Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use hunkim/sentence-transformers-klue-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hunkim/sentence-transformers-klue-bert-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hunkim/sentence-transformers-klue-bert-base") 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 hunkim/sentence-transformers-klue-bert-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hunkim/sentence-transformers-klue-bert-base") model = AutoModel.from_pretrained("hunkim/sentence-transformers-klue-bert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bd1f26b0f60fcf103ece943acac68d1cd836f28612535aaa9fe464e098132b0d
- Size of remote file:
- 443 MB
- SHA256:
- a1ec4fa931a91f4ba13a4d54b7abaa564f4e1d48e8ed5c4367d058d2fb222cdd
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