"He Said She Said" classification challenge (2nd edition)

Guess whether a text in Polish was written by a man or woman.

# submitter when description dev-0 Accuracy dev-1 Accuracy test-A Accuracy
107 Tomasz 2019-02-21 22:58 my brilliant solution 0.65445 N/A 0.64519
119 [anonymised] 2019-02-21 15:18 Michal Mioduszewski - solution 0.64952 N/A 0.63986
72 [anonymised] 2019-01-27 16:29 dampie5 solution v3.5 0.66219 0.65437 0.65190
150 [anonymised] 2019-01-27 16:25 dampie5 solution v3.4 0.65339 0.63752 0.62094
86 [anonymised] 2019-01-27 16:18 dampie5 solution v3.3 0.65339 0.63752 0.64933
108 [anonymised] 2019-01-27 15:43 dampie5 solution v3.2 0.65339 0.63752 0.64495
114 [anonymised] 2019-01-27 15:36 dampie5 solution v3.1 0.65339 0.63752 0.64203
116 [anonymised] 2019-01-27 15:30 dampie5 solution v3 0.65339 0.63752 0.64137
106 [anonymised] 2019-01-27 13:51 asdds N/A N/A 0.64519
28 [anonymised] 2019-01-27 13:33 Blah N/A N/A 0.66275
126 [anonymised] 2019-01-27 13:11 dampie5 solution v2 0.65070 0.61818 0.63449
41 [anonymised] 2019-01-27 13:01 Wesja milion7 0.63707 N/A 0.65659
43 [anonymised] 2019-01-27 12:50 Wesja milion6 0.63707 N/A 0.65600
42 [anonymised] 2019-01-27 12:46 Wesja milion5 N/A N/A 0.65600
70 [anonymised] 2019-01-27 12:31 Wesja milion4 N/A N/A 0.65223
137 [anonymised] 2019-01-27 12:10 Wesja milion3 N/A N/A 0.62460
188 [anonymised] 2019-01-27 12:06 Wesja milion2 N/A N/A 0.58706
210 [anonymised] 2019-01-27 11:23 Wesja milion N/A N/A 0.55697
27 [anonymised] 2019-01-27 09:40 Test4 N/A N/A 0.66276
170 [anonymised] 2019-01-27 08:11 Test N/A N/A 0.59881
90 [anonymised] 2019-01-27 08:07 proba milion nowa N/A N/A 0.64888
158 [anonymised] 2019-01-27 07:53 probamilion N/A N/A 0.61159
37 [anonymised] 2019-01-27 07:23 dodanie zaktualizowanego test.py 0.66416 N/A 0.65883
36 [anonymised] 2019-01-27 06:21 test 0.66416 N/A 0.65883
89 [anonymised] 2019-01-26 20:26 zadanie1 proba testA N/A N/A 0.64888
125 [anonymised] 2019-01-26 19:51 dodanie wyniku dla test-A 0.64904 0.64187 0.63726
455 [anonymised] 2019-01-26 17:54 rozwiązanie zadania "He Said She Said" 0.64904 0.64187 N/A
96 [anonymised] 2019-01-26 17:38 solution1 0.65872 0.64920 0.64647
105 [anonymised] 2019-01-26 17:35 Init_3-zad-1 0.65446 0.65023 0.64519
104 [anonymised] 2019-01-26 17:26 mk 0.65445 N/A 0.64519
103 [anonymised] 2019-01-26 17:12 kd solution 0.65445 N/A 0.64519
102 [anonymised] 2019-01-26 17:04 s402267 - petite-difference-challenge2 0.65446 0.65023 0.64519
127 [anonymised] 2019-01-26 17:04 my solution 0.63105 0.61818 0.63194
202 [anonymised] 2019-01-26 16:48 My brilliant solution 0.57248 0.56905 0.56693
95 [anonymised] 2019-01-26 16:47 xx N/A N/A 0.64647
201 [anonymised] 2019-01-26 16:44 My brilliant solution N/A 0.56905 0.56693
101 [anonymised] 2019-01-26 16:44 my brilliant solution2 0.65445 N/A 0.64519
100 [anonymised] 2019-01-26 16:44 my briliant solution 0.65446 0.65023 0.64519
99 [anonymised] 2019-01-26 16:43 my first soution .py 0.65445 N/A 0.64519
98 [anonymised] 2019-01-26 16:43 my brilliant solution 0.65445 N/A 0.64519
97 [anonymised] 2019-01-26 16:40 my first soution 0.65445 N/A 0.64519
207 [anonymised] 2019-01-26 16:39 DODAŁAM TUTAJ MOJE ZADANIE ZROBIONE N/A N/A 0.56192
454 [anonymised] 2019-01-26 16:31 dssdd N/A N/A N/A
78 [anonymised] 2019-01-26 16:23 dodanie plikow wynikowych oraz skryptu 0.66416 N/A 0.65051
453 [anonymised] 2019-01-26 16:14 My brilliant solution N/A 0.56905 N/A
257 Marcin Gluza 2019-01-03 11:37 brylantowe rozwiazanie2 0.52337 N/A 0.52448
265 Marcin Gluza 2019-01-03 11:31 brylantowe rozwiazanie 0.51297 N/A 0.51836
34 Grzegorz Bąk 2018-12-21 12:40 fixed missing result naive-bayes python scikit-learn 0.67183 0.65628 0.65905
33 Grzegorz Bąk 2018-12-21 12:32 added ML binary NB solution naive-bayes python scikit-learn 0.67183 N/A 0.65905
32 Grzegorz Bąk 2018-12-11 22:32 initial version with training limit on 1m python scikit-learn better-than-no-model-baseline 0.67254 0.66604 0.65983
26 Karol Mazurek 2018-11-29 10:07 LinearSVC dev-0 dev-1 test-A - read submission_info.md python scikit-learn 0.67284 0.66882 0.66309
25 Karol Mazurek 2018-11-27 13:13 LinearSVC dev-0 dev-1 test-A python scikit-learn 0.67284 0.66882 0.66309
24 Karol Mazurek 2018-11-27 12:49 work on files stripped from CR bytes (only locally - commiting only results) 0.67284 N/A 0.66309
194 Karol Mazurek 2018-11-27 10:47 fix len 0.54147 N/A 0.58143
452 Karol Mazurek 2018-11-27 10:44 LinearSVC test solution 0.54147 N/A N/A
451 Złośnik 2018-05-24 13:36 my brilliant solution N/A N/A N/A
219 [anonymised] 2018-05-22 14:09 change dev/test 0.53623 N/A 0.53678
