Feature Extraction
sentence-transformers
Safetensors
English
embedding
retrieval
electrical-engineering
unsloth
information-retrieval
rag
semantic-search
arxiv:2509.20354
lora
Instructions to use disham993/electrical-embeddinggemma-ir_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use disham993/electrical-embeddinggemma-ir_lora with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("disham993/electrical-embeddinggemma-ir_lora") 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
- Local Apps Settings
- Unsloth Desktop
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README.md
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- information-retrieval
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- lora
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license: mit
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arxiv: 2509.20354
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# gemma-300m-electrical-electronics-ir_lora
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- information-retrieval
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- rag
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- semantic-search
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- lora
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license: mit
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# gemma-300m-electrical-electronics-ir_lora
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