Feature Extraction
sentence-transformers
Persian
English
argon1
text-embeddings
bert
contrastive-learning
multilingual
rag
semantic-search
Instructions to use Mohammedkarimi/argon1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Mohammedkarimi/argon1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Mohammedkarimi/argon1") 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
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library_name: sentence-transformers
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# π Argon1 (Argon
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β‘ Ultra-Compact | π Persian & English Optimized | π§ Contrastive Representation | π High-Efficiency RAG
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library_name: sentence-transformers
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# π Argon1 (Argon): Multilingual Semantic Embedding Model
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β‘ Ultra-Compact | π Persian & English Optimized | π§ Contrastive Representation | π High-Efficiency RAG
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