Sentiment by emoticons challenge
Give the probability of a positive sentiment for a short Polish text. [ver. 1.0.1]
Git repo URL: git://gonito.net/sentiment-by-emoticons / Branch: master
Run git clone --single-branch git://gonito.net/sentiment-by-emoticons -b master to get the challenge data
Browse at https://gonito.net/gitlist/sentiment-by-emoticons.git/master
Leaderboard
| # | submitter | when | ver. | description | test-A Accuracy | test-A LogLoss | × | |
|---|---|---|---|---|---|---|---|---|
| 1 | kubapok | 2020-05-21 13:15 | 1.0.0 | roberta polish large finetune | N/A | 33.838 | 1 | |
| 2 | [anonymized] | 2018-05-14 15:18 | 1.0.0 | Vowpal Wabbit -b 27 --ngram 3 vowpal-wabbit logistic-regression | N/A | 47.412 | 12 | |
| 3 | [anonymized] | 2018-05-13 20:33 | 1.0.0 | ISI2018-024 - changed some parameters in vw model generation; changed flattening vowpal-wabbit logistic-regression | N/A | 47.525 | 11 | |
| 4 | [anonymized] | 2018-05-13 21:31 | 1.0.0 | vw | N/A | 47.659 | 18 | |
| 5 | [anonymized] | 2018-05-13 22:10 | 1.0.0 | vowpal wabbit vowpal-wabbit logistic-regression | N/A | 47.690 | 9 | |
| 6 | [anonymized] | 2018-05-21 11:47 | 1.0.0 | improved vowpa vowpal-wabbit logistic-regression | N/A | 48.053 | 11 | |
| 7 | [anonymized] | 2018-05-13 20:41 | 1.0.0 | vowpal-wabbit proba 1 vowpal-wabbit logistic-regression | N/A | 48.636 | 9 | |
| 8 | [anonymized] | 2018-05-13 22:53 | 1.0.0 | vowpal wabbit - 2 vowpal-wabbit logistic-regression | N/A | 49.103 | 13 | |
| 9 | [anonymized] | 2018-05-13 17:25 | 1.0.0 | Volkswagen vowpal-wabbit logistic-regression | N/A | 49.399 | 6 | |
| 10 | [anonymized] | 2018-05-29 09:26 | 1.0.0 | task fix vowpal-wabbit logistic-regression | N/A | 54.511 | 5 | |
| 11 | p/tlen | 2018-05-07 09:43 | 1.0.0 | baseline Vowpal Wabbit + simple flattening probabilities vowpal-wabbit | N/A | 54.537 | 6 | |
| 12 | [anonymized] | 2018-05-27 17:04 | 1.0.0 | vowpal wabbit stemmed model 2 vowpal-wabbit logistic-regression stemming | N/A | 54.669 | 7 | |
| 13 | [anonymized] | 2018-06-18 09:06 | 1.0.0 | LSTM v4 all/500/515880(1)/256, 1 epoch neural-network | N/A | 54.739 | 40 | |
| 14 | [anonymized] | 2018-05-27 21:48 | 1.0.0 | fasttext predict-prob fasttext | N/A | 55.637 | 16 | |
| 15 | [anonymized] | 2018-06-05 23:21 | 1.0.0 | Zadanie029 wersja poprawiona vowpal-wabbit logistic-regression | N/A | 57.464 | 10 | |
| 16 | [anonymized] | 2018-05-20 22:52 | 1.0.0 | implent logistic regression with some ready-made solution (but not Vowpal Wabbit) ready-made logistic-regression | N/A | 57.783 | 27 | |
| 17 | [anonymized] | 2019-01-27 14:36 | 1.0.0 | dampie5 solution v4.2 | N/A | 58.272 | 14 | |
