Sentence Similarity
sentence-transformers
Safetensors
English
bidirectional_pplx_qwen3
feature-extraction
RAG
domain-adapted
custom-embeddings
custom_code
text-embeddings-inference
Instructions to use Layasaran/text_embed_0.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Layasaran/text_embed_0.5b with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Layasaran/text_embed_0.5b", trust_remote_code=True) 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] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- adf373ce8670fb88d6ad53e08917e1a6d10a3faa920c2080531a62e0d32b44e1
- Size of remote file:
- 11.4 MB
- SHA256:
- 32687b48a8d7da95d23b32a8f24677795496605001bddee04016bb78ebcc2e67
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.