Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:3210
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use hoin1218/bge-m3-business-compact-card-context-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hoin1218/bge-m3-business-compact-card-context-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hoin1218/bge-m3-business-compact-card-context-v2") sentences = [ "말및양사육업사업체1 말 및 양 사육업 영업본부 고객사 방문 국내 평일", "콜센터및텔레마케팅서비스업사업체1 콜센터 및 텔레마케팅 서비스업 워크숍 준비 참석자 지원 비용", "광고영화및비디오물제작업사업체1 광고 영화 및 비디오물 제작업 영업본부 고객사 방문 국내 평일", "도장및기타피막처리업사업체1 도장 및 기타 피막처리업 재무팀 월말 결산 법인카드" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 658 Bytes
16055e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
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"XLMRobertaModel"
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