Sentence Similarity
sentence-transformers
Safetensors
GGUF
Persian
English
diba_embed
feature-extraction
text-embeddings
persian
farsi
iran
retrieval
rag
semantic-search
multilingual
dibachain
llama.cpp
custom_code
Instructions to use Dibachain/Diba-Embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Dibachain/Diba-Embed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Dibachain/Diba-Embed", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - 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" | |
| } |