"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
p/tlen 2017-04-25 19:49 5 RNNs combined 0.70078797500619 0.695682157771733 0.690442585686907
p/tlen 2017-04-24 05:36 fasttext combined with KenLM 0.716525627394148 0.705030458603119 0.692945965621239
p/tlen 2017-04-23 17:02 LSTM (by Nozdi) 0.694328327774298 0.689782000689629 0.68382385713649
p/tlen 2017-04-23 10:35 fasttext word 2-ngrams, 10x buckets, character 3-6-ngrams 0.702215360414816 0.693511104299963 0.686319808643718
p/tlen 2017-04-23 08:15 fasttext word 2-ngrams, 10x buckets, character 3-6-ngrams 0.702215360414816 N/A N/A
p/tlen 2017-04-23 06:53 fasttext word 2-ngrams, 10x buckets, character 3-6-ngrams 0.694226371673682 0.686716984023601 0.67830453579759
p/tlen 2017-04-22 20:26 fasttext with word 2-grams and 10x buckets fasttext ready-made 0.693221375824752 0.685784708121017 0.678512531756526
p/tlen 2017-04-22 19:42 fasttext with word 2-grams fasttext ready-made 0.6859315146307 0.678869264268291 0.67183437578927
p/tlen 2017-04-22 19:34 fasttext (baseline) fasttext ready-made 0.677105029348792 0.66869724020791 0.666233341752217
kaczla 2017-04-15 16:18 Vowpal Wabbit vowpal-wabbit ready-made 0.671417335450136 0.666385706805614 0.661092870195665
kaczla 2017-04-10 13:26 KenLM lm kenlm ready-made 0.670769185953362 0.661015542188677 0.650529646852575
kaczla 2017-04-10 13:07 Vowpal Wabbit vowpal-wabbit ready-made 0.670128319035204 0.665312950972504 0.66036488433939
zp30615 2017-04-04 15:19 bayes with simple stemming fix python self-made naive-bayes 0.653684256521549 0.634790493339974 0.640122420478688
zp30615 2017-04-04 13:48 bayes with simple stemming 0.565397555966617 0.560399984674917 0.562822208025673
zp30615 2017-04-03 21:08 bayes tf-idf (classic) python self-made naive-bayes 0.590901146277874 0.589223912238356 0.584201221233416
zp30615 2017-04-03 20:54 dev-0 tf-idf test (big change) 0.541561676158294 0.660626029654036 0.654169576133949
zp30615 2017-04-03 20:07 dev-0 tf-idf test (small change) 0.58224216030412 0.660626029654036 0.654169576133949
zp30615 2017-04-01 17:45 logistic regression 40 epoch 0.66229954702361 0.660626029654036 0.654169576133949
zp30615 2017-04-01 13:38 dev-0 tf-idf test 0.590901146277874 0.660894218612314 0.654942132552853
kaczla 2017-03-31 21:52 Vowpal Wabbit vowpal-wabbit ready-made 0.653006976710314 0.64659719295557 0.643368643123505
zp30615 2017-03-31 17:30 logistic regression 20 epoch logistic-regression python self-made 0.66397454010516 0.660894218612314 0.654942132552853
kaczla 2017-03-27 20:29 Logistic regression logistic-regression self-made python 0.66180433167776 0.656577653474324 0.653805583205812
EmEm 2017-03-27 20:11 logistic regression python self-made logistic-regression N/A N/A 0.598649511952339
zp30615 2017-03-27 18:29 logistic regression 10 epoch logistic-regression self-made python 0.663552150545465 0.660689884167912 0.653991293883433
zp30615 2017-03-27 16:03 logistic regression 1 epoch logistic-regression self-made python 0.650319705201218 0.646316233094517 0.638948728996122
germek 2017-03-27 13:21 Regresja logistic-regression python self-made N/A N/A 0.624723291090345
germek 2017-03-27 13:20 Regresja N/A N/A N/A
germek 2017-03-27 13:19 Regresja N/A N/A N/A
germek 2017-03-27 13:14 Regresja N/A N/A 0.63928300821584
Mario 2017-03-27 13:07 reg logistyczna 10 epok - shuffle logistic-regression self-made 0.638230624699594 0.636706128756242 0.629848905792687
