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
- Xet hash:
- 7f7be6ca26b58e38c5d029a0ca6b959a928c73e3f2534fb38643dd2ef66d5b86
- Size of remote file:
- 90.9 MB
- SHA256:
- bd849e8d6be7481d3888cac4177134c2959f945325127e4e4b40ee48ed1b6dfb
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