Guess a word in a gap in historic texts
Give a probability distribution for a word in a gap in a corpus of Polish historic texts spanning 1814-2013. This is a challenge for (temporal) language models. [ver. 1.0.0]
Git repo URL: git://gonito.net/retro-gap.git / Branch: master
Run git clone --single-branch git://gonito.net/retro-gap.git -b master to get the challenge data
Browse at https://gonito.net/gitlist/retro-gap.git/master
Leaderboard
| # | submitter | when | ver. | description | test-A LogLossHashed | × | |
|---|---|---|---|---|---|---|---|
| 1 | kubapok | 2020-10-09 10:56 | 1.0.0 | roberta z embeddingiem pierwszy token | 4.2801 | 19 | |
| 2 | p/tlen | 2019-04-12 04:40 | 1.0.0 | LM model used (bi-transformer.bin) aggregator=GEO lm word-level transformer | 4.8570 | 49 | |
| 3 | kaczla | 2017-12-12 20:35 | 1.0.0 | 3-gram with prune, best 15, best oov ready-made kenlm lm | 5.7006 | 20 | |
| 4 | [anonymized] | 2018-01-24 14:39 | 1.0.0 | simple neural network, context 2 words ahead 2 words behind neural-network | 5.7395 | 4 | |
| 5 | [anonymized] | 2017-04-24 16:42 | 1.0.0 | unigramy, n=100, v3 self-made lm | 6.0733 | 10 | |
| 6 | [anonymized] | 2021-02-08 06:15 | 1.0.0 | solution self-made lm | 6.0733 | 3 | |
| 7 | [anonymized] | 2021-01-08 18:34 | 1.0.0 | pytorch neural ngram model (3 previous words) lm pytorch-nn | 6.0819 | 8 | |
| 8 | [anonymized] | 2018-01-15 18:11 | 1.0.0 | Bigrams model, 100 best words stupid self-made lm bigram | 6.1097 | 3 | |
| 9 | [anonymized] | 2020-12-04 00:27 | 1.0.0 | solution self-made lm bigram | 6.1610 | 3 | |
| 10 | [anonymized] | 2019-11-27 10:19 | 1.0.0 | Simple bigram model lm | 6.1802 | 3 | |
| 11 | [anonymized] | 2020-12-16 07:16 | 1.0.0 | python bigram self-made lm bigram | 6.1837 | 1 | |
| 12 | [anonymized] | 2017-06-29 15:12 | 1.0.0 | Update source code; kenlm order=3 tokenizer.perl from moses. best 100 results, text mode. ready-made kenlm lm | 6.1898 | 7 | |
| 13 | [anonymized] | 2021-01-09 21:10 | 1.0.0 | 2 left, 2 right context lm pytorch-nn | 6.2379 | 3 | |
| 14 | [anonymized] | 2021-02-03 07:57 | 1.0.0 | updated bigram self-made lm bigram | 6.2673 | 10 | |
| 15 | [anonymized] | 2021-01-13 02:38 | 1.0.0 | v10 lm temporal pytorch-nn | 6.3330 | 44 | |
| 16 | [anonymized] | 2021-01-13 01:51 | 1.0.0 | following_words;x_size=100;epochs=5;lr=0.001 lm pytorch-nn | 6.3331 | 8 | |
| 17 | [anonymized] | 2021-01-27 10:23 | 1.0.0 | TAU22 lm pytorch-nn | 6.4151 | 3 | |
| 18 | [anonymized] | 2020-12-08 16:27 | 1.0.0 | solution self-made lm bigram | 6.4201 | 4 | |
| 19 | [anonymized] | 2021-01-12 17:36 | 1.0.0 | first solution 1 epoch 1000 texts best 15 lm pytorch-nn | 6.5711 | 1 | |
| 20 | [anonymized] | 2020-12-02 13:04 | 1.0.0 | Trigram slef-made self-made lm trigram | 6.7172 | 2 | |
| 21 | [anonymized] | 2019-11-20 17:07 | 1.0.0 | better bigram solution, nananana lm | 6.7249 | 2 | |
| 22 | [anonymized] | 2019-11-13 12:29 | 1.0.0 | My bigram guess a word solution lm | 6.7309 | 1 | |
| 23 | [anonymized] | 2020-12-16 08:52 | 1.0.0 | poprawka tetragram self-made lm tetragram | 6.7517 | 3 | |
| 24 | [anonymized] | 2019-11-30 22:48 | 1.0.0 | 3gram outfile format fix lm trigram | 6.8032 | 2 | |
| 25 | [anonymized] | 2017-05-16 04:31 | 1.0.0 | zad 16 self-made lm | 6.8056 | 4 | |
| 26 | [anonymized] | 2017-06-28 08:47 | 1.0.0 | test 2 ready-made neural-network | 6.8956 | 2 | |
| 27 | [anonymized] | 2021-01-12 17:48 | 1.0.0 | run.py update lm pytorch-nn | 6.9054 | 10 | |
| 28 | [anonymized] | 2021-02-04 20:29 | 1.0.0 | ngram lm pytorch-nn | 6.9123 | 1 | |
| 29 | [anonymized] | 2020-12-08 15:50 | 1.0.0 | finally self-made lm bigram | 6.9236 | 5 | |
| 30 | [anonymized] | 2020-01-16 18:12 | 1.0.0 | IRLSTM 3-gram lm | 6.9314 | 25 | |
| 31 | [anonymized] | 2020-12-13 14:17 | 1.0.0 | solution self-made lm trigram | 7.5152 | 3 |