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

Give the probability that a text in Polish was written by a man. [ver. 2.0.1]

# submitter when ver. description dev-0 Accuracy dev-0 Likelihood dev-1 Accuracy dev-1 Likelihood test-A Accuracy test-A Likelihood
1 kaczla 2020-07-02 08:55 2.0.1 Polish RoBERTa (base), epoch 5, seq_len 512, active dropout fairseq roberta-pl 0.75644 0.62799 0.74832 0.62476 0.74332 0.62110
522 kubapok 2020-06-28 14:33 2.0.0 unsupervised_men_women_mean_closer 0.50007 0.00000 0.50013 0.00000 0.49990 0.00000
521 kubapok 2020-06-25 13:10 2.0.0 unsupervised fix test-a 0.56230 0.00000 0.55795 0.00000 0.55828 0.00000
520 kubapok 2020-06-25 09:54 2.0.0 unsupervised 0.56230 0.00000 0.55795 0.00000 N/A N/A
519 kubapok 2020-06-19 10:51 2.0.0 pl roberta large active dropout 1 run 0.75675 0.62653 0.74711 0.61832 0.74160 0.61615
518 kubapok 2020-06-19 10:25 2.0.0 soften probs roberta large finetunned 0.75989 0.62311 0.74943 0.61641 0.74388 0.61240
2 kubapok 2020-06-19 09:29 2.0.1 pl roberta large active dropout avg 12 runs 0.75965 0.63032 0.74988 0.62247 0.74406 0.61949
517 kaczla 2020-06-15 09:21 2.0.0 XLM-RoBERTa 1 epoch model=xlmr_base-seq_len=512 roberta-xlm 0.73154 0.60428 0.72444 0.60192 0.72356 0.60015
516 kaczla 2020-06-15 09:21 2.0.0 XLM-RoBERTa 1 epoch model=xlmr_large-seq_len=512 roberta-xlm 0.70719 0.57917 0.68732 0.57095 0.69047 0.57141
5 kaczla 2020-06-15 06:40 2.0.1 XLM-R model=xlmr_large-seq_len=512 roberta-xlm 0.70719 0.57917 0.68732 0.57095 0.69047 0.57141
3 kaczla 2020-06-15 06:40 2.0.1 XLM-R model=xlmr_base-seq_len=512 roberta-xlm 0.71171 0.58902 0.70184 0.58230 0.70118 0.58280
515 kubapok 2020-06-14 09:06 2.0.0 bilstm emb size 300 0.70417 0.57807 0.70061 0.57678 0.69496 0.57405
514 kaczla 2020-06-13 20:48 2.0.0 from scratch RoBERTa classifier (only), seq_len=256 epoch=epoch20 fairseq roberta 0.68991 0.56160 0.68586 0.56150 0.67951 0.55784
513 kaczla 2020-06-13 20:48 2.0.0 from scratch RoBERTa classifier (only), seq_len=256 epoch=epoch10 fairseq roberta 0.69010 0.55805 0.68526 0.55695 0.67744 0.55314
512 kaczla 2020-06-13 20:45 2.0.0 from scratch RoBERTa MLM + classifier, seq_len=256 fairseq roberta 0.70276 0.57555 0.69255 0.57229 0.69153 0.57068
511 kaczla 2020-06-13 18:28 2.0.0 Polish RoBERTa (base), epoch 5, seq_len 512 fairseq roberta-pl 0.75686 0.61212 0.74828 0.61205 0.74185 0.60913
510 kubapok 2020-06-12 21:37 2.0.0 logistic regression on polish roberta 0.67341 0.54798 0.65471 0.53876 0.65956 0.54113
509 kubapok 2020-06-12 20:44 2.0.0 logistic regression on xlm roberta 0.66913 0.54803 0.65301 0.53899 0.65545 0.54067
508 kubapok 2020-06-12 13:43 2.0.0 polish roberta finetunned large 3 epochs fairseq 0.75989 0.61474 0.74943 0.60794 0.74388 0.60503
4 kubapok 2020-06-11 21:49 2.0.1 keras lstm on spe 50k vocab, 5epochs 0.70685 0.57509 0.70046 0.57281 0.69786 0.57177
507 kubapok 2020-06-10 19:41 2.0.0 xgb on tfidf 0.66207 0.54693 0.65765 0.54675 0.65112 0.54269
506 kubapok 2020-06-10 19:34 2.0.0 fix fasttext standard params 0.67156 0.53548 0.66367 0.53158 0.65865 0.52771
505 kubapok 2020-06-10 19:22 2.0.0 fasttext hypertune 0.68800 0.55105 0.67664 0.54717 0.67448 0.54541
504 kubapok 2020-06-09 14:06 2.0.0 linearSVM on tfidf 0.67327 0.00000 0.66925 0.00000 0.66477 0.00000
503 kubapok 2020-06-09 13:19 2.0.0 logistic regression on polish roberta last layer 0.67341 0.54798 0.65471 0.53876 0.65956 0.54113
502 kubapok 2020-06-09 10:56 2.0.0 fasttext 50 epochs 0.79221 0.00000 0.49968 0.00000 0.49144 0.00000
501 kubapok 2020-06-09 09:56 2.0.0 fasttext standard 0.66971 0.00000 0.61290 0.52516 0.60438 0.52068
500 kubapok 2020-06-07 20:55 2.0.0 xgbclassifier standard params on tfidf 0.61196 0.52337 0.61290 0.52516 0.60438 0.52068
499 [anonymised] 2020-06-07 12:45 2.0.0 XGBoost ready-made ready-made xgboost 0.61238 0.52290 0.60935 0.52390 0.60039 0.52000
498 kubapok 2020-06-06 09:14 2.0.0 tfidf logistic regression 0.68278 0.55867 0.67661 0.55626 0.67175 0.55278
497 Ivan Novgorodtsev 2020-06-05 10:17 2.0.0 svm ready-made svm 0.60424 0.00000 0.59963 0.00000 0.59623 0.00000
496 Jakub Stefko 2020-06-03 17:39 2.0.0 2nd logistic-regression word2vec N/A N/A N/A N/A 0.49299 0.49679
495 p/tlen 2020-06-03 09:34 2.0.0 test submission 4 baseline 0.50000 0.48990 0.50000 0.48990 0.50000 0.48990
494 p/tlen 2020-06-03 08:33 2.0.0 test submission 3 baseline 0.50000 0.48990 0.50000 0.48990 0.50000 0.48990
493 Jakub Stefko 2020-06-02 14:12 2.0.0 1st logistic-regression word2vec N/A N/A N/A N/A 0.00000 N/A
492 p/tlen 2020-05-30 21:30 2.0.0 null model null-model 0.50000 0.50000 0.50000 0.50000 0.50000 0.48990
491 Yevheniia Tsapkova 2020-05-26 11:59 2.0.0 improved results for probabilities probabilities 0.67284 N/A 0.66882 N/A 0.66309 N/A
490 Yevheniia Tsapkova 2020-05-26 10:00 2.0.0 probabilities solution probabilities 0.67284 N/A 0.66882 N/A 0.66309 N/A
489 kubapok 2020-05-25 09:49 2.0.0 polish large roberta finetune 1 epoch (add likelihood) 0.75119 0.59610 0.74263 0.59295 0.73598 0.58475
6 Damian Litwin 2020-05-24 14:52 2.0.1 self-made NB with probs ISI-2019-063 probabilities 0.66983 0.52984 0.65097 0.52370 0.65612 0.52537
488 Mikolaj Bachorz 2020-05-24 14:17 2.0.0 v5 probabilities 0.80379 0.59574 0.91633 0.65340 0.64427 0.52134
487 Mikolaj Bachorz 2020-05-24 14:11 2.0.0 v6 0.80379 0.57457 0.91633 0.61566 0.64427 0.52052
486 Mikolaj Bachorz 2020-05-24 14:02 2.0.0 v5 0.80379 0.59574 0.91633 0.65340 0.64427 0.52134
485 Mikolaj Bachorz 2020-05-24 13:56 2.0.0 v4 0.80379 0.60903 0.91633 0.68521 0.64427 0.51176
484 Mikolaj Bachorz 2020-05-24 13:39 2.0.0 v3 0.80379 0.62231 0.91633 0.71879 0.64427 0.50223
483 kubapok 2020-05-24 07:57 2.0.0 Merge branch 'master' of ssh://gonito.net/kubapok/petite-difference-challenge2 0.75023 0.00000 N/A N/A 0.73240 0.00000
482 p/tlen 2020-05-23 21:29 2.0.0 null model null-model 0.50000 0.50000 0.50000 0.50000 0.50000 0.50000
481 Mikolaj Bachorz 2020-05-23 09:50 2.0.0 v2 0.80379 0.62830 0.91633 0.77274 0.64427 0.45354
480 Mikolaj Bachorz 2020-05-22 23:04 2.0.0 v1 0.76235 0.00000 0.85390 0.00000 0.61107 0.00000
479 Adam Chrzanowski 2020-05-13 13:24 1.0.1 small-roberta classifier eval_batch_size=200 evaluate_during_training=true evaluate_during_training_steps=5000 num_train_epochs=5 save_steps=5000 train max_lines=1_500_000 train_batch_size=100 use_cached_eval_features=true neural-network transformer roberta 0.71307 N/A 0.70805 N/A 0.70384 N/A
478 kaczla 2020-05-02 12:41 1.0.1 Classifier with RoBERTa corpus=base_with_cc model=small seq_len=256 sliding=False valid=dev-0 neural-network transformer roberta 0.72328 N/A 0.71980 N/A 0.71603 N/A
477 kaczla 2020-05-02 12:41 1.0.1 Classifier with RoBERTa corpus=base_with_cc model=small seq_len=128 sliding=False valid=dev-0 neural-network transformer roberta 0.71685 N/A 0.71588 N/A 0.71112 N/A
476 kaczla 2020-05-02 12:41 1.0.1 Classifier with RoBERTa corpus=base model=small seq_len=512 sliding=False valid=dev-0 neural-network transformer roberta 0.72839 N/A 0.72392 N/A 0.71900 N/A
475 kaczla 2020-05-02 12:41 1.0.1 Classifier with RoBERTa corpus=base model=small seq_len=256 sliding=False valid=dev-0 neural-network transformer roberta 0.72722 N/A 0.72474 N/A 0.71711 N/A
