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
Download tokenizer.json from PerRing/e5_ko: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/PerRing/e5_ko/resolve/main/tokenizer.json
- Command line
-
hf download hf://PerRing/e5_ko/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/PerRing/e5_ko/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 900d575674d5bfcd5c2b9059c9221a42f92c957ed7230f7e096d3e9580c3e4c2
- Size of remote file:
- 17.1 MB
- SHA256:
- 883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
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