Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:1979
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use KameronB/IT-embeddinggemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KameronB/IT-embeddinggemma with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KameronB/IT-embeddinggemma") sentences = [ "The iPhone Bluetooth connection drops unexpectedly during use.", "The customer is asking how to update apps on their iPhone automatically.", "The device loses power quickly despite showing a 100% battery level earlier.", "Bluetooth devices disconnect frequently when paired with the iPhone." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 132 Bytes
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