474 kaczla 2020-05-02 12:41 1.0.1 Classifier with RoBERTa corpus=base model=small seq_len=128 sliding=True valid=dev-0 neural-network transformer roberta 0.71886 N/A 0.71783 N/A 0.71010 N/A
473 kaczla 2020-05-02 12:41 1.0.1 Classifier with RoBERTa corpus=base model=small seq_len=128 sliding=False valid=dev-1 neural-network transformer roberta 0.71623 N/A 0.71499 N/A 0.70933 N/A
472 kaczla 2020-05-02 12:41 1.0.1 Classifier with RoBERTa corpus=base model=small seq_len=128 sliding=False valid=dev-0 neural-network transformer roberta 0.71972 N/A 0.71869 N/A 0.71250 N/A
471 kaczla 2020-05-02 12:41 1.0.1 Classifier with RoBERTa corpus=base model=small seq_len=128 sliding=False valid=dev-0-dev-1 neural-network transformer roberta 0.72099 N/A 0.71799 N/A 0.71292 N/A
470 kaczla 2020-05-02 12:41 1.0.1 Classifier with RoBERTa corpus=base model=normal seq_len=512 sliding=False valid=dev-0 neural-network transformer roberta 0.71632 N/A 0.71134 N/A 0.70462 N/A
469 kaczla 2020-05-02 12:41 1.0.1 Classifier with RoBERTa corpus=base model=normal seq_len=256 sliding=False valid=dev-0 neural-network transformer roberta 0.73558 N/A 0.73049 N/A 0.72432 N/A
468 kaczla 2020-05-02 12:41 1.0.1 Classifier with RoBERTa corpus=base model=normal seq_len=128 sliding=False valid=dev-0 neural-network transformer roberta 0.72824 N/A 0.72552 N/A 0.71995 N/A
467 kaczla 2020-05-02 12:41 1.0.1 Classifier with RoBERTa corpus=base model=big seq_len=512 sliding=False valid=dev-0 neural-network transformer roberta 0.74090 N/A 0.73562 N/A 0.72914 N/A
466 kaczla 2020-05-02 12:41 1.0.1 Classifier with RoBERTa corpus=base model=big seq_len=256 sliding=False valid=dev-0 neural-network transformer roberta 0.74382 N/A 0.74011 N/A 0.73017 N/A
465 kaczla 2020-05-02 12:41 1.0.1 Classifier with RoBERTa corpus=base model=big seq_len=128 sliding=False valid=dev-0 neural-network transformer roberta 0.73382 N/A 0.72900 N/A 0.72484 N/A
464 Artur Nowakowski 2020-04-20 17:14 1.0.0 fine-tuned RoBERTa classifier (full-train) neural-network transformer roberta 0.72107 N/A 0.71903 N/A 0.71279 N/A
463 Artur Nowakowski 2020-04-18 08:12 1.0.0 fine-tuned RoBERTa classifier (full-train) neural-network transformer roberta 0.72107 N/A 0.71903 N/A 0.71279 N/A
462 kaczla 2020-04-17 07:00 1.0.0 fine-tune RoBERTa classifier (train 1M lines in classification) - RoBERTA pretrained for 7 days (5 epochs) on the current corpora model=base neural-network transformer roberta 0.70466 N/A 0.69787 N/A 0.69680 N/A
461 [anonymised] 2019-02-21 22:58 1.0.0 my brilliant solution 0.65445 N/A N/A N/A 0.64519 N/A
460 [anonymised] 2019-02-21 15:18 1.0.0 Michal Mioduszewski - solution 0.64952 N/A N/A N/A 0.63986 N/A
459 [anonymised] 2019-01-27 16:29 1.0.0 dampie5 solution v3.5 0.66219 N/A 0.65437 N/A 0.65190 N/A
458 [anonymised] 2019-01-27 16:25 1.0.0 dampie5 solution v3.4 0.65339 N/A 0.63752 N/A 0.62094 N/A
457 [anonymised] 2019-01-27 16:18 1.0.0 dampie5 solution v3.3 0.65339 N/A 0.63752 N/A 0.64933 N/A
456 [anonymised] 2019-01-27 15:43 1.0.0 dampie5 solution v3.2 0.65339 N/A 0.63752 N/A 0.64495 N/A
455 [anonymised] 2019-01-27 15:36 1.0.0 dampie5 solution v3.1 0.65339 N/A 0.63752 N/A 0.64203 N/A
454 [anonymised] 2019-01-27 15:30 1.0.0 dampie5 solution v3 0.65339 N/A 0.63752 N/A 0.64137 N/A
453 [anonymised] 2019-01-27 13:51 1.0.0 asdds N/A N/A N/A N/A 0.64519 N/A
452 [anonymised] 2019-01-27 13:33 1.0.0 Blah N/A N/A N/A N/A 0.66275 N/A
451 [anonymised] 2019-01-27 13:11 1.0.0 dampie5 solution v2 0.65070 N/A 0.61818 N/A 0.63449 N/A
450 [anonymised] 2019-01-27 13:01 1.0.0 Wesja milion7 0.63707 N/A N/A N/A 0.65659 N/A
449 [anonymised] 2019-01-27 12:50 1.0.0 Wesja milion6 0.63707 N/A N/A N/A 0.65600 N/A
448 [anonymised] 2019-01-27 12:46 1.0.0 Wesja milion5 N/A N/A N/A N/A 0.65600 N/A
447 [anonymised] 2019-01-27 12:31 1.0.0 Wesja milion4 N/A N/A N/A N/A 0.65223 N/A
446 [anonymised] 2019-01-27 12:10 1.0.0 Wesja milion3 N/A N/A N/A N/A 0.62460 N/A
445 [anonymised] 2019-01-27 12:06 1.0.0 Wesja milion2 N/A N/A N/A N/A 0.58706 N/A
444 [anonymised] 2019-01-27 11:23 1.0.0 Wesja milion N/A N/A N/A N/A 0.55697 N/A
443 [anonymised] 2019-01-27 09:40 1.0.0 Test4 N/A N/A N/A N/A 0.66276 N/A
442 [anonymised] 2019-01-27 08:11 1.0.0 Test N/A N/A N/A N/A 0.59881 N/A
441 [anonymised] 2019-01-27 08:07 1.0.0 proba milion nowa N/A N/A N/A N/A 0.64888 N/A
440 [anonymised] 2019-01-27 07:53 1.0.0 probamilion N/A N/A N/A N/A 0.61159 N/A
439 [anonymised] 2019-01-27 07:23 1.0.0 dodanie zaktualizowanego test.py 0.66416 N/A N/A N/A 0.65883 N/A
438 [anonymised] 2019-01-27 06:21 1.0.0 test 0.66416 N/A N/A N/A 0.65883 N/A
437 [anonymised] 2019-01-26 20:26 1.0.0 zadanie1 proba testA N/A N/A N/A N/A 0.64888 N/A
436 [anonymised] 2019-01-26 19:51 1.0.0 dodanie wyniku dla test-A 0.64904 N/A 0.64187 N/A 0.63726 N/A
435 [anonymised] 2019-01-26 17:54 1.0.0 rozwiązanie zadania "He Said She Said" 0.64904 N/A 0.64187 N/A N/A N/A
434 [anonymised] 2019-01-26 17:38 1.0.0 solution1 0.65872 N/A 0.64920 N/A 0.64647 N/A
433 [anonymised] 2019-01-26 17:35 1.0.0 Init_3-zad-1 0.65446 N/A 0.65023 N/A 0.64519 N/A
432 [anonymised] 2019-01-26 17:26 1.0.0 mk 0.65445 N/A N/A N/A 0.64519 N/A
431 [anonymised] 2019-01-26 17:12 1.0.0 kd solution 0.65445 N/A N/A N/A 0.64519 N/A
430 [anonymised] 2019-01-26 17:04 1.0.0 s402267 - petite-difference-challenge2 0.65446 N/A 0.65023 N/A 0.64519 N/A
429 [anonymised] 2019-01-26 17:04 1.0.0 my solution 0.63105 N/A 0.61818 N/A 0.63194 N/A
428 [anonymised] 2019-01-26 16:48 1.0.0 My brilliant solution 0.57248 N/A 0.56905 N/A 0.56693 N/A
427 [anonymised] 2019-01-26 16:47 1.0.0 xx N/A N/A N/A N/A 0.64647 N/A
426 [anonymised] 2019-01-26 16:44 1.0.0 My brilliant solution N/A N/A 0.56905 N/A 0.56693 N/A
425 [anonymised] 2019-01-26 16:44 1.0.0 my brilliant solution2 0.65445 N/A N/A N/A 0.64519 N/A
424 [anonymised] 2019-01-26 16:44 1.0.0 my briliant solution 0.65446 N/A 0.65023 N/A 0.64519 N/A
423 [anonymised] 2019-01-26 16:43 1.0.0 my first soution .py 0.65445 N/A N/A N/A 0.64519 N/A
422 [anonymised] 2019-01-26 16:43 1.0.0 my brilliant solution 0.65445 N/A N/A N/A 0.64519 N/A
421 [anonymised] 2019-01-26 16:40 1.0.0 my first soution 0.65445 N/A N/A N/A 0.64519 N/A
420 [anonymised] 2019-01-26 16:39 1.0.0 DODAŁAM TUTAJ MOJE ZADANIE ZROBIONE N/A N/A N/A N/A 0.56192 N/A
419 [anonymised] 2019-01-26 16:31 1.0.0 dssdd N/A N/A N/A N/A N/A N/A
418 [anonymised] 2019-01-26 16:23 1.0.0 dodanie plikow wynikowych oraz skryptu 0.66416 N/A N/A N/A 0.65051 N/A
417 [anonymised] 2019-01-26 16:14 1.0.0 My brilliant solution N/A N/A 0.56905 N/A N/A N/A
416 [anonymised] 2019-01-03 11:37 1.0.0 brylantowe rozwiazanie2 0.52337 N/A N/A N/A 0.52448 N/A
415 [anonymised] 2019-01-03 11:31 1.0.0 brylantowe rozwiazanie 0.51297 N/A N/A N/A 0.51836 N/A
414 [anonymised] 2018-12-21 12:40 1.0.0 fixed missing result naive-bayes python scikit-learn 0.67183 N/A 0.65628 N/A 0.65905 N/A
413 [anonymised] 2018-12-21 12:32 1.0.0 added ML binary NB solution naive-bayes python scikit-learn 0.67183 N/A N/A N/A 0.65905 N/A
