Sentence Similarity
sentence-transformers
Safetensors
bert
semantic-search
feature-extraction
cybersecurity
text-embeddings-inference
Instructions to use MrZaper/LiteModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use MrZaper/LiteModel with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("MrZaper/LiteModel") 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] - Notebooks
- Google Colab
- Kaggle
Upload 2 files
#1
by MrZaper - opened
- sbert_embeddings.npy +3 -0
- sbert_labels.pkl +3 -0
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sbert_labels.pkl
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oid sha256:2d84fe705cb174632b45bdef70efaf01823d3d6c71f313651ddf5906173dc629
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