siulkilulki 2017-03-27 11:08 without feature engineering, Adaptive Moment Estimation, 49 epoch. discriminative better than generative self-made python logistic-regression 0.671271683877827 0.666871001111069 0.661204296602237
Mario 2017-03-27 10:32 reg logistyczna 10 epok logistic-regression self-made 0.620592219293007 0.618903490287728 0.614501775394078
Mario 2017-03-26 23:22 reg logistyczna 1 epoka logistic-regression self-made 0.596246558981604 0.590117875432614 0.589148553685243
Mario 2017-03-26 23:17 reg logistyczna 1 epoka, mały zbiór uczący v2 0.666690941928718 0.648231868510785 0.589148553685243
Mario 2017-03-26 22:51 reg logistyczna 1 epoka, mały zbiór uczący 0.666690941928718 0.648231868510785 0.507673565199305
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 0.668781041991348 0.664221038785232 0.658136356207937
zp30615 2017-03-20 19:43 Bernoulli Naive Bayes 1 naive-bayes bernoulli python self-made 0.654834903942788 0.637165881256146 0.642685227829859
antystenes 2017-03-20 16:28 Logistic Haskell haskell self-made logistic-regression 0.616754300362672 0.614318736191461 0.610653850153768
zp30615 2017-03-16 17:27 bayes + tf_idf 0.594607978793131 0.590137031786777 0.588457709964492
zp30615 2017-03-16 12:37 corrected bayes naive-bayes python self-made multinomial 0.666647246457026 0.648442588406574 0.653694156799239
siulkilulki 2017-03-15 14:05 sckit-learn naive bayes ready-made python naive-bayes scikit-learn 0.66680018060795 0.648417046601024 0.653939294893699
antystenes 2017-03-13 08:36 TurboHaskell 2010 v2 0.664353234193163 0.705400814783597 0.65029193718522
antystenes 2017-03-11 15:54 TurboHaskell 2010 haskell self-made naive-bayes 0.669116040607658 0.649955940385426 0.655313553908096
Durson 2017-03-11 03:25 Test 0.586648120366459 0.581529443316348 0.578221337414016
Durson 2017-03-11 02:59 Test 0.598569701559928 0.592799765015389 0.589334264362864
Durson 2017-03-11 02:15 Test 0.623228512751795 0.612696831539022 0.60889331292992
Durson 2017-03-11 01:16 Test 0.595277976025751 0.59049461706448 0.586994309824838
Durson 2017-03-11 00:44 Test 0.636504653567735 0.625129305390598 0.620659941463994
Durson 2017-03-11 00:26 Test 0.634545639920183 0.623641495217297 0.619307967730913
Durson 2017-03-11 00:19 Test 0.63424705419695 0.622402717648111 0.618617124010162
Durson 2017-03-10 23:48 Test N/A 0.529966923361812 N/A
Durson 2017-03-09 17:44 Test 0.66363954148885 0.644681557539302 0.649445096495268
Durson 2017-03-09 17:38 Naive Bayes perl self-made naive-bayes multinomial 0.665212578469785 0.645339259032221 0.650433077300212
Durson 2017-03-09 17:18 Test 0.644690271931486 0.629337317854999 0.628021512724896
Durson 2017-03-09 17:03 Yolo 0.643139082686398 0.628347572889928 0.627085530909685
Durson 2017-03-09 16:23 Test 0.639381272120833 0.625250628966962 0.623690739722771
Durson 2017-03-09 16:16 Test 0.643787232183171 0.628507209174617 0.627404953275194
Durson 2017-03-09 15:51 Test 0.643656145768094 0.628449740112129 0.627523808108871
Durson 2017-03-09 15:24 Test 0.643576037403324 0.62857744913988 0.627508951254661
Durson 2017-03-09 14:53 Yolo 0.644195056585636 0.628666845459306 0.627843230474379
Durson 2017-03-09 14:33 Test 0.542326346912915 0.537335734263055 0.53638443595953
antystenes 2017-03-07 02:31 Haskell na resorach 0.663435629287618 0.646380087608393 0.649705091443938
mmalisz 2017-03-02 23:49 I can see that I'll have to teach you how to be villains! naive-bayes self-made lisp regexp 0.568434391249254 0.647944523198345 0.654786135583652