412 [anonymised] 2018-12-11 22:32 1.0.0 initial version with training limit on 1m python scikit-learn better-than-no-model-baseline 0.67254 N/A 0.66604 N/A 0.65983 N/A
411 [anonymised] 2018-11-29 10:07 1.0.0 LinearSVC dev-0 dev-1 test-A - read submission_info.md python scikit-learn 0.67284 N/A 0.66882 N/A 0.66309 N/A
410 [anonymised] 2018-11-27 13:13 1.0.0 LinearSVC dev-0 dev-1 test-A python scikit-learn 0.67284 N/A 0.66882 N/A 0.66309 N/A
409 [anonymised] 2018-11-27 12:49 1.0.0 work on files stripped from CR bytes (only locally - commiting only results) 0.67284 N/A N/A N/A 0.66309 N/A
408 [anonymised] 2018-11-27 10:47 1.0.0 fix len 0.54147 N/A N/A N/A 0.58143 N/A
407 [anonymised] 2018-11-27 10:44 1.0.0 LinearSVC test solution 0.54147 N/A N/A N/A N/A N/A
406 [anonymised] 2018-05-24 13:36 1.0.0 my brilliant solution N/A N/A N/A N/A N/A N/A
405 [anonymised] 2018-05-22 14:09 1.0.0 change dev/test 0.53623 N/A N/A N/A 0.53678 N/A
403 [anonymised] 2018-05-22 14:05 1.0.0 bla bal N/A N/A N/A N/A N/A N/A
404 [anonymised] 2018-05-22 13:56 1.0.0 my brilliant solution N/A N/A N/A N/A N/A N/A
402 [anonymised] 2018-05-22 12:59 1.0.0 my brilliant solution naive bayes N/A N/A N/A N/A N/A N/A
393 [anonymised] 2018-05-20 22:12 1.0.0 naive 0.99640 N/A 0.99542 N/A 0.59369 N/A
401 [anonymised] 2018-05-20 21:58 1.0.0 'bayes' N/A N/A N/A N/A N/A N/A
391 [anonymised] 2018-05-20 18:11 1.0.0 naive bayes 2 N/A N/A N/A N/A 0.59369 N/A
390 [anonymised] 2018-05-20 17:04 1.0.0 naive bayes N/A N/A N/A N/A N/A N/A
400 [anonymised] 2018-05-20 16:36 1.0.0 Zadanie 7 0.66317 N/A 0.64740 N/A 0.65388 N/A
392 [anonymised] 2018-05-20 15:57 1.0.0 naive bayes 0.67005 N/A N/A N/A 0.65872 N/A
394 [anonymised] 2018-05-20 14:38 1.0.0 my solution 0.66317 N/A 0.64740 N/A 0.65388 N/A
399 [anonymised] 2018-05-18 08:55 1.0.0 NaiveBays 0.53623 N/A N/A N/A 0.53678 N/A
397 [anonymised] 2018-05-18 08:53 1.0.0 naive_bayes N/A N/A N/A N/A 0.53678 N/A
396 [anonymised] 2018-05-18 08:32 1.0.0 my solution -nb N/A N/A N/A N/A 0.53678 N/A
389 [anonymised] 2018-05-18 08:29 1.0.0 NaiveBayse N/A N/A N/A N/A 0.53678 N/A
398 [anonymised] 2018-05-18 07:44 1.0.0 naive bayes N/A N/A N/A N/A 0.53678 N/A
395 [anonymised] 2018-05-17 15:52 1.0.0 polecenia 0.61123 N/A N/A N/A 0.60185 N/A
388 [anonymised] 2018-05-17 15:51 1.0.0 my brilliant solution 0.61123 N/A N/A N/A 0.60185 N/A
387 [anonymised] 2018-05-17 15:46 1.0.0 my brilliant solution 0.61123 N/A N/A N/A 0.60185 N/A
386 [anonymised] 2018-05-15 19:19 1.0.0 Naive Bayes solution 0.55897 N/A 0.55444 N/A 0.54939 N/A
384 [anonymised] 2018-05-15 15:17 1.0.0 UMZ homerwork - naive bayes N/A N/A N/A N/A 0.59369 N/A
383 [anonymised] 2018-05-15 14:47 1.0.0 my brilliant solution 0.61123 N/A N/A N/A 0.60185 N/A
385 [anonymised] 2018-05-15 14:40 1.0.0 Naive Bayes naive-bayes N/A N/A N/A N/A 0.59369 N/A
382 [anonymised] 2018-05-11 14:18 1.0.0 my brilliant solution 0.57816 N/A N/A N/A N/A N/A
381 [anonymised] 2018-05-11 13:59 1.0.0 my brilliant solution N/A N/A N/A N/A N/A N/A
380 [anonymised] 2018-05-11 13:51 1.0.0 my brilliant solution N/A N/A N/A N/A N/A N/A
379 [anonymised] 2018-05-11 13:05 1.0.0 my brilliant solution N/A N/A N/A N/A N/A N/A
378 [anonymised] 2018-02-13 21:16 1.0.0 naive-bayes naive-bayes python 0.49760 N/A 0.49896 N/A 0.60877 N/A
377 [anonymised] 2018-02-13 20:14 1.0.0 logistic-regression ready-made python ready-made logistic-regression 0.49760 N/A 0.49896 N/A 0.60632 N/A
376 [anonymised] 2018-02-12 23:45 1.0.0 check 0.49760 N/A 0.49896 N/A 0.49998 N/A
375 [anonymised] 2018-02-12 23:28 1.0.0 check 0.49760 N/A 0.49896 N/A N/A N/A
374 [anonymised] 2018-02-12 23:23 1.0.0 check 0.49760 N/A 0.49896 N/A N/A N/A
373 [anonymised] 2018-02-12 22:59 1.0.0 check 0.49760 N/A 0.49896 N/A N/A N/A
372 [anonymised] 2018-02-06 23:17 1.0.0 logistic-regression ready-made python ready-made logistic-regression 0.49760 N/A 0.49896 N/A 0.50030 N/A
371 [anonymised] 2018-02-06 22:59 1.0.0 logistic-regression ready-made 0.49656 N/A 0.49866 N/A 0.50000 N/A
370 [anonymised] 2018-02-06 22:33 1.0.0 logistic-regression ready-made 0.49656 N/A 0.49866 N/A 0.50001 N/A
369 [anonymised] 2018-02-06 22:06 1.0.0 logistic-regression ready-made 0.49656 N/A 0.49866 N/A 0.49753 N/A
368 [anonymised] 2018-02-06 21:09 1.0.0 logistic-regression ready-made N/A N/A N/A N/A N/A N/A
367 [anonymised] 2018-02-06 21:05 1.0.0 logistic-regression ready-made N/A N/A N/A N/A N/A N/A
366 [anonymised] 2018-02-06 21:01 1.0.0 logistic-regression ready-made N/A N/A N/A N/A N/A N/A
365 [anonymised] 2018-02-06 20:46 1.0.0 logistic-regression ready-made N/A N/A N/A N/A N/A N/A
364 [anonymised] 2018-02-06 20:41 1.0.0 logistic-regression ready-made N/A N/A N/A N/A N/A N/A
360 [anonymised] 2018-01-30 19:34 1.0.0 regr ready-made linear-regression 0.66486 N/A N/A N/A 0.50109 N/A
363 [anonymised] 2018-01-30 19:31 1.0.0 naibe bayss naive-bayes 0.66486 N/A N/A N/A 0.50109 N/A
361 [anonymised] 2018-01-30 19:26 1.0.0 naibe bays 0.66486 N/A N/A N/A N/A N/A
362 [anonymised] 2018-01-29 16:12 1.0.0 regression ready make logistic-regression 0.66486 N/A N/A N/A 0.49973 N/A
359 [anonymised] 2018-01-19 08:25 1.0.0 zadanie 008 z kodem programu v1.3 ready-made logistic-regression 0.51239 N/A 0.50649 N/A 0.50882 N/A
358 [anonymised] 2018-01-18 20:49 1.0.0 zadanie 008 z kodem programu ready-made logistic-regression N/A N/A N/A N/A N/A N/A
356 [anonymised] 2018-01-14 14:00 1.0.0 Add code Task 5. 0.60010 N/A 0.59288 N/A 0.59835 N/A
357 [anonymised] 2018-01-13 20:28 1.0.0 regresja logistyczna ready-made logistic-regression 0.63434 N/A 0.61847 N/A 0.62400 N/A
355 [anonymised] 2018-01-09 12:59 1.0.0 KenLM kenlm 0.53752 N/A N/A N/A 0.61203 N/A
354 [anonymised] 2018-01-08 17:50 1.0.0 fix N/A N/A N/A N/A N/A N/A
353 [anonymised] 2018-01-08 17:29 1.0.0 fix N/A N/A N/A N/A N/A N/A
352 [anonymised] 2018-01-07 18:48 1.0.0 nb_ready naive-bayes N/A N/A N/A N/A 0.50063 N/A
345 [anonymised] 2018-01-07 17:48 1.0.0 logreg_ready ready-made logistic-regression N/A N/A N/A N/A 0.50025 N/A
351 [anonymised] 2018-01-07 10:16 1.0.0 logreg_ready ready-made logistic-regression N/A N/A N/A N/A N/A N/A
350 [anonymised] 2018-01-07 10:13 1.0.0 logreg_ready ready-made logistic-regression N/A N/A N/A N/A N/A N/A
349 [anonymised] 2018-01-07 10:08 1.0.0 solution1 ready-made logistic-regression N/A N/A N/A N/A N/A N/A
347 [anonymised] 2018-01-06 12:30 1.0.0 naive bayes przy uzyciu wektorow czestosci slow naive-bayes 0.65968 N/A 0.64131 N/A 0.64733 N/A
346 kaczla 2018-01-04 19:25 1.0.0 LSTM neural-network 0.70125 N/A 0.69679 N/A 0.69214 N/A
348 kaczla 2018-01-04 19:12 1.0.0 KenLM kenlm 0.67077 N/A 0.66102 N/A 0.65053 N/A
344 [anonymised] 2017-12-30 21:40 1.0.0 Done self-made naive-bayes self-made 0.66918 N/A 0.64976 N/A 0.65531 N/A
289 [anonymised] 2017-12-30 21:29 1.0.0 Poprawilem zgodnosc linii naive-bayes 0.66918 N/A 0.64976 N/A 0.65531 N/A
342 [anonymised] 2017-12-29 08:48 1.0.0 Gotowe naive-bayes N/A N/A N/A N/A N/A N/A
341 [anonymised] 2017-12-23 12:58 1.0.0 naive bayes przegenerowano test-A/out.tsv dla wiekszego slownika czestosci slow naive-bayes N/A N/A N/A N/A 0.59832 N/A
335 [anonymised] 2017-12-23 12:54 1.0.0 naive bayes przegenerowano test-A/out.tsv dla wiekszego slownika czestosci slow N/A N/A N/A N/A 0.50727 N/A
