https://github.com/BM K/Sentence Embedding is all you need Korean Sentence Embedding 🍭 Korean sentence embedding repository. You can download the pre trained models and inference right away, also it provides environments where individuals can train models. Quick tour Performance Semantic Textual Similarity test set results Model AVG Cosine Pearson Cosine Spearman Euclidean Pearson Euclidean Spearman Manhattan Pearson Manhattan Spearman Dot Pearson Dot Spearman : : : : : : : : : : : : : : : : : : KoSBERT † SKT 77.40 78.81 78.47 77.68 77.78 77.71 77.83 75.75 75.22 KoSBERT 80.39 82.13 82.25 80.67 80.75 80.69 80.78 77.96 77.90 KoSRoBERTa 81.64 81.20 82.20 81.79 82.34 81.59 82.20 80.62 81.25 KoSentenceBART 77.14 79.71 78.74 78.42 78.02 78.40 78.00 74.24 72.15 KoSentenceT5 77.83 80.87 79.74 80.24 79.36 80.19 79.27 72.81 70.17 KoSimCSE BERT † SKT 81.32 82.12 82.56 81.84 81.63 81.99 81.74 79.55 79.19 KoSimCSE BERT 83.37 83.22 83.58 83.24 83.60 83.15 83.54 83.13 83.49 KoSimCSE RoBERTa 83.65 83.60 83.77 83.54 83.76 83.55 83.77 83.55 83.64 KoSimCSE BERT multitask 85.71 85.29 86.02 85.63 86.01 85.57 85.97 85.26 85.93 KoSimCSE RoBERTa multitask 85.77 85.08 86.12 85.84 86.12 85.83 86.12 85.03 8…
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