klue sroberta base continue learning by mnr EU AI Act Compliance Profile

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:

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer("bespin-global/klue-sroberta-base-continue-learning-by-mnr")
embeddings = model.encode(sentences)
print(embeddings)

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.

from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_p

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