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Models/T5-11B

T5-11B

Reported on 16 benchmarks across 8 tasks · 1 paper · 8 SOTA

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

Natural Language Processing13 results

Question AnsweringonSQuAD1.1 dev
EM· uses extra data· 2019-10-23
90.06
SOTA
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683
Question AnsweringonSQuAD1.1 dev
F1· uses extra data· 2019-10-23
95.64
best: 95.77 (XLNet+DSC)
SOTA
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683
Question AnsweringonMultiRC
EM· 2019-10-23
63.3
best: 69.2 (PaLM 540B (finetuned) )
SOTA
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683
Common Sense ReasoningonReCoRD
F1· 2019-10-23
94.1
best: 96.4 (Turing NLR v5 XXL 5.4B (fine-tuned))
SOTA
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683
Natural Language InferenceonMultiNLI
Mismatched· 2019-10-23
91.7
best: 92.4 (Turing NLR v5 XXL 5.4B (fine-tuned))
SOTA
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683
Sentiment AnalysisonSST-2 Binary classification
Accuracy· 2019-10-23
97.5
SOTA
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683
Document SummarizationonCNN / Daily Mail
ROUGE-2· uses extra data· 2019-10-23
21.55
best: 22.55 (PEGASUS + SummaReranker)
SOTA
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683
Document SummarizationonCNN / Daily Mail
ROUGE-L· uses extra data· 2019-10-23
40.69
best: 45.35 (Scrambled code + broken (alter))
SOTA
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683
Machine TranslationonWMT2014 English-German
BLEU score· 2019-10-23
32.1
best: 35.14 (Transformer Cycle (Rev))
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683
Semantic Textual SimilarityonMRPC
F1· 2019-10-23
91.9
best: 92.5 (T5-3B)
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683
Semantic Textual SimilarityonSTS Benchmark
Pearson Correlation· 2019-10-23
0.925
best: 0.929 (MT-DNN-SMART)
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683
Semantic Textual SimilarityonSTS Benchmark
Spearman Correlation· 2019-10-23
0.921
best: 0.931 (Mnet-Sim)
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683
Document SummarizationonCNN / Daily Mail
ROUGE-1· uses extra data· 2019-10-23
43.52
best: 48.18 (Scrambled code + broken (alter))
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683

Knowledge Base3 results

Text SummarizationonCNN / Daily Mail
ROUGE-1· uses extra data· 2019-10-23
43.52
best: 48.18 (Scrambled code + broken (alter))
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683
Text SummarizationonCNN / Daily Mail
ROUGE-2· uses extra data· 2019-10-23
21.55
best: 24.02 (Pegasus)
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683
Text SummarizationonCNN / Daily Mail
ROUGE-L· uses extra data· 2019-10-23
40.69
best: 45.35 (Scrambled code + broken (alter))
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683