RetroC temporal classification challenge
Guess the publication year of a Polish text. [ver. 1.0.0]
This is a long list of all submissions, if you want to see only the best, click leaderboard.
| # | submitter | when | ver. | description | dev-0 RMSE | test-A RMSE | |
|---|---|---|---|---|---|---|---|
| 27 | kubapok | 2020-01-04 23:30 | 1.0.0 | xgb standard | 33.4 | 39.9 | |
| 24 | kubapok | 2020-01-04 23:24 | 1.0.0 | lr | 24.6 | 36.9 | |
| 56 | [anonymized] | 2017-05-29 15:18 | 1.0.0 | Basic linear regression haskell self-made linear-regression | 85.0 | 85.4 | |
| 57 | [anonymized] | 2017-05-28 13:55 | 1.0.0 | 50 ep ,00001 lr, 1814-2014 self-made linear-regression | N/A | 93.3 | |
| 59 | [anonymized] | 2017-05-19 19:18 | 1.0.0 | 20 ep ,00005 lr | N/A | 396.9 | |
| 58 | [anonymized] | 2017-05-19 18:52 | 1.0.0 | 10 ep ,0001 lr self-made linear-regression | N/A | 393.3 | |
| 32 | [anonymized] | 2016-06-11 14:29 | 1.0.0 | Max entropy solution for 4 grams and 5 class | 31.5 | 41.0 | |
| 30 | [anonymized] | 2016-06-11 14:09 | 1.0.0 | neural network, 4grams, 8 class | 31.5 | 41.0 | |
| 31 | [anonymized] | 2016-06-11 14:03 | 1.0.0 | GLM | 31.5 | 41.0 | |
| 29 | [anonymized] | 2016-06-11 13:16 | 1.0.0 | GLM solution for 4 grams and 8 class | 31.5 | 41.0 | |
| 2 | [anonymized] | 2016-05-29 14:08 | 1.0.0 | VW -nn 6 on up to 4-grams and [5-7] tokens + wiki years | 17.1 | 24.9 | |
| 55 | [anonymized] | 2016-05-29 10:35 | 1.0.0 | Wiki years - min year from article, with [184-2013] | 81.8 | 82.9 | |
| 60 | [anonymized] | 2016-05-29 08:21 | 1.0.0 | Wiki years | 103.1 | N/A | |
| 63 | [anonymized] | 2016-05-29 07:48 | 1.0.0 | Wiki years | 103.1 | N/A | |
| 9 | [anonymized] | 2016-05-25 13:13 | 1.0.0 | VW -nn 6 on up to 5-grams and [6-8] tokens | 17.9 | 26.4 | |
| 7 | [anonymized] | 2016-05-25 11:40 | 1.0.0 | Current best solution with changed learning rate | 17.3 | 25.5 | |
| 22 | [anonymized] | 2016-05-13 13:21 | 1.0.0 | Max entropy solution for 4 grams and 8 class | 26.6 | 35.8 | |
| 23 | [anonymized] | 2016-05-13 13:16 | 1.0.0 | Solution for 4grams and 8 class | 24.7 | 36.8 | |
| 25 | [anonymized] | 2016-05-07 21:29 | 1.0.0 | Solution for 4grams and 10 class | 24.7 | 37.4 | |
| 26 | [anonymized] | 2016-05-07 21:25 | 1.0.0 | Solution for 4grams and 20 class | 25.1 | 38.4 | |
| 28 | [anonymized] | 2016-04-30 18:20 | 1.0.0 | Solution for 4grams and 40 class | 26.7 | 40.3 | |
| 33 | [anonymized] | 2016-04-29 20:21 | 1.0.0 | Solution for 4grams and 67 class | 27.3 | 41.6 | |
| 34 | [anonymized] | 2016-04-29 07:27 | 1.0.0 | Solution for 4grams and 100 class | 28.4 | 41.8 | |
| 35 | [anonymized] | 2016-04-22 11:32 | 1.0.0 | Solution for 4grams | 30.2 | 43.2 | |
