Sentence Similarity
sentence-transformers
PyTorch
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use hunkim/sentence-transformer-klue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hunkim/sentence-transformer-klue with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hunkim/sentence-transformer-klue") 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-transformer-klue with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hunkim/sentence-transformer-klue") model = AutoModel.from_pretrained("hunkim/sentence-transformer-klue", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 03dc29302396e6f48abb3556d4e805b977112a04ba46463a06e5404832d233f4
- Size of remote file:
- 443 MB
- SHA256:
- c3479e45fefe225c589e1c78fd446ed38f9a9633453b88ddbb651f3436656a51
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