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
| from transformers.models.qwen3.configuration_qwen3 import Qwen3Config | |
| class PPLXQwen3Config(Qwen3Config): | |
| model_type = "bidirectional_pplx_qwen3" | |