449 [anonymised] 2018-05-22 14:05 bla bal N/A N/A N/A
450 [anonymised] 2018-05-22 13:56 my brilliant solution N/A N/A N/A
448 [anonymised] 2018-05-22 12:59 my brilliant solution naive bayes N/A N/A N/A
182 marcin-jedynski 2018-05-20 22:12 naive 0.99640 0.99542 0.59369
447 [anonymised] 2018-05-20 21:58 'bayes' N/A N/A N/A
181 bee713 2018-05-20 18:11 naive bayes 2 N/A N/A 0.59369
446 bee713 2018-05-20 17:04 naive bayes N/A N/A N/A
61 Wurka 2018-05-20 16:36 Zadanie 7 0.66317 0.64740 0.65388
38 [anonymised] 2018-05-20 15:57 naive bayes 0.67005 N/A 0.65872
60 [anonymised] 2018-05-20 14:38 my solution 0.66317 0.64740 0.65388
218 [anonymised] 2018-05-18 08:55 NaiveBays 0.53623 N/A 0.53678
216 [anonymised] 2018-05-18 08:53 naive_bayes N/A N/A 0.53678
215 [anonymised] 2018-05-18 08:32 my solution -nb N/A N/A 0.53678
214 [anonymised] 2018-05-18 08:29 NaiveBayse N/A N/A 0.53678
217 SmerfPracus 2018-05-18 07:44 naive bayes N/A N/A 0.53678
168 Iza 2018-05-17 15:52 polecenia 0.61123 N/A 0.60185
167 Weronika 2018-05-17 15:51 my brilliant solution 0.61123 N/A 0.60185
166 Weronika 2018-05-17 15:46 my brilliant solution 0.61123 N/A 0.60185
211 Kumajka 2018-05-15 19:19 Naive Bayes solution 0.55897 0.55444 0.54939
179 [anonymised] 2018-05-15 15:17 UMZ homerwork - naive bayes N/A N/A 0.59369
165 Iza 2018-05-15 14:47 my brilliant solution 0.61123 N/A 0.60185
180 [anonymised] 2018-05-15 14:40 Naive Bayes naive-bayes N/A N/A 0.59369
445 Iza 2018-05-11 14:18 my brilliant solution 0.57816 N/A N/A
444 Iza 2018-05-11 13:59 my brilliant solution N/A N/A N/A
443 Iza 2018-05-11 13:51 my brilliant solution N/A N/A N/A
442 Iza 2018-05-11 13:05 my brilliant solution N/A N/A N/A
162 [anonymised] 2018-02-13 21:16 naive-bayes naive-bayes python 0.49760 0.49896 0.60877
163 [anonymised] 2018-02-13 20:14 logistic-regression ready-made python ready-made logistic-regression 0.49760 0.49896 0.60632
345 [anonymised] 2018-02-12 23:45 check 0.49760 0.49896 0.49998
441 [anonymised] 2018-02-12 23:28 check 0.49760 0.49896 N/A
440 [anonymised] 2018-02-12 23:23 check 0.49760 0.49896 N/A
439 [anonymised] 2018-02-12 22:59 check 0.49760 0.49896 N/A
334 [anonymised] 2018-02-06 23:17 logistic-regression ready-made python ready-made logistic-regression 0.49760 0.49896 0.50030
343 [anonymised] 2018-02-06 22:59 logistic-regression ready-made 0.49656 0.49866 0.50000
337 [anonymised] 2018-02-06 22:33 logistic-regression ready-made 0.49656 0.49866 0.50001
354 [anonymised] 2018-02-06 22:06 logistic-regression ready-made 0.49656 0.49866 0.49753
438 [anonymised] 2018-02-06 21:09 logistic-regression ready-made N/A N/A N/A
437 [anonymised] 2018-02-06 21:05 logistic-regression ready-made N/A N/A N/A
436 [anonymised] 2018-02-06 21:01 logistic-regression ready-made N/A N/A N/A
435 [anonymised] 2018-02-06 20:46 logistic-regression ready-made N/A N/A N/A
434 [anonymised] 2018-02-06 20:41 logistic-regression ready-made N/A N/A N/A
325 Domagalsky 2018-01-30 19:34 regr ready-made linear-regression 0.66486 N/A 0.50109
326 Domagalsky 2018-01-30 19:31 naibe bayss naive-bayes 0.66486 N/A 0.50109
433 Domagalsky 2018-01-30 19:26 naibe bays 0.66486 N/A N/A
347 Domagalsky 2018-01-29 16:12 regression ready make logistic-regression 0.66486 N/A 0.49973
291 [anonymised] 2018-01-19 08:25 zadanie 008 z kodem programu v1.3 ready-made logistic-regression 0.51239 0.50649 0.50882
432 [anonymised] 2018-01-18 20:49 zadanie 008 z kodem programu ready-made logistic-regression N/A N/A N/A
175 [anonymised] 2018-01-14 14:00 Add code Task 5. 0.60010 0.59288 0.59835
139 [anonymised] 2018-01-13 20:28 regresja logistyczna ready-made logistic-regression 0.63434 0.61847 0.62400
156 [anonymised] 2018-01-09 12:59 KenLM kenlm 0.53752 N/A 0.61203
431 [anonymised] 2018-01-08 17:50 fix N/A N/A N/A
430 [anonymised] 2018-01-08 17:29 fix N/A N/A N/A
333 [anonymised] 2018-01-07 18:48 nb_ready naive-bayes N/A N/A 0.50063
335 [anonymised] 2018-01-07 17:48 logreg_ready ready-made logistic-regression N/A N/A 0.50025
429 [anonymised] 2018-01-07 10:16 logreg_ready ready-made logistic-regression N/A N/A N/A
428 [anonymised] 2018-01-07 10:13 logreg_ready ready-made logistic-regression N/A N/A N/A
427 [anonymised] 2018-01-07 10:08 solution1 ready-made logistic-regression N/A N/A N/A
94 [anonymised] 2018-01-06 12:30 naive bayes przy uzyciu wektorow czestosci slow naive-bayes 0.65968 0.64131 0.64733
5 kaczla 2018-01-04 19:25 LSTM neural-network 0.70125 0.69679 0.69214
77 kaczla 2018-01-04 19:12 KenLM kenlm 0.67077 0.66102 0.65053
