bespin global/klue sroberta base continue learning by mnr 해당 모델은 KLUE/NLI, KLUE/STS 데이터셋을 활용하였으며, sentence transformers의 공식 문서 내 소개된 continue learning 방법을 통해 아래와 같이 학습되었습니다. 1. NLI 데이터셋을 통해 nagative sampling 후, MultipleNegativeRankingLoss를 활용하여 1차 NLI training 수행 2. 1에서 학습완료 된 모델에 STS 데이터셋을 통해, CosineSimilarityLoss를 활용하여 2차 STS training 수행 학습에 관한 자세한 내용은 Blog와 Colab 실습 코드를 참고해주세요. 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 EmbeddingSimilarityEvaluator: Evaluating the model on sts test dataset: Cosine Similarity : Pearson: 0.8901 Spearman: 0.8893 Manhattan Distance: Pearson: 0.8867 Spearman: 0.8818 Euclidean Distance: Pearson: 0.8875 Spearman: 0.88…
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