Sentence Similarity
sentence-transformers
ONNX
Safetensors
embedding_gemma2
feature-extraction
dense
Generated from Trainer
dataset_size:50
loss:MultipleNegativesRankingLoss
Instructions to use Erog0291/stew-embedding-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Erog0291/stew-embedding-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Erog0291/stew-embedding-v2") sentences = [ "task: search result | query: Upscale this image for me", "title: code_app | text: Write and run code in a sandbox; build simple apps and games. Best for building a game or writing a script.", "title: image_edit | text: Edit, upscale or restyle images. Best for cleaning up a photo or removing a background.", "title: code_app | text: Write and run code in a sandbox; build simple apps and games. Best for building a game or writing a script." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 413 Bytes
4830487 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | [
{
"idx": 0,
"name": "0",
"path": "",
"type": "sentence_transformers.base.modules.transformer.Transformer"
},
{
"idx": 1,
"name": "1",
"path": "1_Pooling",
"type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
},
{
"idx": 2,
"name": "2",
"path": "2_Normalize",
"type": "sentence_transformers.base.modules.normalize.Normalize"
}
] |