| 18 | [anonymized] | 2019-01-27 07:37 | 1.0.0 | dodanie zaktualizowanego pliku python | N/A | 58.559 | 11 | |
| 19 | [anonymized] | 2019-01-27 07:50 | 1.0.0 | s402267 - sentiment-by-emoticons | N/A | 58.839 | 1 | |
| 20 | [anonymized] | 2019-01-27 09:31 | 1.0.0 | Init | N/A | 58.839 | 1 | |
| 21 | [anonymized] | 2018-04-21 21:36 | 1.0.0 | corrected | N/A | 58.845 | 5 | |
| 22 | [anonymized] | 2018-04-20 15:06 | 1.0.0 | naive-bayes partially-trained | N/A | 58.881 | 2 | |
| 23 | [anonymized] | 2019-01-27 08:38 | 1.0.0 | Brylantowa 4 | N/A | 59.052 | 5 | |
| 24 | [anonymized] | 2018-05-14 15:14 | 1.0.0 | Logistic regresion with sklearn ready-made logistic-regression | N/A | 59.692 | 16 | |
| 25 | [anonymized] | 2019-01-26 20:14 | 1.0.0 | proba2 testA zadanie2 | N/A | 60.223 | 10 | |
| 26 | [anonymized] | 2019-01-26 20:50 | 1.0.0 | Rozwiązanie zadania Sentiment by emoticons challenge | N/A | 60.473 | 1 | |
| 27 | [anonymized] | 2019-02-21 17:22 | 1.0.0 | Second solution | N/A | 60.789 | 2 | |
| 28 | [anonymized] | 2019-01-27 11:13 | 1.0.0 | Wersja milion | N/A | 61.030 | 4 | |
| 29 | [anonymized] | 2019-01-27 09:42 | 1.0.0 | dssad | N/A | 61.050 | 9 | |
| 30 | [anonymized] | 2019-01-26 17:40 | 1.0.0 | kd solution | N/A | 61.486 | 1 | |
| 31 | [anonymized] | 2019-01-26 17:24 | 1.0.0 | mk | N/A | 61.500 | 1 | |
| 32 | [anonymized] | 2019-01-27 08:56 | 1.0.0 | my second challenge solution | N/A | 61.509 | 1 | |
| 33 | [anonymized] | 2019-01-26 20:13 | 1.0.0 | My brilliant solution | N/A | 61.551 | 15 | |
| 34 | [anonymized] | 2019-01-26 17:54 | 1.0.0 | my solution | N/A | 61.579 | 2 | |
| 35 | [anonymized] | 2019-01-24 12:32 | 1.0.0 | rubinowa 8 | N/A | 62.092 | 5 | |
| 36 | [anonymized] | 2018-04-23 08:25 | 1.0.0 | Saving model to file naive-bayes multinomial python self-made | N/A | 62.494 | 28 | |
| 37 | [anonymized] | 2018-05-29 10:47 | 1.0.0 | LogLoss and Accuracy charts naive-bayes analysis | N/A | 62.914 | 30 | |
| 38 | [anonymized] | 2018-04-23 17:20 | 1.0.0 | Fix lowerCase naive-bayes multinomial self-made java | N/A | 63.241 | 3 | |
| 39 | [anonymized] | 2018-06-12 14:34 | 1.0.0 | zad40 kenlm lm | N/A | 66.313 | 1 | |
| 40 | [anonymized] | 2018-06-14 09:38 | 1.0.0 | Poprawione zadanie 40 kenlm lm | N/A | 66.621 | 2 | |
| 41 | [anonymized] | 2018-06-14 10:05 | 1.0.0 | KenLM edited kenlm lm | N/A | 67.716 | 2 | |
| 42 | [anonymized] | 2018-05-07 15:57 | 1.0.0 | test included stupid better-than-no-model-baseline | N/A | 69.165 | 3 | |
| 43 | [anonymized] | 2019-01-26 19:27 | 1.0.0 | solution2 | N/A | 78.228 | 4 | |
| 44 | [anonymized] | 2018-06-07 20:26 | 1.0.0 | ready logistic regression | N/A | Infinity | 12 | |
| 45 | [anonymized] | 2019-01-24 21:14 | 1.0.0 | test2 | N/A | Infinity | 7 |