48 s429416 2017-12-30 21:40 Done self-made naive-bayes self-made 0.66918 0.64976 0.65531
47 s429416 2017-12-30 21:29 Poprawilem zgodnosc linii naive-bayes 0.66918 0.64976 0.65531
426 s429416 2017-12-29 08:48 Gotowe naive-bayes N/A N/A N/A
176 [anonymised] 2017-12-23 12:58 naive bayes przegenerowano test-A/out.tsv dla wiekszego slownika czestosci slow naive-bayes N/A N/A 0.59832
303 [anonymised] 2017-12-23 12:54 naive bayes przegenerowano test-A/out.tsv dla wiekszego slownika czestosci slow N/A N/A 0.50727
230 [anonymised] 2017-12-23 12:51 naive bayes poprawiony out.tsv w test-A N/A N/A 0.53115
425 [anonymised] 2017-12-23 12:43 naive bayes naive-bayes N/A N/A N/A
424 [anonymised] 2017-12-20 20:48 correct path N/A N/A N/A
423 [anonymised] 2017-12-20 20:46 logistic-regression ready-made N/A N/A N/A
353 deinonzch 2017-12-20 10:57 my brilliant solution naive-bayes 0.49738 N/A 0.49782
304 Mieszko 2017-12-17 16:50 Naive bayes naive-bayes N/A N/A 0.50726
302 Mieszko 2017-12-17 16:41 Naive bayes N/A N/A 0.50728
200 [anonymised] 2017-12-17 15:44 naive-bayes naive-bayes 0.53840 0.50229 0.57380
422 [anonymised] 2017-12-17 15:33 naive bayes N/A N/A N/A
244 MSz 2017-12-16 13:18 Logistic regression, ready-made ready-made logistic-regression 0.52489 0.52753 0.52919
264 MSz 2017-12-16 12:08 Naive Bayes naive-bayes 0.51505 0.51576 0.52003
407 testowe3 2017-12-14 23:47 test commit 2 naive-bayes self-made N/A N/A N/A
406 testowe3 2017-12-14 23:43 test commit 2 naive-bayes N/A N/A N/A
283 Paweł Skórzewski 2017-12-11 13:39 Word2Vec + logistic regression (fix newlines) python ready-made logistic-regression 0.51816 N/A 0.51148
421 Paweł Skórzewski 2017-12-11 13:10 Word2Vec + logistic regression python ready-made logistic-regression 0.51816 N/A N/A
342 Mieszko 2017-12-06 22:52 TF-IDF - logistic regression N/A N/A 0.50000
420 Mieszko 2017-12-06 22:46 TF-IDF - logistic regression N/A N/A N/A
178 Mieszko 2017-12-06 09:56 Word2Vec on 200k words ready-made logistic-regression N/A N/A 0.59513
186 Mieszko 2017-12-05 23:43 Logistic word2vec N/A N/A 0.58870
208 Mieszko 2017-12-05 23:30 naive bayes with word2Vec naive-bayes N/A N/A 0.55990
198 Mieszko 2017-12-05 23:03 old fashioned word2vec N/A N/A 0.57788
281 Mieszko 2017-12-05 22:56 It being wasted N/A N/A 0.51272
288 Mieszko 2017-12-05 22:37 Bigger word2vec model N/A N/A 0.51033
289 Mieszko 2017-12-05 20:33 Test with bigger train model N/A N/A 0.51004
287 Mieszko 2017-12-05 20:11 Attempt with small train and trained word2Vec models N/A N/A 0.51042
416 marlaz 2017-12-03 22:17 Add working app.py file self-made logistic-regression N/A N/A N/A
352 Weronika 2017-12-03 21:43 05 naive bayes v1 naive-bayes N/A N/A 0.49915
75 kaczla 2017-12-03 20:31 Naive Bayes naive-bayes 0.66092 0.64342 0.65071
313 [anonymised] 2017-12-03 19:32 Logistic regression, ready-made ready-made logistic-regression 0.50216 0.50116 0.50305
411 deinonzch 2017-12-03 18:39 naive bayes naive-bayes N/A N/A N/A
417 deinonzch 2017-12-03 18:34 logistic regresion still M self-made logistic-regression N/A N/A N/A
415 deinonzch 2017-12-03 18:19 logistic regresion self-made logistic-regression N/A N/A N/A
157 MF 2017-12-03 16:29 lm kenlm 0.93282 0.75279 0.61190
270 MF 2017-12-03 16:18 Logistic regression self-made logistic-regression 0.53592 0.51831 0.51619
271 MF 2017-12-03 16:05 test 0.53592 0.51831 0.51619
413 [anonymised] 2017-12-03 14:13 Naive Bayes naive-bayes N/A N/A N/A
414 [anonymised] 2017-12-03 13:48 LogReg ready-made logistic-regression N/A N/A N/A
419 [anonymised] 2017-12-03 13:41 LogReg self-made logistic-regression N/A N/A N/A
174 [anonymised] 2017-12-03 10:29 Task 5. naive-bayes 0.60010 0.59288 0.59835
308 [anonymised] 2017-12-03 01:59 Naive Bayes naive-bayes 0.50217 0.50103 0.50391
409 [anonymised] 2017-12-03 01:13 Naive Bayes 0.50217 N/A N/A
248 MSz 2017-12-01 13:44 Logistic regression, self-made self-made logistic-regression 0.52586 0.52755 0.52832
177 Mieszko 2017-11-30 23:11 word2vec N/A N/A 0.59523
159 MF 2017-11-30 20:23 naive-bayes naive-bayes ready-made 0.62018 0.60296 0.61088
341 [anonymised] 2017-11-30 19:51 Logistic regression, self-made self-made logistic-regression 0.50000 0.50000 0.50000
344 MF 2017-11-30 19:49 LogR readymade ready-made logistic-regression N/A 0.49985 0.49999
410 [anonymised] 2017-11-30 18:01 Logistic regression, self-made self-made logistic-regression 0.50000 0.50000 N/A
324 [anonymised] 2017-11-30 12:37 Naive bayes on text length naive-bayes ready-made 0.53752 N/A 0.50117
408 [anonymised] 2017-11-30 10:34 G Naive-Bayes naive-bayes N/A N/A N/A
