Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use hunkim/sentence-transformersklue-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hunkim/sentence-transformersklue-bert-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hunkim/sentence-transformersklue-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-transformersklue-bert-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hunkim/sentence-transformersklue-bert-base") model = AutoModel.from_pretrained("hunkim/sentence-transformersklue-bert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7a299fcae056077775d96a3a92b7471d2b182dce74a5ab48140a21abaa8604b
- Size of remote file:
- 443 MB
- SHA256:
- 41c74c4bfb72e9be5e5f35b03ed17edbaec7f6f6dbfc5a2f3adfc9eecd53ce41
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