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
Add normalization configuration
Browse files- 2_Normalize/config.json +4 -0
2_Normalize/config.json
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{
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"module_input_name": "sentence_embedding",
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"module_output_name": "sentence_embedding"
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}
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