Sentence Similarity
sentence-transformers
Safetensors
PEFT
English
text-embeddings
retrieval
web-search
news
Instructions to use desearch/Desearch-Embedding-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use desearch/Desearch-Embedding-4B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("desearch/Desearch-Embedding-4B") 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] - PEFT
How to use desearch/Desearch-Embedding-4B with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Download config_sentence_transformers.json from desearch/Desearch-Embedding-4B: direct link, hf CLI and curl.
- Browser
- Download file 376 Bytes
-
https://huggingface.co/desearch/Desearch-Embedding-4B/resolve/main/config_sentence_transformers.json
- Command line
-
hf download hf://desearch/Desearch-Embedding-4B/config_sentence_transformers.json
-
curl -L -o config_sentence_transformers.json https://huggingface.co/desearch/Desearch-Embedding-4B/resolve/main/config_sentence_transformers.json
376 Bytes
| { | |
| "__version__": { | |
| "pytorch": "2.13.0+cu130", | |
| "sentence_transformers": "5.7.0", | |
| "transformers": "5.15.0" | |
| }, | |
| "default_prompt_name": null, | |
| "model_type": "SentenceTransformer", | |
| "prompts": { | |
| "document": "", | |
| "query": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery:" | |
| }, | |
| "similarity_fn_name": "cosine" | |
| } |