Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
Generated from Trainer
dataset_size:10501
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use roomnumber103/embedding-BOK with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use roomnumber103/embedding-BOK with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("roomnumber103/embedding-BOK") sentences = [ "추운날이니 외출은 자제해주시기 바랍니다.", "추운날인데 외출하지마", "소·돼지에 대해서만 실시하던 축산물이력제가 1월 1일부터 닭·오리·계란까지 확대·시행된다.", "광고메일함 비중이 에어비앤비가 더 높니 트립닷컴이 더 많니?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 296 Bytes
7324130 | 1 2 3 4 5 6 7 8 9 10 | {
"word_embedding_dimension": 768,
"pooling_mode_cls_token": false,
"pooling_mode_mean_tokens": true,
"pooling_mode_max_tokens": false,
"pooling_mode_mean_sqrt_len_tokens": false,
"pooling_mode_weightedmean_tokens": false,
"pooling_mode_lasttoken": false,
"include_prompt": true
} |