Ntumpha

Reported on 3 benchmarks across 1 task · 1 paper · 1 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing3 results

Natural Language InferenceonSNLI
Parameters· 2019-01-31
220
best: 355 (Neural Tree Indexers for Text Understanding)
SOTA
Multi-Task Deep Neural Networks for Natural Language Understanding arXiv:1901.11504
Natural Language InferenceonSNLI
% Test Accuracy· 2019-01-31
90.5
best: 94.7 (UnitedSynT5 (3B))
Multi-Task Deep Neural Networks for Natural Language Understanding arXiv:1901.11504
Natural Language InferenceonSNLI
% Train Accuracy· 2019-01-31
99.1
best: 99.7 (+ Unigram and bigram features)
Multi-Task Deep Neural Networks for Natural Language Understanding arXiv:1901.11504