332 [anonymised] 2017-12-23 12:51 1.0.0 naive bayes poprawiony out.tsv w test-A N/A N/A N/A N/A 0.53115 N/A
340 [anonymised] 2017-12-23 12:43 1.0.0 naive bayes naive-bayes N/A N/A N/A N/A N/A N/A
339 [anonymised] 2017-12-20 20:48 1.0.0 correct path N/A N/A N/A N/A N/A N/A
338 [anonymised] 2017-12-20 20:46 1.0.0 logistic-regression ready-made N/A N/A N/A N/A N/A N/A
337 [anonymised] 2017-12-20 10:57 1.0.0 my brilliant solution naive-bayes 0.49738 N/A N/A N/A 0.49782 N/A
336 [anonymised] 2017-12-17 16:50 1.0.0 Naive bayes naive-bayes N/A N/A N/A N/A 0.50726 N/A
334 [anonymised] 2017-12-17 16:41 1.0.0 Naive bayes N/A N/A N/A N/A 0.50728 N/A
331 [anonymised] 2017-12-17 15:44 1.0.0 naive-bayes naive-bayes 0.53840 N/A 0.50229 N/A 0.57380 N/A
333 [anonymised] 2017-12-17 15:33 1.0.0 naive bayes N/A N/A N/A N/A N/A N/A
329 [anonymised] 2017-12-16 13:18 1.0.0 Logistic regression, ready-made ready-made logistic-regression 0.52489 N/A 0.52753 N/A 0.52919 N/A
330 [anonymised] 2017-12-16 12:08 1.0.0 Naive Bayes naive-bayes 0.51505 N/A 0.51576 N/A 0.52003 N/A
288 [anonymised] 2017-12-14 23:47 1.0.0 test commit 2 naive-bayes self-made N/A N/A N/A N/A N/A N/A
287 [anonymised] 2017-12-14 23:43 1.0.0 test commit 2 naive-bayes N/A N/A N/A N/A N/A N/A
328 Paweł Skórzewski 2017-12-11 13:39 1.0.0 Word2Vec + logistic regression (fix newlines) python ready-made logistic-regression 0.51816 N/A N/A N/A 0.51148 N/A
327 Paweł Skórzewski 2017-12-11 13:10 1.0.0 Word2Vec + logistic regression python ready-made logistic-regression 0.51816 N/A N/A N/A N/A N/A
326 [anonymised] 2017-12-06 22:52 1.0.0 TF-IDF - logistic regression N/A N/A N/A N/A 0.50000 N/A
325 [anonymised] 2017-12-06 22:46 1.0.0 TF-IDF - logistic regression N/A N/A N/A N/A N/A N/A
343 [anonymised] 2017-12-06 09:56 1.0.0 Word2Vec on 200k words ready-made logistic-regression N/A N/A N/A N/A 0.59513 N/A
324 [anonymised] 2017-12-05 23:43 1.0.0 Logistic word2vec N/A N/A N/A N/A 0.58870 N/A
308 [anonymised] 2017-12-05 23:30 1.0.0 naive bayes with word2Vec naive-bayes N/A N/A N/A N/A 0.55990 N/A
323 [anonymised] 2017-12-05 23:03 1.0.0 old fashioned word2vec N/A N/A N/A N/A 0.57788 N/A
322 [anonymised] 2017-12-05 22:56 1.0.0 It being wasted N/A N/A N/A N/A 0.51272 N/A
321 [anonymised] 2017-12-05 22:37 1.0.0 Bigger word2vec model N/A N/A N/A N/A 0.51033 N/A
319 [anonymised] 2017-12-05 20:33 1.0.0 Test with bigger train model N/A N/A N/A N/A 0.51004 N/A
317 [anonymised] 2017-12-05 20:11 1.0.0 Attempt with small train and trained word2Vec models N/A N/A N/A N/A 0.51042 N/A
313 [anonymised] 2017-12-03 22:17 1.0.0 Add working app.py file self-made logistic-regression N/A N/A N/A N/A N/A N/A
309 [anonymised] 2017-12-03 21:43 1.0.0 05 naive bayes v1 naive-bayes N/A N/A N/A N/A 0.49915 N/A
307 kaczla 2017-12-03 20:31 1.0.0 Naive Bayes naive-bayes 0.66092 N/A 0.64342 N/A 0.65071 N/A
306 [anonymised] 2017-12-03 19:32 1.0.0 Logistic regression, ready-made ready-made logistic-regression 0.50216 N/A 0.50116 N/A 0.50305 N/A
298 [anonymised] 2017-12-03 18:39 1.0.0 naive bayes naive-bayes N/A N/A N/A N/A N/A N/A
315 [anonymised] 2017-12-03 18:34 1.0.0 logistic regresion still M self-made logistic-regression N/A N/A N/A N/A N/A N/A
312 [anonymised] 2017-12-03 18:19 1.0.0 logistic regresion self-made logistic-regression N/A N/A N/A N/A N/A N/A
305 [anonymised] 2017-12-03 16:29 1.0.0 lm kenlm 0.93282 N/A 0.75279 N/A 0.61190 N/A
299 [anonymised] 2017-12-03 16:18 1.0.0 Logistic regression self-made logistic-regression 0.53592 N/A 0.51831 N/A 0.51619 N/A
303 [anonymised] 2017-12-03 16:05 1.0.0 test 0.53592 N/A 0.51831 N/A 0.51619 N/A
310 [anonymised] 2017-12-03 14:13 1.0.0 Naive Bayes naive-bayes N/A N/A N/A N/A N/A N/A
311 [anonymised] 2017-12-03 13:48 1.0.0 LogReg ready-made logistic-regression N/A N/A N/A N/A N/A N/A
320 [anonymised] 2017-12-03 13:41 1.0.0 LogReg self-made logistic-regression N/A N/A N/A N/A N/A N/A
301 [anonymised] 2017-12-03 10:29 1.0.0 Task 5. naive-bayes 0.60010 N/A 0.59288 N/A 0.59835 N/A
302 [anonymised] 2017-12-03 01:59 1.0.0 Naive Bayes naive-bayes 0.50217 N/A 0.50103 N/A 0.50391 N/A
296 [anonymised] 2017-12-03 01:13 1.0.0 Naive Bayes 0.50217 N/A N/A N/A N/A N/A
294 [anonymised] 2017-12-01 13:44 1.0.0 Logistic regression, self-made self-made logistic-regression 0.52586 N/A 0.52755 N/A 0.52832 N/A
318 [anonymised] 2017-11-30 23:11 1.0.0 word2vec N/A N/A N/A N/A 0.59523 N/A
314 [anonymised] 2017-11-30 20:23 1.0.0 naive-bayes naive-bayes ready-made 0.62018 N/A 0.60296 N/A 0.61088 N/A
300 [anonymised] 2017-11-30 19:51 1.0.0 Logistic regression, self-made self-made logistic-regression 0.50000 N/A 0.50000 N/A 0.50000 N/A
291 [anonymised] 2017-11-30 19:49 1.0.0 LogR readymade ready-made logistic-regression N/A N/A 0.49985 N/A 0.49999 N/A
297 [anonymised] 2017-11-30 18:01 1.0.0 Logistic regression, self-made self-made logistic-regression 0.50000 N/A 0.50000 N/A N/A N/A
295 [anonymised] 2017-11-30 12:37 1.0.0 Naive bayes on text length naive-bayes ready-made 0.53752 N/A N/A N/A 0.50117 N/A
293 [anonymised] 2017-11-30 10:34 1.0.0 G Naive-Bayes naive-bayes N/A N/A N/A N/A N/A N/A
292 [anonymised] 2017-11-29 14:48 1.0.0 Attempt with word2vec N/A N/A N/A N/A 0.57634 N/A
304 [anonymised] 2017-11-26 23:38 1.0.0 LR N/A N/A N/A N/A N/A N/A
290 [anonymised] 2017-11-26 23:17 1.0.0 04b logistic regression ready-made v3 ready-made logistic-regression N/A N/A N/A N/A 0.50086 N/A
316 [anonymised] 2017-11-26 23:02 1.0.0 04b logistic regression ready-made v2 ready-made logistic-regression N/A N/A N/A N/A N/A N/A
286 [anonymised] 2017-11-26 22:40 1.0.0 LR ready-made logistic-regression N/A N/A N/A N/A 0.49918 N/A
284 [anonymised] 2017-11-26 22:35 1.0.0 Task 4. logistic-regression 0.60779 N/A 0.60323 N/A 0.60482 N/A
283 [anonymised] 2017-11-26 22:34 1.0.0 logistic regression from sklearn ready-made logistic-regression N/A N/A N/A N/A 0.49915 N/A
282 [anonymised] 2017-11-26 21:36 1.0.0 LR test N/A N/A N/A N/A N/A N/A
281 [anonymised] 2017-11-26 21:20 1.0.0 Logistic regression N/A N/A N/A N/A N/A N/A
280 [anonymised] 2017-11-26 21:09 1.0.0 Logistic regression N/A N/A N/A N/A N/A N/A
279 [anonymised] 2017-11-26 20:44 1.0.0 logistic regression test N/A N/A N/A N/A N/A N/A
278 [anonymised] 2017-11-26 20:02 1.0.0 Logistic regression self-made python, correct outs file self-made logistic-regression 0.53950 N/A 0.50202 N/A 0.57821 N/A
274 [anonymised] 2017-11-26 19:46 1.0.0 Logisitc regression, self made, python self-made logistic-regression 0.66180 N/A 0.65658 N/A 0.65381 N/A
276 [anonymised] 2017-11-26 19:29 1.0.0 Logistic regression python N/A N/A N/A N/A N/A N/A
275 [anonymised] 2017-11-26 19:20 1.0.0 my bad solution 2 N/A N/A N/A N/A N/A N/A
277 [anonymised] 2017-11-26 19:17 1.0.0 my bad solution self-made logistic-regression N/A N/A N/A N/A N/A N/A
253 [anonymised] 2017-11-25 19:29 1.0.0 test 0.51683 N/A 0.51195 N/A 0.51120 N/A
273 [anonymised] 2017-11-25 12:20 1.0.0 Selfmade Logical Regression self-made logistic-regression N/A N/A N/A N/A N/A N/A
261 [anonymised] 2017-11-24 19:32 1.0.0 logistic regression ready-made logistic-regression N/A N/A N/A N/A 0.61262 N/A
