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
Download tokenizer.json from disham993/electrical-embeddinggemma-ir_lora: direct link, hf CLI and curl.
- Browser
- Download file 33.4 MB
-
https://huggingface.co/disham993/electrical-embeddinggemma-ir_lora/resolve/main/tokenizer.json
- Command line
-
hf download hf://disham993/electrical-embeddinggemma-ir_lora/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/disham993/electrical-embeddinggemma-ir_lora/resolve/main/tokenizer.json
33.4 MB
- Xet hash:
- 449432b0acb727cb06573a022cab7247cf1f1fa97d6287ab6c51ddb091d2ac78
- Size of remote file:
- 33.4 MB
- SHA256:
- 45732ebfacbc3b3da71bd8290d8c14c0df3b2d8d63d86970a953c59d71bd36d8
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