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.model from disham993/electrical-embeddinggemma-ir_lora: direct link, hf CLI and curl.
- Browser
- Download file 4.69 MB
-
https://huggingface.co/disham993/electrical-embeddinggemma-ir_lora/resolve/main/tokenizer.model
- Command line
-
hf download hf://disham993/electrical-embeddinggemma-ir_lora/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/disham993/electrical-embeddinggemma-ir_lora/resolve/main/tokenizer.model
4.69 MB
- Xet hash:
- f6aa6b6d77b66c0b5905517df9e5261c32195192839dd05e71e37d6b89933074
- Size of remote file:
- 4.69 MB
- SHA256:
- 1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
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