199 Mieszko 2017-11-29 14:48 Attempt with word2vec N/A N/A 0.57634
412 [anonymised] 2017-11-26 23:38 LR N/A N/A N/A
329 Weronika 2017-11-26 23:17 04b logistic regression ready-made v3 ready-made logistic-regression N/A N/A 0.50086
418 Weronika 2017-11-26 23:02 04b logistic regression ready-made v2 ready-made logistic-regression N/A N/A N/A
350 [anonymised] 2017-11-26 22:40 LR ready-made logistic-regression N/A N/A 0.49918
164 [anonymised] 2017-11-26 22:35 Task 4. logistic-regression 0.60779 0.60323 0.60482
351 Mieszko 2017-11-26 22:34 logistic regression from sklearn ready-made logistic-regression N/A N/A 0.49915
405 [anonymised] 2017-11-26 21:36 LR test N/A N/A N/A
404 [anonymised] 2017-11-26 21:20 Logistic regression N/A N/A N/A
403 [anonymised] 2017-11-26 21:09 Logistic regression N/A N/A N/A
402 [anonymised] 2017-11-26 20:44 logistic regression test N/A N/A N/A
197 [anonymised] 2017-11-26 20:02 Logistic regression self-made python, correct outs file self-made logistic-regression 0.53950 0.50202 0.57821
62 [anonymised] 2017-11-26 19:46 Logisitc regression, self made, python self-made logistic-regression 0.66180 0.65658 0.65381
400 [anonymised] 2017-11-26 19:29 Logistic regression python N/A N/A N/A
399 deinonzch 2017-11-26 19:20 my bad solution 2 N/A N/A N/A
401 deinonzch 2017-11-26 19:17 my bad solution self-made logistic-regression N/A N/A N/A
284 MF 2017-11-25 19:29 test 0.51683 0.51195 0.51120
398 [anonymised] 2017-11-25 12:20 Selfmade Logical Regression self-made logistic-regression N/A N/A N/A
155 [anonymised] 2017-11-24 19:32 logistic regression ready-made logistic-regression N/A N/A 0.61262
396 [anonymised] 2017-11-23 20:02 Self made Logistic Regression self-made logistic-regression N/A N/A N/A
319 [anonymised] 2017-11-23 18:20 Linear regression on length 0.53752 N/A 0.50162
318 [anonymised] 2017-11-23 18:10 Logical regression on length ready-made logistic-regression 0.53752 N/A 0.50185
328 [anonymised] 2017-11-23 17:53 Logical regression on length 0.53752 N/A 0.50087
327 [anonymised] 2017-11-23 17:30 Logical regression on length 0.53752 N/A 0.50087
397 [anonymised] 2017-11-23 17:29 Logical regression on length 0.53752 N/A N/A
395 [anonymised] 2017-11-23 17:28 Logical regression on length 0.53752 N/A N/A
301 [anonymised] 2017-11-23 15:29 Logical regression files 0.53752 N/A 0.50762
300 [anonymised] 2017-11-20 17:45 Self made ngrams (ruby) self-made n-grams 0.53752 N/A 0.50762
299 [anonymised] 2017-11-20 17:06 Normalized by occurance 0.53752 N/A 0.50762
286 [anonymised] 2017-11-20 16:29 Normalization optimalization 0.53752 N/A 0.51110
64 kaczla 2017-11-20 16:16 Logistic regression self-made logistic-regression 0.66180 0.65658 0.65381
285 [anonymised] 2017-11-20 16:12 Trained on entire train 0.53752 N/A 0.51110
296 [anonymised] 2017-11-20 15:56 Remove trash 0.53752 N/A 0.50793
292 [anonymised] 2017-11-20 15:52 Add build model time counter 0.53752 N/A 0.50793
295 [anonymised] 2017-11-20 15:48 Code improvements 0.53752 N/A 0.50793
294 [anonymised] 2017-11-20 14:08 Add helpers 0.53752 N/A 0.50793
293 [anonymised] 2017-11-19 23:28 Scaled 0.53752 N/A 0.50793
209 [anonymised] 2017-11-19 23:22 Self-made ngrams (ruby) self-made 0.53752 N/A 0.55832
245 [anonymised] 2017-11-19 22:13 Self-made ngrams (ruby) self-made 0.53752 N/A 0.52906
312 [anonymised] 2017-11-19 21:42 Scaled to 1000000 0.53752 N/A 0.50323
311 [anonymised] 2017-11-19 21:36 Self made ngrams (ruby) scaled 1 to 10 0.53752 N/A 0.50373
280 [anonymised] 2017-11-19 21:22 Add normalization (ruby ngrams) 0.53752 N/A 0.51275
297 [anonymised] 2017-11-19 20:53 Self made ngrams (ruby) 0.53752 N/A 0.50780
274 [anonymised] 2017-11-19 20:47 Self-made ngrams (ruby) self-made 0.53752 N/A 0.51515
267 [anonymised] 2017-11-19 20:40 Self made n-grams (ruby) 0.53752 N/A 0.51755
250 [anonymised] 2017-11-19 20:26 Commiting splitter 0.53752 N/A 0.52828
249 [anonymised] 2017-11-19 20:23 Self-made ngrams (ruby) 0.53752 N/A 0.52828
394 [anonymised] 2017-11-19 20:22 Self-made ngrams (ruby) 0.53752 N/A N/A
393 [anonymised] 2017-11-19 16:31 Self n-grams 0.53752 N/A N/A
269 [anonymised] 2017-11-19 01:32 Self made ngrams 0.53752 N/A 0.51746
392 [anonymised] 2017-11-19 01:28 Self made ngrams 0.53752 N/A N/A
389 [anonymised] 2017-11-19 01:27 Self made ngrams 0.53752 N/A N/A
391 [anonymised] 2017-11-19 01:27 Self made ngrams 0.53752 N/A N/A
390 [anonymised] 2017-11-19 01:25 Self made ngrams (ruby) 0.53752 N/A N/A
247 [anonymised] 2017-11-13 17:27 Ruby 0.53752 N/A 0.52835
115 Durson 2017-06-21 17:05 keras, tragiczne parametry neural-network 0.64476 0.64158 0.64147
