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
Commit History
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Add TF weights (#1)
jhgan +1: joaogante- Add TF weights (ad295f2312ee97840b928241aa8044bec381cdc8) Co-authored-by: Joao Gante <joaogante@users.noreply.huggingface.co>04194e9
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changed backborn - to klue bert
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updated model path
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added citations
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updated README.md
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initial commit
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initial commit
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