Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:49962
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use PerRing/e5_ko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use PerRing/e5_ko with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("PerRing/e5_ko") sentences = [ "query: 잡지는 인종차별적이고 성차별적인 유머를 제공했다.", "passage: 잡지는 인종과 성별에 대해 편견적인 농담을 실었다.", "passage: 잡지는 모든 사람을 존중하는 유머를 제공했다.", "passage: 그들은 중요하지만 활용도가 낮은 데이터를 보유하고 있었다." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 297 Bytes
3db5861 | 1 2 3 4 5 6 7 8 9 10 | {
"word_embedding_dimension": 1024,
"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
} |