Feature Extraction
sentence-transformers
Safetensors
English
bert
embeddings
text-embeddings
semantic-search
information-retrieval
rag
retrieval
bge
Eval Results (legacy)
text-embeddings-inference
Instructions to use KeZZ08/mirror-memory-default-embedding-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KeZZ08/mirror-memory-default-embedding-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KeZZ08/mirror-memory-default-embedding-model") 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
Download 1_Pooling/config.json from KeZZ08/mirror-memory-default-embedding-model: direct link, hf CLI and curl.
- Browser
- Download file 89 Bytes
-
https://huggingface.co/KeZZ08/mirror-memory-default-embedding-model/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://KeZZ08/mirror-memory-default-embedding-model/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/KeZZ08/mirror-memory-default-embedding-model/resolve/main/1_Pooling/config.json
89 Bytes
| { | |
| "embedding_dimension": 384, | |
| "pooling_mode": "cls", | |
| "include_prompt": true | |
| } |