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
| { | |
| "architectures": [ | |
| "XLMRobertaModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 8194, | |
| "model_type": "xlm-roberta", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "transformers_version": "4.57.6", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 250002 | |
| } | |