267 [anonymised] 2017-11-23 20:02 1.0.0 Self made Logistic Regression self-made logistic-regression N/A N/A N/A N/A N/A N/A
271 [anonymised] 2017-11-23 18:20 1.0.0 Linear regression on length 0.53752 N/A N/A N/A 0.50162 N/A
272 [anonymised] 2017-11-23 18:10 1.0.0 Logical regression on length ready-made logistic-regression 0.53752 N/A N/A N/A 0.50185 N/A
270 [anonymised] 2017-11-23 17:53 1.0.0 Logical regression on length 0.53752 N/A N/A N/A 0.50087 N/A
269 [anonymised] 2017-11-23 17:30 1.0.0 Logical regression on length 0.53752 N/A N/A N/A 0.50087 N/A
268 [anonymised] 2017-11-23 17:29 1.0.0 Logical regression on length 0.53752 N/A N/A N/A N/A N/A
266 [anonymised] 2017-11-23 17:28 1.0.0 Logical regression on length 0.53752 N/A N/A N/A N/A N/A
265 [anonymised] 2017-11-23 15:29 1.0.0 Logical regression files 0.53752 N/A N/A N/A 0.50762 N/A
264 [anonymised] 2017-11-20 17:45 1.0.0 Self made ngrams (ruby) self-made n-grams 0.53752 N/A N/A N/A 0.50762 N/A
263 [anonymised] 2017-11-20 17:06 1.0.0 Normalized by occurance 0.53752 N/A N/A N/A 0.50762 N/A
262 [anonymised] 2017-11-20 16:29 1.0.0 Normalization optimalization 0.53752 N/A N/A N/A 0.51110 N/A
285 kaczla 2017-11-20 16:16 1.0.0 Logistic regression self-made logistic-regression 0.66180 N/A 0.65658 N/A 0.65381 N/A
260 [anonymised] 2017-11-20 16:12 1.0.0 Trained on entire train 0.53752 N/A N/A N/A 0.51110 N/A
259 [anonymised] 2017-11-20 15:56 1.0.0 Remove trash 0.53752 N/A N/A N/A 0.50793 N/A
236 [anonymised] 2017-11-20 15:52 1.0.0 Add build model time counter 0.53752 N/A N/A N/A 0.50793 N/A
258 [anonymised] 2017-11-20 15:48 1.0.0 Code improvements 0.53752 N/A N/A N/A 0.50793 N/A
257 [anonymised] 2017-11-20 14:08 1.0.0 Add helpers 0.53752 N/A N/A N/A 0.50793 N/A
256 [anonymised] 2017-11-19 23:28 1.0.0 Scaled 0.53752 N/A N/A N/A 0.50793 N/A
255 [anonymised] 2017-11-19 23:22 1.0.0 Self-made ngrams (ruby) self-made 0.53752 N/A N/A N/A 0.55832 N/A
254 [anonymised] 2017-11-19 22:13 1.0.0 Self-made ngrams (ruby) self-made 0.53752 N/A N/A N/A 0.52906 N/A
252 [anonymised] 2017-11-19 21:42 1.0.0 Scaled to 1000000 0.53752 N/A N/A N/A 0.50323 N/A
251 [anonymised] 2017-11-19 21:36 1.0.0 Self made ngrams (ruby) scaled 1 to 10 0.53752 N/A N/A N/A 0.50373 N/A
250 [anonymised] 2017-11-19 21:22 1.0.0 Add normalization (ruby ngrams) 0.53752 N/A N/A N/A 0.51275 N/A
249 [anonymised] 2017-11-19 20:53 1.0.0 Self made ngrams (ruby) 0.53752 N/A N/A N/A 0.50780 N/A
248 [anonymised] 2017-11-19 20:47 1.0.0 Self-made ngrams (ruby) self-made 0.53752 N/A N/A N/A 0.51515 N/A
247 [anonymised] 2017-11-19 20:40 1.0.0 Self made n-grams (ruby) 0.53752 N/A N/A N/A 0.51755 N/A
246 [anonymised] 2017-11-19 20:26 1.0.0 Commiting splitter 0.53752 N/A N/A N/A 0.52828 N/A
245 [anonymised] 2017-11-19 20:23 1.0.0 Self-made ngrams (ruby) 0.53752 N/A N/A N/A 0.52828 N/A
244 [anonymised] 2017-11-19 20:22 1.0.0 Self-made ngrams (ruby) 0.53752 N/A N/A N/A N/A N/A
243 [anonymised] 2017-11-19 16:31 1.0.0 Self n-grams 0.53752 N/A N/A N/A N/A N/A
242 [anonymised] 2017-11-19 01:32 1.0.0 Self made ngrams 0.53752 N/A N/A N/A 0.51746 N/A
241 [anonymised] 2017-11-19 01:28 1.0.0 Self made ngrams 0.53752 N/A N/A N/A N/A N/A
238 [anonymised] 2017-11-19 01:27 1.0.0 Self made ngrams 0.53752 N/A N/A N/A N/A N/A
240 [anonymised] 2017-11-19 01:27 1.0.0 Self made ngrams 0.53752 N/A N/A N/A N/A N/A
239 [anonymised] 2017-11-19 01:25 1.0.0 Self made ngrams (ruby) 0.53752 N/A N/A N/A N/A N/A
237 [anonymised] 2017-11-13 17:27 1.0.0 Ruby 0.53752 N/A N/A N/A 0.52835 N/A
235 [anonymised] 2017-06-21 17:05 1.0.0 keras, tragiczne parametry neural-network 0.64476 N/A 0.64158 N/A 0.64147 N/A
233 [anonymised] 2017-06-12 13:28 1.0.0 prosty model jezyka, unix, vol8 self-made ready-made lm 0.57682 N/A 0.56149 N/A 0.56657 N/A
232 [anonymised] 2017-06-12 13:25 1.0.0 prosty model jezyka, unix, vol6 - nowe ratio, test #7 0.57682 N/A 0.56149 N/A 0.56277 N/A
231 [anonymised] 2017-06-12 13:19 1.0.0 prosty model jezyka, unix, vol6 - nowe ratio 0.57457 N/A 0.56149 N/A 0.56277 N/A
230 [anonymised] 2017-06-12 13:16 1.0.0 prosty model jezyka, unix, vol6 - nowe ratio, test #4 (zakres) 0.57457 N/A 0.50106 N/A N/A N/A
229 [anonymised] 2017-06-12 13:14 1.0.0 prosty model jezyka, unix, vol6 - nowe ratio, test #3 0.57459 N/A 0.50106 N/A N/A N/A
228 [anonymised] 2017-06-12 13:12 1.0.0 prosty model jezyka, unix, vol6 - nowe ratio, test #2 0.53362 N/A 0.50106 N/A N/A N/A
227 [anonymised] 2017-06-12 13:11 1.0.0 prosty model jezyka, unix, vol6 - nowe ratio, test #1 0.53072 N/A 0.50106 N/A N/A N/A
226 [anonymised] 2017-06-12 13:09 1.0.0 prosty model jezyka, unix, vol6 - nowe ratio 0.46553 N/A 0.50106 N/A N/A N/A
225 [anonymised] 2017-06-12 12:56 1.0.0 prosty model jezyka, vol5, unix 0.50741 N/A 0.50106 N/A N/A N/A
224 [anonymised] 2017-06-12 12:46 1.0.0 prosty model jezyka, vol4, unix 0.51963 N/A 0.50106 N/A N/A N/A
234 [anonymised] 2017-06-12 12:46 1.0.0 Bernoulli naive-bayes bernoulli python self-made N/A N/A N/A N/A 0.64037 N/A
223 [anonymised] 2017-06-12 12:44 1.0.0 prosty model jezyka, vol3, unix 0.51971 N/A 0.50106 N/A N/A N/A
222 [anonymised] 2017-06-12 12:41 1.0.0 prosty model jezyka, vol2, unix 0.48608 N/A 0.50106 N/A N/A N/A
221 [anonymised] 2017-06-12 12:38 1.0.0 prosty model jezyka, vol1, unix 0.48013 N/A 0.50106 N/A N/A N/A
220 [anonymised] 2017-06-12 09:31 1.0.0 Naive Bayes - Bernoulli naive-bayes bernoulli python self-made N/A N/A N/A N/A 0.63972 N/A
219 [anonymised] 2017-06-11 23:28 1.0.0 prosty model jezyka v4 0.50977 N/A 0.50106 N/A N/A N/A
217 [anonymised] 2017-06-11 23:20 1.0.0 prosty model jezyka v3 0.50977 N/A 0.50147 N/A 0.50155 N/A
216 [anonymised] 2017-06-11 23:18 1.0.0 prosty model jezyka v2 N/A N/A 0.50147 N/A 0.50155 N/A
215 [anonymised] 2017-06-11 23:13 1.0.0 prosty model jezyka v1 N/A N/A 0.50147 N/A 0.50155 N/A
218 p/tlen 2017-06-11 19:34 1.0.0 CNN, embeddings with more dimensions 0.68324 N/A 0.67808 N/A 0.67507 N/A
213 p/tlen 2017-06-11 05:26 1.0.0 simple convolutional network neural-network cnn 0.68111 N/A 0.67355 N/A 0.67189 N/A
214 [anonymised] 2017-06-04 13:45 1.0.0 lm self-made ready-made lm N/A N/A N/A N/A 0.62377 N/A
211 [anonymised] 2017-06-04 13:29 1.0.0 lm N/A N/A N/A N/A N/A N/A
210 [anonymised] 2017-06-04 11:14 1.0.0 em 0.66730 N/A 0.64996 N/A 0.65499 N/A
212 kaczla 2017-05-29 04:25 1.0.0 LSTM - remove one layer, simple lemmatizer neural-network 0.67703 N/A 0.67424 N/A 0.67083 N/A
205 kaczla 2017-05-27 17:08 1.0.0 LSTM - remove one layer, simple lemmatizer neural-network 0.64777 N/A 0.64211 N/A 0.64444 N/A
202 kaczla 2017-05-25 19:55 1.0.0 LSTM - decrease batch_size, 5 RNNs neural-network 0.70343 N/A 0.69886 N/A 0.69348 N/A
203 kaczla 2017-05-24 18:04 1.0.0 LSTM - decrease batch_size, 3 RNNs neural-network 0.70125 N/A 0.69679 N/A 0.69214 N/A
209 kaczla 2017-05-23 05:32 1.0.0 LSTM - remove one layer, 3 RNNs neural-network 0.70082 N/A 0.69814 N/A 0.69063 N/A
208 kaczla 2017-05-19 04:25 1.0.0 LSTM - remove one layer, decrease batch_size, epoch = 2 neural-network 0.69495 N/A 0.69329 N/A 0.68734 N/A
