Sentence Embeddings For Biomedical Texts On 4
평가 지표
F1
Precision
Recall
평가 결과
이 벤치마크에서 각 모델의 성능 결과
모델 이름 | F1 | Precision | Recall | Paper Title | Repository |
|---|---|---|---|---|---|
| SciBERT uncased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus") | 91.51 | 91.3 | 91.79 | Measuring semantic similarity of clinical trial outcomes using deep pre-trained language representations | - |
| BERT-Base uncased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus") | 89.16 | 89.31 | 89.12 | Measuring semantic similarity of clinical trial outcomes using deep pre-trained language representations | - |
| BioBERT (pre-trained on PubMed abstracts + PMC, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus") | 93.38 | 92.98 | 93.85 | Measuring semantic similarity of clinical trial outcomes using deep pre-trained language representations | - |
| BERT-Base cased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus") | 89.12 | 88.25 | 90.1 | Measuring semantic similarity of clinical trial outcomes using deep pre-trained language representations | - |
| SciBERT cased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus") | 90.69 | 89 | 92.54 | Measuring semantic similarity of clinical trial outcomes using deep pre-trained language representations | - |
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