Sentence Similarity
sentence-transformers
Safetensors
Transformers
xlm-roberta
feature-extraction
korean
Eval Results (legacy)
text-embeddings-inference
Instructions to use upskyy/e5-base-korean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use upskyy/e5-base-korean with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("upskyy/e5-base-korean") sentences = [ "이집트 군대가 형제애를 단속하다", "이집트의 군대가 무슬림 형제애를 단속하다", "아르헨티나의 기예르모 코리아와 네덜란드의 마틴 버커크의 또 다른 준결승전도 매력적이다.", "그것이 사실일 수도 있다고 생각하는 것은 재미있다." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use upskyy/e5-base-korean with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("upskyy/e5-base-korean") model = AutoModel.from_pretrained("upskyy/e5-base-korean", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |