Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:6043097
loss:OrdinalProxyContrastiveLoss
text-embeddings-inference
Instructions to use swardiantara/bert-tiny-snli-k5-fixed-euclidean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use swardiantara/bert-tiny-snli-k5-fixed-euclidean with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("swardiantara/bert-tiny-snli-k5-fixed-euclidean") sentences = [ "A woman in a green jacket and hood over her head looking towards a valley. [SEP] The woman is wearing green.", "A group of young children kick around a ball on a field with a body of water in the background. [SEP] The children are playing video games.", "A brown dog sits alone in front of a snowbank. [SEP] The dog is getting a tan on the beach.", "A man on stilts is playing a tuba for money on the boardwalk. [SEP] A male street performer with a tuba is playing outside." ] 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" | |
| } | |
| ] |