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 modules.json from KeZZ08/mirror-memory-default-embedding-model: direct link, hf CLI and curl.
- Browser
- Download file 432 Bytes
-
https://huggingface.co/KeZZ08/mirror-memory-default-embedding-model/resolve/main/modules.json
- Command line
-
hf download hf://KeZZ08/mirror-memory-default-embedding-model/modules.json
-
curl -L -o modules.json https://huggingface.co/KeZZ08/mirror-memory-default-embedding-model/resolve/main/modules.json
432 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.base.modules.transformer.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.base.modules.normalize.Normalize" | |
| } | |
| ] |