207 kaczla 2017-05-18 17:54 1.0.0 LSTM - remove one layer, decrease batch_size, epoch = 3 neural-network 0.68841 N/A 0.68476 N/A 0.68000 N/A
206 kaczla 2017-05-16 10:23 1.0.0 LSTM - epoch = 3 neural-network 0.68501 N/A 0.68359 N/A 0.67617 N/A
188 kaczla 2017-05-15 04:27 1.0.0 LSTM - decrease batch_size neural-network 0.69484 N/A 0.69201 N/A 0.68766 N/A
201 kaczla 2017-05-15 04:25 1.0.0 LSTM - decrease batch_size 0.69364 N/A 0.69189 N/A 0.68599 N/A
191 kaczla 2017-05-15 04:21 1.0.0 LSTM - remove one layer neural-network 0.69364 N/A 0.69189 N/A 0.68599 N/A
200 [anonymised] 2017-05-14 22:05 1.0.0 Bpe smalltrain 0.56843 N/A 0.64794 N/A N/A N/A
199 [anonymised] 2017-05-14 22:02 1.0.0 Keras smalltrain 0.56843 N/A 0.64794 N/A 0.49932 N/A
198 kaczla 2017-05-14 15:48 1.0.0 LSTM - remove one layer neural-network 0.69364 N/A 0.69189 N/A 0.68599 N/A
196 [anonymised] 2017-05-11 20:01 1.0.0 Trigram hard keywords that occured at least 13 times, when can't decide on hard keywords "F" is assigned, Answers based on hard keywords percentage: dev-0 6%, dev-1 7%, test-A 6% python self-made 0.51779 N/A 0.51906 N/A 0.51526 N/A
197 [anonymised] 2017-05-11 19:54 1.0.0 Trigram hard keywords that occured at least 13 times, when can't decide on hard keywords naive bayes is used, Answers based on hard keywords percentage: dev-0 6%, dev-1 7%, test-A 6% python self-made 0.67116 N/A 0.65394 N/A 0.65709 N/A
194 [anonymised] 2017-05-11 16:56 1.0.0 Bigram hard keywords that occured at least 17 times, when can't decide on hard keywords "F" is assigned, Based on hard keywords percentage: dev-0 12%, dev-1 13%, test-A 14% python self-made 0.53133 N/A 0.53295 N/A 0.53057 N/A
195 [anonymised] 2017-05-11 16:42 1.0.0 Bigram hard keywords that occured at least 17 times, when can't decide on hard keywords naive bayes is used, Based on hard keywords percentage: dev-0 12%, dev-1 13%, test-A 14% python self-made 0.67223 N/A 0.65568 N/A 0.65883 N/A
192 [anonymised] 2017-05-11 14:40 1.0.0 Bigram hard keywords that occured at least 5 times, when can't decide on hard keywords naive bayes is used, Based on hard keywords percantage: dev-0 59%, dev-1 57%, test-A 56% python self-made 0.64618 N/A 0.63862 N/A 0.63857 N/A
193 [anonymised] 2017-05-11 13:13 1.0.0 Bigram hard keywords that occured at least 5 times, when can't decide on hard keywords assings "F", Based on hard keywords percantage: dev-0 59%, dev-1 57%, test-A 56% python self-made 0.59141 N/A 0.58698 N/A 0.58315 N/A
189 [anonymised] 2017-05-04 19:08 1.0.0 1st try N/A N/A N/A N/A 0.59865 N/A
187 [anonymised] 2017-04-28 17:45 1.0.0 Hard keywords based solution ver 2. If can't decide based on hard keywords naive bayes is used. Percentage of answers based on keywords: dev-0 10%, dev-1 9%, test-A 8%. Only words with count 3 and bigger are considered in hard keyword based approach. python self-made 0.66285 N/A 0.64877 N/A 0.65190 N/A
190 [anonymised] 2017-04-28 17:26 1.0.0 Hard keywords based solution ver 1. If can't decide based on hard keywords naive bayes is used. Percentage of answers based on keywords: dev-0 22%, dev-1 19%, test-A 20% python self-made 0.65111 N/A 0.64067 N/A 0.64489 N/A
204 p/tlen 2017-04-25 19:49 1.0.0 5 RNNs combined 0.70079 N/A 0.69568 N/A 0.69044 N/A
185 p/tlen 2017-04-24 05:36 1.0.0 fasttext combined with KenLM 0.71653 N/A 0.70503 N/A 0.69295 N/A
186 p/tlen 2017-04-23 17:02 1.0.0 LSTM (by Nozdi) 0.69433 N/A 0.68978 N/A 0.68382 N/A
180 p/tlen 2017-04-23 10:35 1.0.0 fasttext word 2-ngrams, 10x buckets, character 3-6-ngrams 0.70222 N/A 0.69351 N/A 0.68632 N/A
179 p/tlen 2017-04-23 08:15 1.0.0 fasttext word 2-ngrams, 10x buckets, character 3-6-ngrams 0.70222 N/A N/A N/A N/A N/A
177 p/tlen 2017-04-23 06:53 1.0.0 fasttext word 2-ngrams, 10x buckets, character 3-6-ngrams 0.69423 N/A 0.68672 N/A 0.67830 N/A
184 p/tlen 2017-04-22 20:26 1.0.0 fasttext with word 2-grams and 10x buckets ready-made fasttext 0.69322 N/A 0.68578 N/A 0.67851 N/A
183 p/tlen 2017-04-22 19:42 1.0.0 fasttext with word 2-grams ready-made fasttext 0.68593 N/A 0.67887 N/A 0.67183 N/A
182 p/tlen 2017-04-22 19:34 1.0.0 fasttext (baseline) ready-made fasttext 0.67711 N/A 0.66870 N/A 0.66623 N/A
181 kaczla 2017-04-15 16:18 1.0.0 Vowpal Wabbit vowpal-wabbit ready-made 0.67142 N/A 0.66639 N/A 0.66109 N/A
178 kaczla 2017-04-10 13:26 1.0.0 KenLM ready-made kenlm lm 0.67077 N/A 0.66102 N/A 0.65053 N/A
176 kaczla 2017-04-10 13:07 1.0.0 Vowpal Wabbit vowpal-wabbit ready-made 0.67013 N/A 0.66531 N/A 0.66036 N/A
167 [anonymised] 2017-04-04 15:19 1.0.0 bayes with simple stemming fix naive-bayes python self-made 0.65368 N/A 0.63479 N/A 0.64012 N/A
169 [anonymised] 2017-04-04 13:48 1.0.0 bayes with simple stemming 0.56540 N/A 0.56040 N/A 0.56282 N/A
172 [anonymised] 2017-04-03 21:08 1.0.0 bayes tf-idf (classic) naive-bayes python self-made 0.59090 N/A 0.58922 N/A 0.58420 N/A
173 [anonymised] 2017-04-03 20:54 1.0.0 dev-0 tf-idf test (big change) 0.54156 N/A 0.66063 N/A 0.65417 N/A
174 [anonymised] 2017-04-03 20:07 1.0.0 dev-0 tf-idf test (small change) 0.58224 N/A 0.66063 N/A 0.65417 N/A
155 [anonymised] 2017-04-01 17:45 1.0.0 logistic regression 40 epoch 0.66230 N/A 0.66063 N/A 0.65417 N/A
152 [anonymised] 2017-04-01 13:38 1.0.0 dev-0 tf-idf test 0.59090 N/A 0.66089 N/A 0.65494 N/A
148 kaczla 2017-03-31 21:52 1.0.0 Vowpal Wabbit vowpal-wabbit ready-made 0.65301 N/A 0.64660 N/A 0.64337 N/A
159 [anonymised] 2017-03-31 17:30 1.0.0 logistic regression 20 epoch python self-made logistic-regression 0.66397 N/A 0.66089 N/A 0.65494 N/A
168 kaczla 2017-03-27 20:29 1.0.0 Logistic regression python self-made logistic-regression 0.66180 N/A 0.65658 N/A 0.65381 N/A
170 [anonymised] 2017-03-27 20:11 1.0.0 logistic regression python self-made logistic-regression N/A N/A N/A N/A 0.59865 N/A
175 [anonymised] 2017-03-27 18:29 1.0.0 logistic regression 10 epoch python self-made logistic-regression 0.66355 N/A 0.66069 N/A 0.65399 N/A
156 [anonymised] 2017-03-27 16:03 1.0.0 logistic regression 1 epoch python self-made logistic-regression 0.65032 N/A 0.64632 N/A 0.63895 N/A
171 [anonymised] 2017-03-27 13:21 1.0.0 Regresja python self-made logistic-regression N/A N/A N/A N/A 0.62472 N/A
146 [anonymised] 2017-03-27 13:20 1.0.0 Regresja N/A N/A N/A N/A N/A N/A
145 [anonymised] 2017-03-27 13:19 1.0.0 Regresja N/A N/A N/A N/A N/A N/A
144 [anonymised] 2017-03-27 13:14 1.0.0 Regresja N/A N/A N/A N/A 0.63928 N/A
165 [anonymised] 2017-03-27 13:07 1.0.0 reg logistyczna 10 epok - shuffle self-made logistic-regression 0.63823 N/A 0.63671 N/A 0.62985 N/A
150 [anonymised] 2017-03-27 11:08 1.0.0 without feature engineering, Adaptive Moment Estimation, 49 epoch. discriminative better than generative python self-made logistic-regression 0.67127 N/A 0.66687 N/A 0.66120 N/A
164 [anonymised] 2017-03-27 10:32 1.0.0 reg logistyczna 10 epok self-made logistic-regression 0.62059 N/A 0.61890 N/A 0.61450 N/A
163 [anonymised] 2017-03-26 23:22 1.0.0 reg logistyczna 1 epoka self-made logistic-regression 0.59625 N/A 0.59012 N/A 0.58915 N/A
162 [anonymised] 2017-03-26 23:17 1.0.0 reg logistyczna 1 epoka, mały zbiór uczący v2 0.66669 N/A 0.64823 N/A 0.58915 N/A
