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>
논문
MSE: 0.2532Pearson Correlation: 0.8676Spearman Correlation: 0.8083
Pearson Correlation: 0.9363
Pearson Correlation: 0.848
Macro F1: 85.6
Pearson Correlation: 85.62
MSE: 0.3053Pearson Correlation: 0.8381Spearman Correlation: 0.7621
MSE: 0.2687Pearson Correlation: 0.8584Spearman Correlation: 0.7916