F1: 89.75Precision: 88.93Recall: 90.76
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Semantic Similarity
The main objective Semantic Similarity is to measure the distance between the semantic meanings of a pair of words, phrases, sentences, or documents. For example, the word “car” is more similar to “bus” than it is to “cat”. The two main approaches to measuring Semantic Similarity are knowledge-based approaches and corpus-based, distributional methods.
<span class="description-source">Source: Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection </span>
개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.
벤치마크
Papers with Code 아카이브의 데이터셋별 기록 예시입니다. 평가 조건과 지표 방향이 검증되지 않아 순위를 의미하지 않습니다.
Dataset기록된 모델 예시MetricPaperCode
BioBERT (pre-trained on PubMed abstracts + PMC, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")
F1: 93.38Precision: 92.98Recall: 93.85
논문
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BioBERT
(pre-trained on PubMed abstracts + PMC, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")
F1: 89.75Precision: 88.93Recall: 90.76
논문
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데이터셋
28개논문
MSE: 0.2532Pearson Correlation: 0.8676Spearman Correlation: 0.8083
Pearson Correlation: 85.62
MSE: 0.3053Pearson Correlation: 0.8381Spearman Correlation: 0.7621
MSE: 0.2687Pearson Correlation: 0.8584Spearman Correlation: 0.7916
Pearson Correlation: 0.848
라이브러리
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