mmalisz 2017-03-02 23:35 Throw it at him, not me! 0.568434391249254 0.647944523198345 0.653753584216078
mmalisz 2017-03-02 23:16 Back to old corpora 0.568434391249254 0.647944523198345 0.654503855353667
mmalisz 2017-03-02 23:00 Change of preprocessing 0.568434391249254 0.647944523198345 0.65030679403943
mmalisz 2017-03-02 21:48 Próba raz dwa czy 0.568434391249254 0.647944523198345 0.649348526942905
Durson 2017-03-02 13:01 Test N/A N/A 0.623623883878828
Durson 2017-03-02 12:22 Yolo N/A N/A 0.50288222971668
Durson 2017-03-02 12:11 Yolo N/A N/A 0.503810783104785
Durson 2017-03-02 12:08 Yolo N/A N/A 0.0
mmalisz 2017-03-02 11:15 Now look at this net that I just found; when I say go... 0.568434391249254 0.647944523198345 0.653307878589787
mmalisz 2017-03-02 10:54 Now look at this net that I just found 0.568434391249254 N/A 0.653307878589787
mmalisz 2017-03-02 10:44 Now look at this net 0.568434391249254 N/A 0.346692121410213
Durson 2017-03-02 08:19 Yolo N/A N/A 0.50288222971668
Durson 2017-03-02 08:10 Yolo N/A N/A 0.503736498833737
zp30615 2017-03-01 11:40 bayes3 naive-bayes self-made python multinomial 0.501565754402319 0.50408030343665 0.499806860895274
zp30615 2017-03-01 11:04 bayes2 0.499817935534614 0.500478908854067 0.499413154258717
antystenes 2017-03-01 07:13 Haskell 0.635958460171578 0.619120595634905 0.623831879837763
germek 2017-02-28 23:51 something is no yes :X naive-bayes python self-made N/A N/A 0.63928300821584
germek 2017-02-28 22:42 test N/A N/A N/A
germek 2017-02-28 21:47 something is no yes :X N/A N/A N/A
zp30615 2017-02-28 21:37 bayes1 N/A N/A N/A
zp30615 2017-02-28 21:13 bayes solution1 0.50033499861631 0.501551664687177 0.500846840689952
siulkilulki 2017-02-28 19:32 naiwen bajesen, changed equation 0.665817032494866 0.647395374379015 0.651725623616455
siulkilulki 2017-02-28 19:19 naiwen bajesen naive-bayes self-made python 0.665999096960252 0.647452843441503 0.65223818508669
kaczla 2017-02-28 18:33 Rozwiązanie python naive-bayes self-made 0.660923139665293 0.643423623615953 0.650707929103092
Mario 2017-02-28 17:06 Rozwiązanie 3 naive-bayes self-made java 0.666690941928718 0.648231868510785 0.654823277719176
Mario 2017-02-28 16:44 Rozwiązanie 2 N/A N/A 0.500059427416839
antystenes 2017-02-28 15:35 Swag 0.61095008520617 0.599185216402948 0.600053484675155
Durson 2017-02-28 15:04 Yolo N/A N/A 0.62325989095069
Durson 2017-02-28 10:44 Yolo N/A N/A 0.622680473636512
Mario 2017-02-27 23:17 Rozwiązanie 1 N/A N/A N/A
Durson 2017-02-27 17:57 First N/A N/A 0.530738831359848
Durson 2017-02-27 17:44 First N/A N/A 0.53376220119152
Durson 2017-02-27 17:31 First N/A N/A 0.522121855918228
[anonymised] 2017-02-27 17:22 moje rozwiazanie 1 python self-made stupid 0.501230755786009 N/A 0.500683415293646
zp30615 2017-02-27 16:23 regexPro stupid regexp self-made python 0.50033499861631 0.501551664687177 0.500846840689952
tamazaki 2017-02-27 16:21 test regexp self-made python stupid 0.502410533521709 0.501468653819139 0.501545112837808
antystenes 2017-02-24 08:31 Simple regexp solution 0.521898713896616 0.519475626732054 0.512457472254825
[anonymised] 2017-02-21 16:58 test simple solution 0.528693359744818 0.530848115653295 0.52200300108455
p/tlen 2017-01-26 10:08 KenLM + Vowpal Wabbit vowpal-wabbit 0.71473411305475 0.70513262582532 0.693785377884087
Domagalsky 2017-01-08 20:31 Punct split v2 0.664855732117628 0.656392475384085 0.642596086704601
Domagalsky 2017-01-08 15:16 KenLM punctuation.split 0.643510494195785 0.639726447262557 0.624366726589312
Mieszko 2016-12-27 14:04 Train LM 3 grams & tokenize 0.994246762893805 0.636597576082653 0.649088531994236
Mieszko 2016-12-27 14:00 LM 4grams female 0.994246762893805 0.636597576082653 0.622130770030754
Mieszko 2016-12-27 13:55 Train LM improvement 0.994246762893805 0.636597576082653 0.531503959351647
Mieszko 2016-12-27 13:46 Train LM improvement 0.994246762893805 0.636597576082653 0.580427580264155
Mieszko 2016-12-27 10:22 Kenml devs & train LM & remove punct 0.994246762893805 0.636597576082653 0.655907828076483
Mieszko 2016-12-27 10:17 Kenml devs & train LM 0.994246762893805 0.636597576082653 0.655907828076483
Mieszko 2016-12-27 01:21 2 w nocy -> wystarczy 0.980067582329551 0.978800301393306 0.647580561291952
Mieszko 2016-12-27 01:16 2 w nocy -> wystarczy 0.980067582329551 0.978800301393306 0.534779895704883
Mieszko 2016-12-27 01:09 kenml & dict v2 0.980067582329551 0.978800301393306 0.631059739410777
Mieszko 2016-12-27 00:57 kenml & dict 0.980067582329551 0.978800301393306 0.598471229701823
Mieszko 2016-12-27 00:43 kenml train LM 0.980067582329551 0.978800301393306 0.649088531994236
Mieszko 2016-12-27 00:39 kenml v4 0.980067582329551 0.978800301393306 0.647580561291952
Mieszko 2016-12-27 00:32 Kenml v3 0.980067582329551 0.978800301393306 0.647580561291952
Mieszko 2016-12-27 00:19 Kenml v2 0.980067582329551 0.978800301393306 0.622561618802835
Mieszko 2016-12-27 00:04 Kenml v2 0.980067582329551 0.978800301393306 N/A
Mieszko 2016-12-26 23:58 Kenml v2 0.980067582329551 0.978800301393306 N/A
Mieszko 2016-12-26 23:54 Kenml v2 0.980067582329551 0.978800301393306 N/A
Mieszko 2016-12-26 23:25 Kenml v1 0.980067582329551 0.978800301393306 0.621291357767906
RafciX 2016-12-07 09:31 sama 0.515227871884877 N/A 0.504627910086318
RafciX 2016-12-07 09:24 v2 0.515227871884877 N/A 0.514076869363681
PioBec 2016-12-05 22:38 extra rules, information about each rule accuracy 0.500946735220007 N/A N/A
PioBec 2016-12-05 21:59 silly mistake in adding stuff twice to out 0.50091032232693 N/A N/A
PioBec 2016-12-05 21:50 Dydlojn zaliczony? N/A N/A N/A
Dominik Ziętkowski 2016-12-05 00:26 Womendict ver.3 0.519910569934602 N/A 0.515161419720988
Dominik Ziętkowski 2016-12-05 00:03 Womendict ver.2 0.520005243456603 N/A 0.514938566907843
Dominik Ziętkowski 2016-12-04 23:43 Womendict ver.2 0.515468196979186 N/A 0.512776894620333
Dominik Ziętkowski 2016-12-03 23:50 First submission - Womendict 0.514601570123949 N/A 0.512776894620333
Dominik Ziętkowski 2016-12-03 23:35 First submission - Womendict 0.514601570123949 N/A N/A
Dominik Ziętkowski 2016-12-03 23:32 First submission - Womendict 0.514601570123949 N/A N/A
KamilTrabka 2016-12-03 23:06 proste rozwiazanie N/A 0.51687036256593 0.517538516394539
Dominik Ziętkowski 2016-12-03 18:15 First submission - Womendict N/A N/A N/A
KamilTrabka 2016-12-01 12:40 p3 0.497531205849367 N/A 0.502510808361438
KamilTrabka 2016-12-01 12:36 2ga proba 0.50351020289264 N/A 0.502510808361438
KamilTrabka 2016-12-01 12:29 pp N/A N/A N/A
Martin 2016-12-01 02:45 kenlm first attempt 0.996395123585359 0.995421631355121 0.650470219435737
Domagalsky 2016-11-30 14:38 Poprawki w ./runD.py 0.527346082700963 0.523619784682579 0.525212081593843
Domagalsky 2016-11-30 13:49 Push z plikami - wersja słownikowa 0.527346082700963 0.523619784682579 0.525212081593843
