Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:50
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hyperakan/all-MiniLM-L6-v2-smoke with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Hyperakan/all-MiniLM-L6-v2-smoke with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Hyperakan/all-MiniLM-L6-v2-smoke") sentences = [ "Two men on bicycles competing in a race.", "People are riding bikes.", "A woman is doing a cartwheel.", "A few people are catching fish." ] 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.base.modules.transformer.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling" | |
| } | |
| ] |