HyperAI초신경
홈뉴스최신 연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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HyperAI초신경

Sentence Embeddings For Biomedical Texts On

평가 지표

Pearson Correlation

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
Pearson Correlation
Paper TitleRepository
Universal Sentence Encoder0.345BioSentVec: creating sentence embeddings for biomedical texts
Q-gram (q = 3)0.723Neural sentence embedding models for semantic similarity estimation in the biomedical domain-
Paragraph Vector0.787BIOSSES: A Semantic Sentence Similarity Estimation System for the Biomedical Domain-
Paragraph vector (PV-DBOW)0.804Neural sentence embedding models for semantic similarity estimation in the biomedical domain-
BioSentVec (MIMIC-III)0.350BioSentVec: creating sentence embeddings for biomedical texts
Supervised combination of: Jaccard, Q-gram, sent2vec, Paragraph vector DM, skip-thoughts, fastText0.871Neural sentence embedding models for semantic similarity estimation in the biomedical domain-
Sent2vec0.798Neural sentence embedding models for semantic similarity estimation in the biomedical domain-
Skip-thoughts0.485Neural sentence embedding models for semantic similarity estimation in the biomedical domain-
fastText (skip-gram, max pooling)0.766Neural sentence embedding models for semantic similarity estimation in the biomedical domain-
BioSentVec (PubMed + MIMIC-III)0.795BioSentVec: creating sentence embeddings for biomedical texts
Paragraph vector (PV-DM)0.819Neural sentence embedding models for semantic similarity estimation in the biomedical domain-
Unsupervised combination (mean) of: Jaccard, q-gram, Paragraph vector (PV-DBOW) and sent2vec0.846Neural sentence embedding models for semantic similarity estimation in the biomedical domain-
fastText (CBOW, max pooling)0.253Neural sentence embedding models for semantic similarity estimation in the biomedical domain-
BioSentVec (PubMed)0.817BioSentVec: creating sentence embeddings for biomedical texts
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