161 [anonymised] 2017-03-26 22:51 1.0.0 reg logistyczna 1 epoka, mały zbiór uczący 0.66669 N/A 0.64823 N/A 0.50767 N/A
160 [anonymised] 2017-03-23 08:23 1.0.0 22 epoch, simple SGD with stupid annealing, need to make better SGD, without feature engineering python self-made logistic-regression 0.66878 N/A 0.66422 N/A 0.65814 N/A
153 [anonymised] 2017-03-20 19:43 1.0.0 Bernoulli Naive Bayes 1 naive-bayes bernoulli python self-made 0.65483 N/A 0.63717 N/A 0.64269 N/A
154 [anonymised] 2017-03-20 16:28 1.0.0 Logistic Haskell haskell self-made logistic-regression 0.61675 N/A 0.61432 N/A 0.61065 N/A
151 [anonymised] 2017-03-16 17:27 1.0.0 bayes + tf_idf 0.59461 N/A 0.59014 N/A 0.58846 N/A
158 [anonymised] 2017-03-16 12:37 1.0.0 corrected bayes naive-bayes multinomial python self-made 0.66665 N/A 0.64844 N/A 0.65369 N/A
157 [anonymised] 2017-03-15 14:05 1.0.0 sckit-learn naive bayes naive-bayes python ready-made scikit-learn 0.66680 N/A 0.64842 N/A 0.65394 N/A
143 [anonymised] 2017-03-13 08:36 1.0.0 TurboHaskell 2010 v2 0.66435 N/A 0.70540 N/A 0.65029 N/A
149 [anonymised] 2017-03-11 15:54 1.0.0 TurboHaskell 2010 naive-bayes multinomial haskell self-made 0.66912 N/A 0.64996 N/A 0.65531 N/A
142 [anonymised] 2017-03-11 03:25 1.0.0 Test 0.58665 N/A 0.58153 N/A 0.57822 N/A
141 [anonymised] 2017-03-11 02:59 1.0.0 Test 0.59857 N/A 0.59280 N/A 0.58933 N/A
140 [anonymised] 2017-03-11 02:15 1.0.0 Test 0.62323 N/A 0.61270 N/A 0.60889 N/A
139 [anonymised] 2017-03-11 01:16 1.0.0 Test 0.59528 N/A 0.59049 N/A 0.58699 N/A
138 [anonymised] 2017-03-11 00:44 1.0.0 Test 0.63650 N/A 0.62513 N/A 0.62066 N/A
137 [anonymised] 2017-03-11 00:26 1.0.0 Test 0.63455 N/A 0.62364 N/A 0.61931 N/A
136 [anonymised] 2017-03-11 00:19 1.0.0 Test 0.63425 N/A 0.62240 N/A 0.61862 N/A
135 [anonymised] 2017-03-10 23:48 1.0.0 Test N/A N/A 0.52997 N/A N/A N/A
134 [anonymised] 2017-03-09 17:44 1.0.0 Test 0.66364 N/A 0.64468 N/A 0.64945 N/A
166 [anonymised] 2017-03-09 17:38 1.0.0 Naive Bayes naive-bayes multinomial self-made perl 0.66521 N/A 0.64534 N/A 0.65043 N/A
127 [anonymised] 2017-03-09 17:18 1.0.0 Test 0.64469 N/A 0.62934 N/A 0.62802 N/A
126 [anonymised] 2017-03-09 17:03 1.0.0 Yolo 0.64314 N/A 0.62835 N/A 0.62709 N/A
125 [anonymised] 2017-03-09 16:23 1.0.0 Test 0.63938 N/A 0.62525 N/A 0.62369 N/A
124 [anonymised] 2017-03-09 16:16 1.0.0 Test 0.64379 N/A 0.62851 N/A 0.62740 N/A
123 [anonymised] 2017-03-09 15:51 1.0.0 Test 0.64366 N/A 0.62845 N/A 0.62752 N/A
122 [anonymised] 2017-03-09 15:24 1.0.0 Test 0.64358 N/A 0.62858 N/A 0.62751 N/A
121 [anonymised] 2017-03-09 14:53 1.0.0 Yolo 0.64420 N/A 0.62867 N/A 0.62784 N/A
120 [anonymised] 2017-03-09 14:33 1.0.0 Test 0.54233 N/A 0.53734 N/A 0.53638 N/A
119 [anonymised] 2017-03-07 02:31 1.0.0 Haskell na resorach 0.66344 N/A 0.64638 N/A 0.64971 N/A
91 [anonymised] 2017-03-02 23:49 1.0.0 I can see that I'll have to teach you how to be villains! naive-bayes multinomial self-made regexp lisp 0.56843 N/A 0.64794 N/A 0.65479 N/A
118 [anonymised] 2017-03-02 23:35 1.0.0 Throw it at him, not me! 0.56843 N/A 0.64794 N/A 0.65375 N/A
117 [anonymised] 2017-03-02 23:16 1.0.0 Back to old corpora 0.56843 N/A 0.64794 N/A 0.65450 N/A
116 [anonymised] 2017-03-02 23:00 1.0.0 Change of preprocessing 0.56843 N/A 0.64794 N/A 0.65031 N/A
115 [anonymised] 2017-03-02 21:48 1.0.0 Próba raz dwa czy 0.56843 N/A 0.64794 N/A 0.64935 N/A
114 [anonymised] 2017-03-02 13:01 1.0.0 Test N/A N/A N/A N/A 0.62362 N/A
113 [anonymised] 2017-03-02 12:22 1.0.0 Yolo N/A N/A N/A N/A 0.50288 N/A
112 [anonymised] 2017-03-02 12:11 1.0.0 Yolo N/A N/A N/A N/A 0.50381 N/A
99 [anonymised] 2017-03-02 12:08 1.0.0 Yolo N/A N/A N/A N/A 0.00000 N/A
98 [anonymised] 2017-03-02 11:15 1.0.0 Now look at this net that I just found; when I say go... 0.56843 N/A 0.64794 N/A 0.65331 N/A
88 [anonymised] 2017-03-02 10:54 1.0.0 Now look at this net that I just found 0.56843 N/A N/A N/A 0.65331 N/A
87 [anonymised] 2017-03-02 10:44 1.0.0 Now look at this net 0.56843 N/A N/A N/A 0.34669 N/A
86 [anonymised] 2017-03-02 08:19 1.0.0 Yolo N/A N/A N/A N/A 0.50288 N/A
85 [anonymised] 2017-03-02 08:10 1.0.0 Yolo N/A N/A N/A N/A 0.50374 N/A
133 [anonymised] 2017-03-01 11:40 1.0.0 bayes3 naive-bayes multinomial python self-made 0.50157 N/A 0.50408 N/A 0.49981 N/A
109 [anonymised] 2017-03-01 11:04 1.0.0 bayes2 0.49982 N/A 0.50048 N/A 0.49941 N/A
108 [anonymised] 2017-03-01 07:13 1.0.0 Haskell 0.63596 N/A 0.61912 N/A 0.62383 N/A
147 [anonymised] 2017-02-28 23:51 1.0.0 something is no yes :X naive-bayes multinomial python self-made N/A N/A N/A N/A 0.63928 N/A
107 [anonymised] 2017-02-28 22:42 1.0.0 test N/A N/A N/A N/A N/A N/A
106 [anonymised] 2017-02-28 21:47 1.0.0 something is no yes :X N/A N/A N/A N/A N/A N/A
105 [anonymised] 2017-02-28 21:37 1.0.0 bayes1 N/A N/A N/A N/A N/A N/A
104 [anonymised] 2017-02-28 21:13 1.0.0 bayes solution1 0.50033 N/A 0.50155 N/A 0.50085 N/A
103 [anonymised] 2017-02-28 19:32 1.0.0 naiwen bajesen, changed equation 0.66582 N/A 0.64740 N/A 0.65173 N/A
90 [anonymised] 2017-02-28 19:19 1.0.0 naiwen bajesen naive-bayes multinomial python self-made 0.66600 N/A 0.64745 N/A 0.65224 N/A
128 kaczla 2017-02-28 18:33 1.0.0 Rozwiązanie naive-bayes multinomial python self-made 0.66092 N/A 0.64342 N/A 0.65071 N/A
102 [anonymised] 2017-02-28 17:06 1.0.0 Rozwiązanie 3 naive-bayes multinomial self-made java 0.66669 N/A 0.64823 N/A 0.65482 N/A
100 [anonymised] 2017-02-28 16:44 1.0.0 Rozwiązanie 2 N/A N/A N/A N/A 0.50006 N/A
97 [anonymised] 2017-02-28 15:35 1.0.0 Swag 0.61095 N/A 0.59919 N/A 0.60005 N/A
96 [anonymised] 2017-02-28 15:04 1.0.0 Yolo N/A N/A N/A N/A 0.62326 N/A
95 [anonymised] 2017-02-28 10:44 1.0.0 Yolo N/A N/A N/A N/A 0.62268 N/A
101 [anonymised] 2017-02-27 23:17 1.0.0 Rozwiązanie 1 N/A N/A N/A N/A N/A N/A
94 [anonymised] 2017-02-27 17:57 1.0.0 First N/A N/A N/A N/A 0.53074 N/A
93 [anonymised] 2017-02-27 17:44 1.0.0 First N/A N/A N/A N/A 0.53376 N/A
92 [anonymised] 2017-02-27 17:31 1.0.0 First N/A N/A N/A N/A 0.52212 N/A
132 [anonymised] 2017-02-27 17:22 1.0.0 moje rozwiazanie 1 stupid python self-made 0.50123 N/A N/A N/A 0.50068 N/A
129 [anonymised] 2017-02-27 16:23 1.0.0 regexPro stupid python self-made regexp 0.50033 N/A 0.50155 N/A 0.50085 N/A
131 [anonymised] 2017-02-27 16:21 1.0.0 test stupid python self-made regexp 0.50241 N/A 0.50147 N/A 0.50155 N/A
110 [anonymised] 2017-02-24 08:31 1.0.0 Simple regexp solution stupid self-made regexp 0.52190 N/A 0.51948 N/A 0.51246 N/A
89 [anonymised] 2017-02-21 16:58 1.0.0 test simple solution 0.52869 N/A 0.53085 N/A 0.52200 N/A
130 p/tlen 2017-01-26 10:08 1.0.0 KenLM + Vowpal Wabbit vowpal-wabbit 0.71473 N/A 0.70513 N/A 0.69379 N/A
111 [anonymised] 2017-01-08 20:31 1.0.0 Punct split v2 kenlm 0.66486 N/A 0.65639 N/A 0.64260 N/A
84 [anonymised] 2017-01-08 15:16 1.0.0 KenLM punctuation.split 0.64351 N/A 0.63973 N/A 0.62437 N/A
83 [anonymised] 2016-12-27 14:04 1.0.0 Train LM 3 grams & tokenize 0.99425 N/A 0.63660 N/A 0.64909 N/A
82 [anonymised] 2016-12-27 14:00 1.0.0 LM 4grams female 0.99425 N/A 0.63660 N/A 0.62213 N/A
81 [anonymised] 2016-12-27 13:55 1.0.0 Train LM improvement 0.99425 N/A 0.63660 N/A 0.53150 N/A