203 tamazaki 2017-06-12 13:28 prosty model jezyka, unix, vol8 self-made ready-made lm 0.57682 0.56149 0.56657
206 tamazaki 2017-06-12 13:25 prosty model jezyka, unix, vol6 - nowe ratio, test #7 0.57682 0.56149 0.56277
205 tamazaki 2017-06-12 13:19 prosty model jezyka, unix, vol6 - nowe ratio 0.57457 0.56149 0.56277
388 tamazaki 2017-06-12 13:16 prosty model jezyka, unix, vol6 - nowe ratio, test #4 (zakres) 0.57457 0.50106 N/A
387 tamazaki 2017-06-12 13:14 prosty model jezyka, unix, vol6 - nowe ratio, test #3 0.57459 0.50106 N/A
386 tamazaki 2017-06-12 13:12 prosty model jezyka, unix, vol6 - nowe ratio, test #2 0.53362 0.50106 N/A
385 tamazaki 2017-06-12 13:11 prosty model jezyka, unix, vol6 - nowe ratio, test #1 0.53072 0.50106 N/A
384 tamazaki 2017-06-12 13:09 prosty model jezyka, unix, vol6 - nowe ratio 0.46553 0.50106 N/A
383 tamazaki 2017-06-12 12:56 prosty model jezyka, vol5, unix 0.50741 0.50106 N/A
382 tamazaki 2017-06-12 12:46 prosty model jezyka, vol4, unix 0.51963 0.50106 N/A
117 germek 2017-06-12 12:46 Bernoulli naive-bayes bernoulli python self-made N/A N/A 0.64037
381 tamazaki 2017-06-12 12:44 prosty model jezyka, vol3, unix 0.51971 0.50106 N/A
380 tamazaki 2017-06-12 12:41 prosty model jezyka, vol2, unix 0.48608 0.50106 N/A
379 tamazaki 2017-06-12 12:38 prosty model jezyka, vol1, unix 0.48013 0.50106 N/A
120 germek 2017-06-12 09:31 Naive Bayes - Bernoulli naive-bayes bernoulli python self-made N/A N/A 0.63972
378 tamazaki 2017-06-11 23:28 prosty model jezyka v4 0.50977 0.50106 N/A
323 tamazaki 2017-06-11 23:20 prosty model jezyka v3 0.50977 0.50147 0.50155
322 tamazaki 2017-06-11 23:18 prosty model jezyka v2 N/A 0.50147 0.50155
321 tamazaki 2017-06-11 23:13 prosty model jezyka v1 N/A 0.50147 0.50155
19 p/tlen 2017-06-11 19:34 CNN, embeddings with more dimensions 0.68324 0.67808 0.67507
20 p/tlen 2017-06-11 05:26 simple convolutional network neural-network cnn 0.68111 0.67355 0.67189
141 EmEm 2017-06-04 13:45 lm self-made ready-made lm N/A N/A 0.62377
377 EmEm 2017-06-04 13:29 lm N/A N/A N/A
49 zp30615 2017-06-04 11:14 em 0.66730 0.64996 0.65499
22 kaczla 2017-05-29 04:25 LSTM - remove one layer, simple lemmatizer neural-network 0.67703 0.67424 0.67083
110 kaczla 2017-05-27 17:08 LSTM - remove one layer, simple lemmatizer neural-network 0.64777 0.64211 0.64444
2 kaczla 2017-05-25 19:55 LSTM - decrease batch_size, 5 RNNs neural-network 0.70343 0.69886 0.69348
4 kaczla 2017-05-24 18:04 LSTM - decrease batch_size, 3 RNNs neural-network 0.70125 0.69679 0.69214
6 kaczla 2017-05-23 05:32 LSTM - remove one layer, 3 RNNs neural-network 0.70082 0.69814 0.69063
9 kaczla 2017-05-19 04:25 LSTM - remove one layer, decrease batch_size, epoch = 2 neural-network 0.69495 0.69329 0.68734
15 kaczla 2017-05-18 17:54 LSTM - remove one layer, decrease batch_size, epoch = 3 neural-network 0.68841 0.68476 0.68000
18 kaczla 2017-05-16 10:23 LSTM - epoch = 3 neural-network 0.68501 0.68359 0.67617
8 kaczla 2017-05-15 04:27 LSTM - decrease batch_size neural-network 0.69484 0.69201 0.68766
13 kaczla 2017-05-15 04:25 LSTM - decrease batch_size 0.69364 0.69189 0.68599
11 kaczla 2017-05-15 04:21 LSTM - remove one layer neural-network 0.69364 0.69189 0.68599
376 mmalisz 2017-05-14 22:05 Bpe smalltrain 0.56843 0.64794 N/A
349 mmalisz 2017-05-14 22:02 Keras smalltrain 0.56843 0.64794 0.49932
12 kaczla 2017-05-14 15:48 LSTM - remove one layer neural-network 0.69364 0.69189 0.68599
272 siulkilulki 2017-05-11 20:01 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 0.51906 0.51526
40 siulkilulki 2017-05-11 19:54 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 0.65394 0.65709
232 siulkilulki 2017-05-11 16:56 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 0.53295 0.53057
35 siulkilulki 2017-05-11 16:42 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 0.65568 0.65883
124 siulkilulki 2017-05-11 14:40 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 0.63862 0.63857
193 siulkilulki 2017-05-11 13:13 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 0.58698 0.58315
172 EmEm 2017-05-04 19:08 1st try N/A N/A 0.59865
71 siulkilulki 2017-04-28 17:45 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 0.64877 0.65190
109 siulkilulki 2017-04-28 17:26 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 0.64067 0.64489
7 p/tlen 2017-04-25 19:49 5 RNNs combined 0.70079 0.69568 0.69044
3 p/tlen 2017-04-24 05:36 fasttext combined with KenLM 0.71653 0.70503 0.69295
14 p/tlen 2017-04-23 17:02 LSTM (by Nozdi) 0.69433 0.68978 0.68382
