Sentence Similarity
sentence-transformers
PyTorch
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use jeonsworld/sentence-embedding-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jeonsworld/sentence-embedding-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jeonsworld/sentence-embedding-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 jeonsworld/sentence-embedding-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jeonsworld/sentence-embedding-base") model = AutoModel.from_pretrained("jeonsworld/sentence-embedding-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 608ce819129dc116a0bd87fd638905a39965b6e762c81102adc58252d8fb42bf
- Size of remote file:
- 443 MB
- SHA256:
- 19e234f2685222a1b9ab81db861941cf76a98b69d27f46844f2f12dad238eb35
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