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