10 p/tlen 2017-04-23 10:35 fasttext word 2-ngrams, 10x buckets, character 3-6-ngrams 0.70222 0.69351 0.68632
375 p/tlen 2017-04-23 08:15 fasttext word 2-ngrams, 10x buckets, character 3-6-ngrams 0.70222 N/A N/A
17 p/tlen 2017-04-23 06:53 fasttext word 2-ngrams, 10x buckets, character 3-6-ngrams 0.69423 0.68672 0.67830
16 p/tlen 2017-04-22 20:26 fasttext with word 2-grams and 10x buckets ready-made fasttext 0.69322 0.68578 0.67851
21 p/tlen 2017-04-22 19:42 fasttext with word 2-grams ready-made fasttext 0.68593 0.67887 0.67183
23 p/tlen 2017-04-22 19:34 fasttext (baseline) ready-made fasttext 0.67711 0.66870 0.66623
30 kaczla 2017-04-15 16:18 Vowpal Wabbit vowpal-wabbit ready-made 0.67142 0.66639 0.66109
76 kaczla 2017-04-10 13:26 KenLM ready-made kenlm lm 0.67077 0.66102 0.65053
31 kaczla 2017-04-10 13:07 Vowpal Wabbit vowpal-wabbit ready-made 0.67013 0.66531 0.66036
118 zp30615 2017-04-04 15:19 bayes with simple stemming fix naive-bayes python self-made 0.65368 0.63479 0.64012
204 zp30615 2017-04-04 13:48 bayes with simple stemming 0.56540 0.56040 0.56282
192 zp30615 2017-04-03 21:08 bayes tf-idf (classic) naive-bayes python self-made 0.59090 0.58922 0.58420
56 zp30615 2017-04-03 20:54 dev-0 tf-idf test (big change) 0.54156 0.66063 0.65417
57 zp30615 2017-04-03 20:07 dev-0 tf-idf test (small change) 0.58224 0.66063 0.65417
55 zp30615 2017-04-01 17:45 logistic regression 40 epoch 0.66230 0.66063 0.65417
50 zp30615 2017-04-01 13:38 dev-0 tf-idf test 0.59090 0.66089 0.65494
111 kaczla 2017-03-31 21:52 Vowpal Wabbit vowpal-wabbit ready-made 0.65301 0.64660 0.64337
51 zp30615 2017-03-31 17:30 logistic regression 20 epoch python self-made logistic-regression 0.66397 0.66089 0.65494
63 kaczla 2017-03-27 20:29 Logistic regression python self-made logistic-regression 0.66180 0.65658 0.65381
171 EmEm 2017-03-27 20:11 logistic regression python self-made logistic-regression N/A N/A 0.59865
58 zp30615 2017-03-27 18:29 logistic regression 10 epoch python self-made logistic-regression 0.66355 0.66069 0.65399
123 zp30615 2017-03-27 16:03 logistic regression 1 epoch python self-made logistic-regression 0.65032 0.64632 0.63895
136 germek 2017-03-27 13:21 Regresja python self-made logistic-regression N/A N/A 0.62472
374 germek 2017-03-27 13:20 Regresja N/A N/A N/A
373 germek 2017-03-27 13:19 Regresja N/A N/A N/A
121 germek 2017-03-27 13:14 Regresja N/A N/A 0.63928
129 Mario 2017-03-27 13:07 reg logistyczna 10 epok - shuffle self-made logistic-regression 0.63823 0.63671 0.62985
29 siulkilulki 2017-03-27 11:08 without feature engineering, Adaptive Moment Estimation, 49 epoch. discriminative better than generative python self-made logistic-regression 0.67127 0.66687 0.66120
154 Mario 2017-03-27 10:32 reg logistyczna 10 epok self-made logistic-regression 0.62059 0.61890 0.61450
185 Mario 2017-03-26 23:22 reg logistyczna 1 epoka self-made logistic-regression 0.59625 0.59012 0.58915
184 Mario 2017-03-26 23:17 reg logistyczna 1 epoka, mały zbiór uczący v2 0.66669 0.64823 0.58915
298 Mario 2017-03-26 22:51 reg logistyczna 1 epoka, mały zbiór uczący 0.66669 0.64823 0.50767
39 siulkilulki 2017-03-23 08:23 22 epoch, simple SGD with stupid annealing, need to make better SGD, without feature engineering python self-made logistic-regression 0.66878 0.66422 0.65814
112 zp30615 2017-03-20 19:43 Bernoulli Naive Bayes 1 naive-bayes bernoulli python self-made 0.65483 0.63717 0.64269
160 antystenes 2017-03-20 16:28 Logistic Haskell haskell self-made logistic-regression 0.61675 0.61432 0.61065
187 zp30615 2017-03-16 17:27 bayes + tf_idf 0.59461 0.59014 0.58846
66 zp30615 2017-03-16 12:37 corrected bayes naive-bayes multinomial python self-made 0.66665 0.64844 0.65369
59 siulkilulki 2017-03-15 14:05 sckit-learn naive bayes naive-bayes python ready-made scikit-learn 0.66680 0.64842 0.65394
82 antystenes 2017-03-13 08:36 TurboHaskell 2010 v2 0.66435 0.70540 0.65029
46 antystenes 2017-03-11 15:54 TurboHaskell 2010 naive-bayes multinomial haskell self-made 0.66912 0.64996 0.65531
196 Durson 2017-03-11 03:25 Test 0.58665 0.58153 0.57822
183 Durson 2017-03-11 02:59 Test 0.59857 0.59280 0.58933
161 Durson 2017-03-11 02:15 Test 0.62323 0.61270 0.60889
189 Durson 2017-03-11 01:16 Test 0.59528 0.59049 0.58699
151 Durson 2017-03-11 00:44 Test 0.63650 0.62513 0.62066
152 Durson 2017-03-11 00:26 Test 0.63455 0.62364 0.61931
153 Durson 2017-03-11 00:19 Test 0.63425 0.62240 0.61862
372 Durson 2017-03-10 23:48 Test N/A 0.52997 N/A
84 Durson 2017-03-09 17:44 Test 0.66364 0.64468 0.64945
80 Durson 2017-03-09 17:38 Naive Bayes naive-bayes multinomial self-made perl 0.66521 0.64534 0.65043