| 39 | [anonymized] | 2016-04-20 12:19 | 1.0.0 | Solution | 30.2 | 44.6 | |
| 62 | [anonymized] | 2016-01-07 07:56 | 1.0.0 | Po najczęściej występujących słowach | N/A | N/A | |
| 21 | p/tlen | 2015-12-19 11:07 | 1.0.0 | VW classifier (10-year chronon) | 24.5 | 34.4 | |
| 54 | [anonymized] | 2015-12-16 23:38 | 1.0.0 | first submission | N/A | 75.0 | |
| 10 | [anonymized] | 2015-12-16 20:40 | 1.0.0 | another try with more bits | 16.7 | 27.2 | |
| 46 | [anonymized] | 2015-12-16 20:13 | 1.0.0 | + script | 34.7 | 50.3 | |
| 45 | [anonymized] | 2015-12-14 08:01 | 1.0.0 | tfidf, min_df = 3 | 34.7 | 50.3 | |
| 53 | [anonymized] | 2015-12-13 22:16 | 1.0.0 | test with min_df = 3 | 41.5 | 60.1 | |
| 13 | [anonymized] | 2015-12-13 18:50 | 1.0.0 | Simple 2-layer network with up to 4 chars n-grams | 16.8 | 27.6 | |
| 1 | p/tlen | 2015-12-13 14:31 | 1.0.0 | VW -nn 6 on up to 4-grams and [5-7] tokens vowpal-wabbit neural-network | 17.2 | 24.8 | |
| 5 | p/tlen | 2015-12-13 12:50 | 1.0.0 | VW -nn 6 on up to 4-grams and [5-8] tokens | 17.2 | 25.3 | |
| 42 | p/tlen | 2015-12-13 12:49 | 1.0.0 | VW -nn 6 on up to 4-grams and [5-8] tokens | 45.9 | 46.5 | |
| 37 | [anonymized] | 2015-12-13 11:52 | 1.0.0 | Corrected better solution | 31.9 | 44.4 | |
| 36 | [anonymized] | 2015-12-13 11:43 | 1.0.0 | Maybe better solution | N/A | 44.4 | |
| 3 | [anonymized] | 2015-12-12 22:02 | 1.0.0 | The same as last, best epoch | 16.3 | 24.9 | |
| 61 | [anonymized] | 2015-12-12 15:56 | 1.0.0 | test samego dev-0 dla okrojonego treningu | 61.5 | N/A | |
| 6 | p/tlen | 2015-12-12 15:12 | 1.0.0 | up to 4-grams (VW with -nn 6 found with vw-hypersearch) | 17.2 | 25.5 | |
| 41 | p/tlen | 2015-12-12 15:10 | 1.0.0 | up to 4-grams (VW with -nn 6 found with vw-hypersearch) | 45.9 | 46.5 | |
| 4 | [anonymized] | 2015-12-11 19:49 | 1.0.0 | Same as lat, word2vec pretraining | 16.4 | 25.3 | |
| 11 | [anonymized] | 2015-12-11 19:37 | 1.0.0 | 200 hidden units in GRU, more epochs, full batches | 17.5 | 27.3 | |
| 38 | [anonymized] | 2015-12-11 16:32 | 1.0.0 | First solution | 30.2 | 44.6 | |
| 8 | p/tlen | 2015-12-10 21:15 | 1.0.0 | up to 4-grams (VW with -nn 10) | 17.7 | 26.0 | |
| 12 | [anonymized] | 2015-12-09 08:46 | 1.0.0 | 900 words, more units, RMSprop | 18.5 | 27.5 | |
| 44 | p/tlen | 2015-12-08 20:18 | 1.0.0 | signi tempori (with poor man's stemming by taking the first 7 letters) | 48.1 | 50.2 | |
| 15 | [anonymized] | 2015-12-07 21:26 | 1.0.0 | Better regression layer, 10 year bins weighted average | 19.6 | 30.0 | |
| 20 | [anonymized] | 2015-12-07 14:58 | 1.0.0 | just one epoch, to avoid overfitting | 23.3 | 33.8 |
Showing 1 to 50 of 63 entries