Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:102127
loss:SpladeLoss
loss:SparseMultipleNegativesRankingLoss
loss:FlopsLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use seregadgl/splade_gemma_google_base_checkpoint_100_ver2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use seregadgl/splade_gemma_google_base_checkpoint_100_ver2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("seregadgl/splade_gemma_google_base_checkpoint_100_ver2") sentences = [ "query: 6460338 acdelco", "document: очиститель тормозов rsqprofessional арт 072589767pl volkswagen id buzz янтарный", "document: гтц 6460338 для chevrolet traverse", "document: гтц 6960358 для chevrolet traverse" ] 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_SparseLayer", | |
| "type": "__main__.SparseLayer" | |
| } | |
| ] |