130 Durson 2017-03-09 17:18 Test 0.64469 0.62934 0.62802
135 Durson 2017-03-09 17:03 Yolo 0.64314 0.62835 0.62709
142 Durson 2017-03-09 16:23 Test 0.63938 0.62525 0.62369
134 Durson 2017-03-09 16:16 Test 0.64379 0.62851 0.62740
132 Durson 2017-03-09 15:51 Test 0.64366 0.62845 0.62752
133 Durson 2017-03-09 15:24 Test 0.64358 0.62858 0.62751
131 Durson 2017-03-09 14:53 Yolo 0.64420 0.62867 0.62784
220 Durson 2017-03-09 14:33 Test 0.54233 0.53734 0.53638
83 antystenes 2017-03-07 02:31 Haskell na resorach 0.66344 0.64638 0.64971
53 mmalisz 2017-03-02 23:49 I can see that I'll have to teach you how to be villains! naive-bayes multinomial self-made regexp lisp 0.56843 0.64794 0.65479
65 mmalisz 2017-03-02 23:35 Throw it at him, not me! 0.56843 0.64794 0.65375
54 mmalisz 2017-03-02 23:16 Back to old corpora 0.56843 0.64794 0.65450
81 mmalisz 2017-03-02 23:00 Change of preprocessing 0.56843 0.64794 0.65031
85 mmalisz 2017-03-02 21:48 Próba raz dwa czy 0.56843 0.64794 0.64935
143 Durson 2017-03-02 13:01 Test N/A N/A 0.62362
315 Durson 2017-03-02 12:22 Yolo N/A N/A 0.50288
309 Durson 2017-03-02 12:11 Yolo N/A N/A 0.50381
356 Durson 2017-03-02 12:08 Yolo N/A N/A 0.00000
68 mmalisz 2017-03-02 11:15 Now look at this net that I just found; when I say go... 0.56843 0.64794 0.65331
67 mmalisz 2017-03-02 10:54 Now look at this net that I just found 0.56843 N/A 0.65331
355 mmalisz 2017-03-02 10:44 Now look at this net 0.56843 N/A 0.34669
314 Durson 2017-03-02 08:19 Yolo N/A N/A 0.50288
310 Durson 2017-03-02 08:10 Yolo N/A N/A 0.50374
346 zp30615 2017-03-01 11:40 bayes3 naive-bayes multinomial python self-made 0.50157 0.50408 0.49981
348 zp30615 2017-03-01 11:04 bayes2 0.49982 0.50048 0.49941
140 antystenes 2017-03-01 07:13 Haskell 0.63596 0.61912 0.62383
122 germek 2017-02-28 23:51 something is no yes :X naive-bayes multinomial python self-made N/A N/A 0.63928
371 germek 2017-02-28 22:42 test N/A N/A N/A
370 germek 2017-02-28 21:47 something is no yes :X N/A N/A N/A
369 zp30615 2017-02-28 21:37 bayes1 N/A N/A N/A
330 zp30615 2017-02-28 21:13 bayes solution1 0.50033 0.50155 0.50085
73 siulkilulki 2017-02-28 19:32 naiwen bajesen, changed equation 0.66582 0.64740 0.65173
69 siulkilulki 2017-02-28 19:19 naiwen bajesen naive-bayes multinomial python self-made 0.66600 0.64745 0.65224
74 kaczla 2017-02-28 18:33 Rozwiązanie naive-bayes multinomial python self-made 0.66092 0.64342 0.65071
52 Mario 2017-02-28 17:06 Rozwiązanie 3 naive-bayes multinomial self-made java 0.66669 0.64823 0.65482
336 Mario 2017-02-28 16:44 Rozwiązanie 2 N/A N/A 0.50006
169 antystenes 2017-02-28 15:35 Swag 0.61095 0.59919 0.60005
144 Durson 2017-02-28 15:04 Yolo N/A N/A 0.62326
145 Durson 2017-02-28 10:44 Yolo N/A N/A 0.62268
368 Mario 2017-02-27 23:17 Rozwiązanie 1 N/A N/A N/A
231 Durson 2017-02-27 17:57 First N/A N/A 0.53074
222 Durson 2017-02-27 17:44 First N/A N/A 0.53376
261 Durson 2017-02-27 17:31 First N/A N/A 0.52212
332 [anonymised] 2017-02-27 17:22 moje rozwiazanie 1 stupid python self-made 0.50123 N/A 0.50068
331 zp30615 2017-02-27 16:23 regexPro stupid python self-made regexp 0.50033 0.50155 0.50085
320 tamazaki 2017-02-27 16:21 test stupid python self-made regexp 0.50241 0.50147 0.50155
282 antystenes 2017-02-24 08:31 Simple regexp solution stupid self-made regexp 0.52190 0.51948 0.51246
262 [anonymised] 2017-02-21 16:58 test simple solution 0.52869 0.53085 0.52200
1 p/tlen 2017-01-26 10:08 KenLM + Vowpal Wabbit vowpal-wabbit 0.71473 0.70513 0.69379
113 Domagalsky 2017-01-08 20:31 Punct split v2 kenlm 0.66486 0.65639 0.64260
138 Domagalsky 2017-01-08 15:16 KenLM punctuation.split 0.64351 0.63973 0.62437
88 Mieszko 2016-12-27 14:04 Train LM 3 grams & tokenize 0.99425 0.63660 0.64909
147 Mieszko 2016-12-27 14:00 LM 4grams female 0.99425 0.63660 0.62213
227 Mieszko 2016-12-27 13:55 Train LM improvement 0.99425 0.63660 0.53150
195 Mieszko 2016-12-27 13:46 Train LM improvement 0.99425 0.63660 0.58043
45 Mieszko 2016-12-27 10:22 Kenml devs & train LM & remove punct kenlm 0.99425 0.63660 0.65591
44 Mieszko 2016-12-27 10:17 Kenml devs & train LM 0.99425 0.63660 0.65591
93 Mieszko 2016-12-27 01:21 2 w nocy -> wystarczy 0.98007 0.97880 0.64758
221 Mieszko 2016-12-27 01:16 2 w nocy -> wystarczy 0.98007 0.97880 0.53478
128 Mieszko 2016-12-27 01:09 kenml & dict v2 0.98007 0.97880 0.63106
173 Mieszko 2016-12-27 00:57 kenml & dict 0.98007 0.97880 0.59847
87 Mieszko 2016-12-27 00:43 kenml train LM 0.98007 0.97880 0.64909
92 Mieszko 2016-12-27 00:39 kenml v4 0.98007 0.97880 0.64758
91 Mieszko 2016-12-27 00:32 Kenml v3 0.98007 0.97880 0.64758
146 Mieszko 2016-12-27 00:19 Kenml v2 0.98007 0.97880 0.62256
367 Mieszko 2016-12-27 00:04 Kenml v2 0.98007 0.97880 N/A
366 Mieszko 2016-12-26 23:58 Kenml v2 0.98007 0.97880 N/A
365 Mieszko 2016-12-26 23:54 Kenml v2 0.98007 0.97880 N/A
149 Mieszko 2016-12-26 23:25 Kenml v1 0.98007 0.97880 0.62129
307 RafciX 2016-12-07 09:31 sama 0.51523 N/A 0.50463
277 RafciX 2016-12-07 09:24 v2 0.51523 N/A 0.51408
364 PioBec 2016-12-05 22:38 extra rules, information about each rule accuracy 0.50095 N/A N/A
363 PioBec 2016-12-05 21:59 silly mistake in adding stuff twice to out 0.50091 N/A N/A
362 PioBec 2016-12-05 21:50 Dydlojn zaliczony? N/A N/A N/A
273 [anonymised] 2016-12-05 00:26 Womendict ver.3 0.51991 N/A 0.51516
275 [anonymised] 2016-12-05 00:03 Womendict ver.2 0.52001 N/A 0.51494
279 [anonymised] 2016-12-04 23:43 Womendict ver.2 0.51547 N/A 0.51278
278 [anonymised] 2016-12-03 23:50 First submission - Womendict 0.51460 N/A 0.51278
361 [anonymised] 2016-12-03 23:35 First submission - Womendict 0.51460 N/A N/A
360 [anonymised] 2016-12-03 23:32 First submission - Womendict 0.51460 N/A N/A
268 KamilTrabka 2016-12-03 23:06 proste rozwiazanie N/A 0.51687 0.51754
359 [anonymised] 2016-12-03 18:15 First submission - Womendict N/A N/A N/A
316 KamilTrabka 2016-12-01 12:40 p3 0.49753 N/A 0.50251
317 KamilTrabka 2016-12-01 12:36 2ga proba 0.50351 N/A 0.50251
357 KamilTrabka 2016-12-01 12:29 pp N/A N/A N/A
79 marcin-jedynski 2016-12-01 02:45 kenlm first attempt 0.99640 0.99542 0.65047
254 Domagalsky 2016-11-30 14:38 Poprawki w ./runD.py 0.52735 0.52362 0.52521
253 Domagalsky 2016-11-30 13:49 Push z plikami - wersja słownikowa 0.52735 0.52362 0.52521
252 Domagalsky 2016-11-30 13:46 Test plikow 0.52735 0.52362 0.52521
148 Domagalsky 2016-11-30 10:32 KenLM z Train'a* 0.64377 0.52363 0.62182
256 Domagalsky 2016-11-30 10:30 KenLM z Train'a 0.64377 0.52363 0.52520
225 Mieszko 2016-11-30 10:28 merged v2 0.54357 N/A 0.53326
224 Mieszko 2016-11-30 10:27 merged v1 N/A N/A 0.53326
223 Mieszko 2016-11-30 10:26 merged Mieszko & Maciej solution N/A N/A 0.53326
276 RafciX 2016-11-30 09:28 dict v1 0.51523 N/A 0.51408
255 Domagalsky 2016-11-30 09:26 Słownik na Trainie 0.52735 0.52363 0.52520
246 [anonymised] 2016-11-28 16:23 Women - interpunction 0.53752 N/A 0.52835
191 Domagalsky 2016-11-28 08:57 KenLM 3gram 0.98726 0.98495 0.58469
190 Domagalsky 2016-11-28 07:57 KenLM 1st Try 0.98843 0.98664 0.58520
212 Domagalsky 2016-11-26 14:53 Best On test-A** 0.61496 0.53644 0.53855
235 Domagalsky 2016-11-26 14:48 Best on test-A 0.77005 0.73899 0.53038
234 Domagalsky 2016-11-26 14:35 Best on devs 0.77007 0.73899 0.53038
251 Domagalsky 2016-11-26 14:19 _ 0.63512 0.61667 0.52541
229 Domagalsky 2016-11-26 13:25 El Dictioannte finallo 0.67131 0.64660 0.53131
233 Domagalsky 2016-11-26 12:40 Dic v4 cleaning + tr improve 0.77005 0.73899 0.53040
213 Domagalsky 2016-11-26 10:50 Dic v3 0.61498 0.53645 0.53853
243 Domagalsky 2016-11-25 19:33 Dictionary version over 9000 small cleaning 0.60219 0.53713 0.52968
242 Domagalsky 2016-11-25 19:02 Dictionary version over 9000 dev-1 0.59657 0.52953 0.52968
241 Domagalsky 2016-11-25 18:58 Dictionary version over 9000 0.59657 N/A 0.52968
260 [anonymised] 2016-11-23 20:08 Women dictionary v3 0.53190 N/A 0.52321
263 [anonymised] 2016-11-23 19:56 Women dictionary v2 0.52793 N/A 0.52035
266 [anonymised] 2016-11-23 19:47 Women dictionary 0.52408 N/A 0.51827
290 [anonymised] 2016-11-23 19:31 Only men v3 0.51677 N/A 0.50993
305 [anonymised] 2016-11-23 17:41 Only men - bigger dictionary 0.51156 N/A 0.50724
358 RafciX 2016-11-23 14:06 words v1 N/A N/A N/A
340 [anonymised] 2016-11-22 23:15 Dictionary - only women 0.50000 N/A 0.50000
306 [anonymised] 2016-11-22 23:07 "First attempt - dictionary" 0.50867 N/A 0.50562
339 [anonymised] 2016-11-22 21:10 test submition (all F) 0.50000 N/A 0.50000
228 Mieszko 2016-11-22 18:29 female + male dict 0.53915 N/A 0.53150
238 Mieszko 2016-11-22 18:14 male + female dict N/A N/A 0.53001
237 Mieszko 2016-11-20 17:14 add swears 0.53714 N/A 0.53001
236 Mieszko 2016-11-20 17:07 add swears N/A N/A 0.53001
226 Mieszko 2016-11-20 09:42 dict v4 0.54134 N/A 0.53208
240 Mieszko 2016-11-19 23:15 dict v3 0.53816 N/A 0.52971
239 Mieszko 2016-11-19 22:18 improve dict v2 0.53785 N/A 0.52971
259 Mieszko 2016-11-19 22:13 improve dict 0.53785 N/A 0.52399
258 Mieszko 2016-11-19 20:07 Dictionary approach 0.52699 N/A 0.52399
338 p/tlen 2016-11-15 09:29 trivial baseline (only female) 0.50000 0.50000 0.50000