Sentence Similarity
sentence-transformers
Safetensors
English
Chinese
multilingual
qwen3
feature-extraction
embedding
text-embedding
retrieval
text-embeddings-inference
Instructions to use bflhc/MoD-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use bflhc/MoD-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("bflhc/MoD-Embedding") 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
| { | |
| "prompts": { | |
| "query": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery:", | |
| "document": "" | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } | |