ko sbert nli 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 KorNLI 학습 데이터셋으로 학습한 후 KorSTS 평가 데이터셋으로 평가한 결과입니다. Cosine Pearson: 82.24 Cosine Spearman: 83.16 Euclidean Pearson: 82.19 Euclidean Spearman: 82.31 Manhattan Pearson: 82.18 Manhattan Spearman: 82.30 Dot Pearson: 79.30 Dot Spearman: 78.78 Training The model was trained with the parameters: DataLoader : sentence transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader of length 8885 with parameters: Loss : sentence transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss with parameters: Parameters of the fit() Method: Full Model Architecture Citing & Authors Ham,…
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