ko sroberta multitask 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, KorNLI 학습 데이터셋으로 멀티 태스크 학습을 진행한 후 KorSTS 평가 데이터셋으로 평가한 결과입니다. Cosine Pearson: 84.77 Cosine Spearman: 85.60 Euclidean Pearson: 83.71 Euclidean Spearman: 84.40 Manhattan Pearson: 83.70 Manhattan Spearman: 84.38 Dot Pearson: 82.42 Dot Spearman: 82.33 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: DataLoader : torch.utils.data.dataloader.DataLoad…
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