Sentence Similarity
sentence-transformers
Safetensors
feature-extraction
dense
Generated from Trainer
dataset_size:1979
loss:MultipleNegativesRankingLoss
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
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Dense", | |
| "type": "sentence_transformers.models.Dense" | |
| }, | |
| { | |
| "idx": 3, | |
| "name": "3", | |
| "path": "3_Dense", | |
| "type": "sentence_transformers.models.Dense" | |
| }, | |
| { | |
| "idx": 4, | |
| "name": "4", | |
| "path": "4_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |