Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use jhgan/ko-sbert-sts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jhgan/ko-sbert-sts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jhgan/ko-sbert-sts") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use jhgan/ko-sbert-sts with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jhgan/ko-sbert-sts") model = AutoModel.from_pretrained("jhgan/ko-sbert-sts", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jhgan/ko-sbert-sts: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/jhgan/ko-sbert-sts/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jhgan/ko-sbert-sts/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jhgan/ko-sbert-sts/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 261e3bcafdfbf52ca0c7086388fc83f0d5a7c6aa8f6df8f8799f58cfa21061d8
- Size of remote file:
- 443 MB
- SHA256:
- 548d8ae1045c5ca961a86c59266e61efe11363162ad6499ac56796735e57978c
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