79 [anonymised] 2016-12-27 13:46 1.0.0 Train LM improvement 0.99425 N/A 0.63660 N/A 0.58043 N/A
78 [anonymised] 2016-12-27 10:22 1.0.0 Kenml devs & train LM & remove punct kenlm 0.99425 N/A 0.63660 N/A 0.65591 N/A
77 [anonymised] 2016-12-27 10:17 1.0.0 Kenml devs & train LM 0.99425 N/A 0.63660 N/A 0.65591 N/A
75 [anonymised] 2016-12-27 01:21 1.0.0 2 w nocy -> wystarczy 0.98007 N/A 0.97880 N/A 0.64758 N/A
73 [anonymised] 2016-12-27 01:16 1.0.0 2 w nocy -> wystarczy 0.98007 N/A 0.97880 N/A 0.53478 N/A
80 [anonymised] 2016-12-27 01:09 1.0.0 kenml & dict v2 0.98007 N/A 0.97880 N/A 0.63106 N/A
72 [anonymised] 2016-12-27 00:57 1.0.0 kenml & dict 0.98007 N/A 0.97880 N/A 0.59847 N/A
74 [anonymised] 2016-12-27 00:43 1.0.0 kenml train LM 0.98007 N/A 0.97880 N/A 0.64909 N/A
71 [anonymised] 2016-12-27 00:39 1.0.0 kenml v4 0.98007 N/A 0.97880 N/A 0.64758 N/A
70 [anonymised] 2016-12-27 00:32 1.0.0 Kenml v3 0.98007 N/A 0.97880 N/A 0.64758 N/A
69 [anonymised] 2016-12-27 00:19 1.0.0 Kenml v2 0.98007 N/A 0.97880 N/A 0.62256 N/A
68 [anonymised] 2016-12-27 00:04 1.0.0 Kenml v2 0.98007 N/A 0.97880 N/A N/A N/A
67 [anonymised] 2016-12-26 23:58 1.0.0 Kenml v2 0.98007 N/A 0.97880 N/A N/A N/A
66 [anonymised] 2016-12-26 23:54 1.0.0 Kenml v2 0.98007 N/A 0.97880 N/A N/A N/A
76 [anonymised] 2016-12-26 23:25 1.0.0 Kenml v1 0.98007 N/A 0.97880 N/A 0.62129 N/A
65 [anonymised] 2016-12-07 09:31 1.0.0 sama 0.51523 N/A N/A N/A 0.50463 N/A
64 [anonymised] 2016-12-07 09:24 1.0.0 v2 0.51523 N/A N/A N/A 0.51408 N/A
62 [anonymised] 2016-12-05 22:38 1.0.0 extra rules, information about each rule accuracy 0.50095 N/A N/A N/A N/A N/A
61 [anonymised] 2016-12-05 21:59 1.0.0 silly mistake in adding stuff twice to out 0.50091 N/A N/A N/A N/A N/A
60 [anonymised] 2016-12-05 21:50 1.0.0 Dydlojn zaliczony? N/A N/A N/A N/A N/A N/A
59 [anonymised] 2016-12-05 00:26 1.0.0 Womendict ver.3 0.51991 N/A N/A N/A 0.51516 N/A
58 [anonymised] 2016-12-05 00:03 1.0.0 Womendict ver.2 0.52001 N/A N/A N/A 0.51494 N/A
57 [anonymised] 2016-12-04 23:43 1.0.0 Womendict ver.2 0.51547 N/A N/A N/A 0.51278 N/A
56 [anonymised] 2016-12-03 23:50 1.0.0 First submission - Womendict 0.51460 N/A N/A N/A 0.51278 N/A
55 [anonymised] 2016-12-03 23:35 1.0.0 First submission - Womendict 0.51460 N/A N/A N/A N/A N/A
54 [anonymised] 2016-12-03 23:32 1.0.0 First submission - Womendict 0.51460 N/A N/A N/A N/A N/A
53 [anonymised] 2016-12-03 23:06 1.0.0 proste rozwiazanie N/A N/A 0.51687 N/A 0.51754 N/A
52 [anonymised] 2016-12-03 18:15 1.0.0 First submission - Womendict N/A N/A N/A N/A N/A N/A
44 [anonymised] 2016-12-01 12:40 1.0.0 p3 0.49753 N/A N/A N/A 0.50251 N/A
50 [anonymised] 2016-12-01 12:36 1.0.0 2ga proba 0.50351 N/A N/A N/A 0.50251 N/A
25 [anonymised] 2016-12-01 12:29 1.0.0 pp N/A N/A N/A N/A N/A N/A
49 [anonymised] 2016-12-01 02:45 1.0.0 kenlm first attempt 0.99640 N/A 0.99542 N/A 0.65047 N/A
48 [anonymised] 2016-11-30 14:38 1.0.0 Poprawki w ./runD.py 0.52735 N/A 0.52362 N/A 0.52521 N/A
47 [anonymised] 2016-11-30 13:49 1.0.0 Push z plikami - wersja słownikowa 0.52735 N/A 0.52362 N/A 0.52521 N/A
45 [anonymised] 2016-11-30 13:46 1.0.0 Test plikow 0.52735 N/A 0.52362 N/A 0.52521 N/A
46 [anonymised] 2016-11-30 10:32 1.0.0 KenLM z Train'a* 0.64377 N/A 0.52363 N/A 0.62182 N/A
43 [anonymised] 2016-11-30 10:30 1.0.0 KenLM z Train'a 0.64377 N/A 0.52363 N/A 0.52520 N/A
63 [anonymised] 2016-11-30 10:28 1.0.0 merged v2 0.54357 N/A N/A N/A 0.53326 N/A
42 [anonymised] 2016-11-30 10:27 1.0.0 merged v1 N/A N/A N/A N/A 0.53326 N/A
41 [anonymised] 2016-11-30 10:26 1.0.0 merged Mieszko & Maciej solution N/A N/A N/A N/A 0.53326 N/A
51 [anonymised] 2016-11-30 09:28 1.0.0 dict v1 0.51523 N/A N/A N/A 0.51408 N/A
40 [anonymised] 2016-11-30 09:26 1.0.0 Słownik na Trainie 0.52735 N/A 0.52363 N/A 0.52520 N/A
38 [anonymised] 2016-11-28 16:23 1.0.0 Women - interpunction 0.53752 N/A N/A N/A 0.52835 N/A
36 [anonymised] 2016-11-28 08:57 1.0.0 KenLM 3gram 0.98726 N/A 0.98495 N/A 0.58469 N/A
35 [anonymised] 2016-11-28 07:57 1.0.0 KenLM 1st Try 0.98843 N/A 0.98664 N/A 0.58520 N/A
34 [anonymised] 2016-11-26 14:53 1.0.0 Best On test-A** 0.61496 N/A 0.53644 N/A 0.53855 N/A
33 [anonymised] 2016-11-26 14:48 1.0.0 Best on test-A 0.77005 N/A 0.73899 N/A 0.53038 N/A
32 [anonymised] 2016-11-26 14:35 1.0.0 Best on devs 0.77007 N/A 0.73899 N/A 0.53038 N/A
31 [anonymised] 2016-11-26 14:19 1.0.0 _ 0.63512 N/A 0.61667 N/A 0.52541 N/A
30 [anonymised] 2016-11-26 13:25 1.0.0 El Dictioannte finallo 0.67131 N/A 0.64660 N/A 0.53131 N/A
29 [anonymised] 2016-11-26 12:40 1.0.0 Dic v4 cleaning + tr improve 0.77005 N/A 0.73899 N/A 0.53040 N/A
28 [anonymised] 2016-11-26 10:50 1.0.0 Dic v3 0.61498 N/A 0.53645 N/A 0.53853 N/A
27 [anonymised] 2016-11-25 19:33 1.0.0 Dictionary version over 9000 small cleaning 0.60219 N/A 0.53713 N/A 0.52968 N/A
26 [anonymised] 2016-11-25 19:02 1.0.0 Dictionary version over 9000 dev-1 0.59657 N/A 0.52953 N/A 0.52968 N/A
24 [anonymised] 2016-11-25 18:58 1.0.0 Dictionary version over 9000 0.59657 N/A N/A N/A 0.52968 N/A
23 [anonymised] 2016-11-23 20:08 1.0.0 Women dictionary v3 0.53190 N/A N/A N/A 0.52321 N/A
22 [anonymised] 2016-11-23 19:56 1.0.0 Women dictionary v2 0.52793 N/A N/A N/A 0.52035 N/A
21 [anonymised] 2016-11-23 19:47 1.0.0 Women dictionary 0.52408 N/A N/A N/A 0.51827 N/A
20 [anonymised] 2016-11-23 19:31 1.0.0 Only men v3 0.51677 N/A N/A N/A 0.50993 N/A
19 [anonymised] 2016-11-23 17:41 1.0.0 Only men - bigger dictionary 0.51156 N/A N/A N/A 0.50724 N/A
37 [anonymised] 2016-11-23 14:06 1.0.0 words v1 N/A N/A N/A N/A N/A N/A
18 [anonymised] 2016-11-22 23:15 1.0.0 Dictionary - only women 0.50000 N/A N/A N/A 0.50000 N/A
17 [anonymised] 2016-11-22 23:07 1.0.0 "First attempt - dictionary" 0.50867 N/A N/A N/A 0.50562 N/A
16 [anonymised] 2016-11-22 21:10 1.0.0 test submition (all F) 0.50000 N/A N/A N/A 0.50000 N/A
15 [anonymised] 2016-11-22 18:29 1.0.0 female + male dict 0.53915 N/A N/A N/A 0.53150 N/A
14 [anonymised] 2016-11-22 18:14 1.0.0 male + female dict N/A N/A N/A N/A 0.53001 N/A
13 [anonymised] 2016-11-20 17:14 1.0.0 add swears 0.53714 N/A N/A N/A 0.53001 N/A
12 [anonymised] 2016-11-20 17:07 1.0.0 add swears N/A N/A N/A N/A 0.53001 N/A
39 [anonymised] 2016-11-20 09:42 1.0.0 dict v4 0.54134 N/A N/A N/A 0.53208 N/A
11 [anonymised] 2016-11-19 23:15 1.0.0 dict v3 0.53816 N/A N/A N/A 0.52971 N/A
10 [anonymised] 2016-11-19 22:18 1.0.0 improve dict v2 0.53785 N/A N/A N/A 0.52971 N/A
9 [anonymised] 2016-11-19 22:13 1.0.0 improve dict 0.53785 N/A N/A N/A 0.52399 N/A
8 [anonymised] 2016-11-19 20:07 1.0.0 Dictionary approach 0.52699 N/A N/A N/A 0.52399 N/A
7 p/tlen 2016-11-15 09:29 1.0.0 trivial baseline (only female) 0.50000 N/A 0.50000 N/A 0.50000 N/A

Submission graph

Graphs by parameters

eval_batch_size

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evaluate_during_training_steps

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num_train_epochs

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save_steps

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seq_len

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train_batch_size

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