Universal Sentence Encoder
Reported on 5 benchmarks across 3 tasks · 2 papers
Note: results are matched by exact model name. Different papers may use the same name for different model variants.
Natural Language Processing3 results
- Soft-F1· 2019-11-100.38best: 0.84 (Human baseline)
- Pearson Correlation· 2018-10-220.345best: 0.871 (Supervised combination of: Jaccard, Q-gram, sent2vec, Paragraph vector DM, skip-thoughts, fastText)
- Pearson Correlation· 2018-10-220.714best: 0.767 (BioSentVec (PubMed + MIMIC-III))
Methodology2 results
- Pearson Correlation· 2018-10-220.345best: 0.871 (Supervised combination of: Jaccard, Q-gram, sent2vec, Paragraph vector DM, skip-thoughts, fastText)
- Pearson Correlation· 2018-10-220.714best: 0.767 (BioSentVec (PubMed + MIMIC-III))