SWEM-max

Reported on 3 benchmarks across 1 task · 1 paper

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
% Test Accuracy· 2018-05-24
83.8
best: 94.7 (UnitedSynT5 (3B))
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms arXiv:1805.09843
Natural Language InferenceonMultiNLI
Matched· 2018-05-24
68.2
best: 92.6 (Turing NLR v5 XXL 5.4B (fine-tuned))
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms arXiv:1805.09843
Natural Language InferenceonMultiNLI
Mismatched· 2018-05-24
67.7
best: 92.4 (Turing NLR v5 XXL 5.4B (fine-tuned))
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms arXiv:1805.09843