Sentence Similarity
sentence-transformers
Safetensors
Transformers
xlm-roberta
feature-extraction
korean
Eval Results (legacy)
text-embeddings-inference
Instructions to use upskyy/e5-large-korean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use upskyy/e5-large-korean with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("upskyy/e5-large-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-large-korean with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("upskyy/e5-large-korean") model = AutoModel.from_pretrained("upskyy/e5-large-korean", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add exported onnx model 'model.onnx'
#1
by Epbox - opened
Hello!
This pull request has been automatically generated from the Sentence Transformers backend-export Space.
Pull Request overview
- Add exported ONNX model
model.onnx.
Tip:
Consider testing this pull request before merging by loading the model from this PR with the revision argument:
from sentence_transformers import SentenceTransformer
# TODO: Fill in the PR number
pr_number = 2
model = SentenceTransformer(
"upskyy/e5-large-korean",
revision=f"refs/pr/{pr_number}",
backend="onnx",
)
# Verify that everything works as expected
embeddings = model.encode(["The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium."])
print(embeddings.shape)
similarities = model.similarity(embeddings, embeddings)
print(similarities)