Representation Learning on BIOSSES

Metric: Pearson Correlation (higher is better)

LeaderboardDataset

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#	Model↕	Pearson Correlation▼	Augmentations	Paper	Date↕	Code
1	Supervised combination of: Jaccard, Q-gram, sent2vec, Paragraph vector DM, skip-thoughts, fastText	0.871	No	Neural sentence embedding models for semantic si...	2021-10-01	Code
2	Unsupervised combination (mean) of: Jaccard, q-gram, Paragraph vector (PV-DBOW) and sent2vec	0.846	No	Neural sentence embedding models for semantic si...	2021-10-01	Code
3	Paragraph vector (PV-DM)	0.819	No	Neural sentence embedding models for semantic si...	2021-10-01	Code
4	BioSentVec (PubMed)	0.817	Yes	BioSentVec: creating sentence embeddings for bio...	2018-10-22	Code
5	Paragraph vector (PV-DBOW)	0.804	No	Neural sentence embedding models for semantic si...	2021-10-01	Code
6	Sent2vec	0.798	No	Neural sentence embedding models for semantic si...	2021-10-01	Code
7	BioSentVec (PubMed + MIMIC-III)	0.795	Yes	BioSentVec: creating sentence embeddings for bio...	2018-10-22	Code
8	Paragraph Vector	0.787	Yes	-	-	-
9	fastText (skip-gram, max pooling)	0.766	No	Neural sentence embedding models for semantic si...	2021-10-01	Code
10	Q-gram (q = 3)	0.723	No	Neural sentence embedding models for semantic si...	2021-10-01	Code
11	Skip-thoughts	0.485	No	Neural sentence embedding models for semantic si...	2021-10-01	Code
12	BioSentVec (MIMIC-III)	0.35	Yes	BioSentVec: creating sentence embeddings for bio...	2018-10-22	Code
13	Universal Sentence Encoder	0.345	No	BioSentVec: creating sentence embeddings for bio...	2018-10-22	Code
14	fastText (CBOW, max pooling)	0.253	No	Neural sentence embedding models for semantic si...	2021-10-01	Code