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
| { | |
| "add_prefix_space": false, | |
| "backend": "tokenizers", | |
| "bos_token": null, | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|endoftext|>", | |
| "errors": "replace", | |
| "is_local": false, | |
| "local_files_only": false, | |
| "mask_token": "â½Ĺ", | |
| "model_max_length": 32768, | |
| "pad_token": "<|endoftext|>", | |
| "sep_token": "<|endoftext|>", | |
| "split_special_tokens": false, | |
| "tokenizer_class": "Qwen2Tokenizer", | |
| "unk_token": null | |
| } | |