09:44 | 1.0.0 | simple rule based rule-based | 57.733 | N/A | 52.306 | |

159 | [anonymized] | 2019-04-07 08:42 | 1.0.0 | my best solution rule-based | 86.441 | 61.280 | 65.953 | |

113 | [anonymized] | 2019-04-06 16:59 | 1.0.0 | based on historical word list rule-based | 57.220 | 46.287 | 43.424 | |

97 | [anonymized] | 2019-04-05 19:22 | 1.0.0 | simple set solution rule-based | 53.933 | 37.245 | 39.885 | |

81 | Artur Nowakowski | 2019-04-03 11:52 | 1.0.0 | simple solution rule-based | 50.255 | 37.596 | 36.563 | |

143 | [anonymized] | 2019-04-02 16:50 | 1.0.0 | LSTM EPOCHS=10 LR=0.001 DROPOUT=0.1 - input w/o filtering, adding missing line neural-network bilstm | 66.348 | N/A | 55.115 | |

158 | [anonymized] | 2019-03-30 19:01 | 1.0.0 | LSTM EPOCHS=10 LR=0.001 DROPOUT=0.2 - input w/o filtering, adding missing line neural-network bilstm | 73.227 | N/A | 63.913 | |

169 | [anonymized] | 2019-03-27 17:36 | 1.0.0 | LSTM EPOCHS=10 LR=0.001 DROPOUT=0.5 - input w/o filtering, adding missing line neural-network bilstm | N/A | N/A | 97.902 | |

138 | [anonymized] | 2019-02-19 09:08 | 1.0.0 | Modyfikacja skryptu do uruchomienia | 72.529 | N/A | 52.950 | |

137 | [anonymized] | 2019-02-19 08:50 | 1.0.0 | 5 epochs; filtered input; feedforward network neural-network | 72.529 | N/A | 52.950 | |

156 | p/tlen | 2018-08-30 20:16 | 1.0.0 | tescik 5 stupid | N/A | N/A | 62.502 | |

162 | p/tlen | 2018-08-30 19:38 | 1.0.0 | tescik 3 stupid | N/A | N/A | 69.597 | |

116 | p/tlen | 2018-08-30 19:31 | 1.0.0 | tescik2 stupid | N/A | N/A | 46.513 | |

117 | p/tlen | 2018-08-30 19:27 | 1.0.0 | test stupid | N/A | N/A | 46.591 | |

135 | [anonymized] | 2018-08-14 18:29 | 1.0.0 | dev0 first solution stupid neural-network | 57.863 | N/A | 52.721 | |

80 | p/tlen | 2018-05-29 07:19 | 1.0.0 | try xgboost xgboost | 39.907 | 37.228 | 36.484 | |

11 | p/tlen | 2017-07-08 12:52 | 1.0.0 | VW with yearly resolution | 23.065 | 17.178 | 19.695 | |

173 | p/tlen | 2017-07-08 12:50 | 1.0.0 | VW with yearly resolution | N/A | N/A | N/A | |

75 | p/tlen | 2017-07-07 10:23 | 1.0.0 | year references combined with hand-crafted rules | 42.302 | 42.654 | 35.807 | |

115 | p/tlen | 2017-07-07 09:41 | 1.0.0 | hand-crafted rules | 48.602 | 50.646 | 44.167 | |

84 | p/tlen | 2017-07-07 09:11 | 1.0.0 | year references | 46.447 | 39.809 | 37.652 | |

9 | p/tlen | 2017-05-31 04:49 | 1.0.0 | VW -nn 6 on up to 4-grams and [5-7] tokens stupid vowpal-wabbit neural-network | 22.413 | 16.991 | 19.501 | |

134 | p/tlen | 2017-05-26 21:48 | 1.0.0 | null model stupid null-model | 57.735 | 51.906 | 52.539 |