Domagalsky 2016-11-30 13:46 Test plikow 0.527346082700963 0.523619784682579 0.525212081593843
Domagalsky 2016-11-30 10:32 KenLM z Train'a* 0.643765384447325 0.523626170133967 0.62181877609235
Domagalsky 2016-11-30 10:30 KenLM z Train'a 0.643765384447325 0.523626170133967 0.525204653166738
Mieszko 2016-11-30 10:28 merged v2 0.543571667856155 N/A 0.53325706814839
Mieszko 2016-11-30 10:27 merged v1 N/A N/A 0.53325706814839
Mieszko 2016-11-30 10:26 merged Mieszko & Maciej solution N/A N/A 0.53325706814839
RafciX 2016-11-30 09:28 dict v1 0.515227871884877 N/A 0.514076869363681
Domagalsky 2016-11-30 09:26 Słownik na Trainie 0.527346082700963 0.523626170133967 0.525204653166738
Maciej 2016-11-28 16:23 Women - interpunction 0.537519845026727 N/A 0.528346877832088
Domagalsky 2016-11-28 08:57 KenLM 3gram 0.987255487422987 0.984949491079524 0.584691497422336
Domagalsky 2016-11-28 07:57 KenLM 1st Try 0.988427982580072 0.98663525024584 0.58520405889257
Domagalsky 2016-11-26 14:53 Best On test-A** 0.614962786023275 0.536441771068797 0.53854610824704
Domagalsky 2016-11-26 14:48 Best on test-A 0.770052580217603 0.738994674533543 0.530382266858815
Domagalsky 2016-11-26 14:35 Best on devs 0.770067145374834 0.738994674533543 0.530382266858815
Domagalsky 2016-11-26 14:19 _ 0.635120963630802 0.616674967753471 0.525412649125674
Domagalsky 2016-11-26 13:25 El Dictioannte finallo 0.671308096770905 0.646603578406958 0.531310820246921
Domagalsky 2016-11-26 12:40 Dic v4 cleaning + tr improve 0.770052580217603 0.738994674533543 0.530397123713025
Domagalsky 2016-11-26 10:50 Dic v3 0.614984633759121 0.536454541971572 0.53853125139283
Domagalsky 2016-11-25 19:33 Dictionary version over 9000 small cleaning 0.6021891431318 0.537125014367266 0.52968399471096
Domagalsky 2016-11-25 19:02 Dictionary version over 9000 dev-1 0.596574275019299 0.529532712667458 0.52968399471096
Domagalsky 2016-11-25 18:58 Dictionary version over 9000 0.596574275019299 N/A 0.52968399471096
Maciej 2016-11-23 20:08 Women dictionary v3 0.53189769433561 N/A 0.52321383470264
Maciej 2016-11-23 19:56 Women dictionary v2 0.527928688990198 N/A 0.520353890267275
Maciej 2016-11-23 19:47 Women dictionary 0.524083487481247 N/A 0.518266502250813
Maciej 2016-11-23 19:31 Only men v3 0.516771778551349 N/A 0.509931807039178
Maciej 2016-11-23 17:41 Only men - bigger dictionary 0.511557452262697 N/A 0.507235288000119
RafciX 2016-11-23 14:06 words v1 N/A N/A N/A
Maciej 2016-11-22 23:15 Dictionary - only women 0.5 N/A 0.5
Maciej 2016-11-22 23:07 "First attempt - dictionary" 0.508673551130984 N/A 0.505623319318368
Maciej 2016-11-22 21:10 test submition (all F) 0.5 N/A 0.5
Mieszko 2016-11-22 18:29 female + male dict 0.539151142636585 N/A 0.531496530924542
Mieszko 2016-11-22 18:14 male + female dict N/A N/A 0.530010845503573
Mieszko 2016-11-20 17:14 add swears 0.537141150938724 N/A 0.530010845503573
Mieszko 2016-11-20 17:07 add swears N/A N/A 0.530010845503573
Mieszko 2016-11-20 09:42 dict v4 0.541335916221216 N/A 0.532083376665825
Mieszko 2016-11-19 23:15 dict v3 0.538160711944885 N/A 0.529713708419379
Mieszko 2016-11-19 22:18 improve dict v2 0.537847561064422 N/A 0.529713708419379
Mieszko 2016-11-19 22:13 improve dict 0.537847561064422 N/A 0.523993819548649
Mieszko 2016-11-19 20:07 Dictionary approach 0.526989236348806 N/A 0.523993819548649
p/tlen 2016-11-15 09:29 trivial baseline (only female) 0.5 0.5 0.5