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
Download added_tokens.json from KameronB/IT-embeddinggemma: direct link, hf CLI and curl.
- Browser
- Download file 35 Bytes
-
https://huggingface.co/KameronB/IT-embeddinggemma/resolve/main/added_tokens.json
- Command line
-
hf download hf://KameronB/IT-embeddinggemma/added_tokens.json
-
curl -L -o added_tokens.json https://huggingface.co/KameronB/IT-embeddinggemma/resolve/main/added_tokens.json
35 Bytes
| { | |
| "<image_soft_token>": 262144 | |
| } | |