LTG-BERT-base 98M

Reported on 4 benchmarks across 2 tasks · 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 Processing4 results

Linguistic AcceptabilityonCoLA
Accuracy· 2023-11-03
82.7
SOTA
Not all layers are equally as important: Every Layer Counts BERT arXiv:2311.02265
Natural Language InferenceonRTE
Accuracy· 2023-11-03
54.7
best: 86.8 (PSQ (Chen et al., 2020))
Not all layers are equally as important: Every Layer Counts BERT arXiv:2311.02265
Natural Language InferenceonMultiNLI
Matched· 2023-11-03
83
best: 92.6 (Turing NLR v5 XXL 5.4B (fine-tuned))
Not all layers are equally as important: Every Layer Counts BERT arXiv:2311.02265
Natural Language InferenceonMultiNLI
Mismatched· 2023-11-03
83.4
best: 92.4 (Turing NLR v5 XXL 5.4B (fine-tuned))
Not all layers are equally as important: Every Layer Counts BERT arXiv:2311.02265