ko sbert sts This is a sentence transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Usage (Sentence Transformers) Using this model becomes easy when you have sentence transformers installed: Then you can use the model like this: Usage (HuggingFace Transformers) Without sentence transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling operation on top of the contextualized word embeddings. Evaluation Results KorSTS 학습 데이터셋으로 학습한 후 KorSTS 평가 데이터셋으로 평가한 결과입니다. Cosine Pearson: 81.55 Cosine Spearman: 81.23 Euclidean Pearson: 79.94 Euclidean Spearman: 79.79 Manhattan Pearson: 79.90 Manhattan Spearman: 79.75 Dot Pearson: 76.02 Dot Spearman: 75.31 Training The model was trained with the parameters: DataLoader : torch.utils.data.dataloader.DataLoader of length 719 with parameters: Loss : sentence transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss Parameters of the fit() Method: Full Model Architecture Citing & Authors Ham, J., Choe, Y. J., Park, K., Choi, I., & Soh, H. (2020). Kornli and korst…
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