Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:3845587
loss:OrdinalProxyContrastiveLoss
text-embeddings-inference
Instructions to use swardiantara/bert-tiny-snli-k3-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-k3-fixed-euclidean with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("swardiantara/bert-tiny-snli-k3-fixed-euclidean") sentences = [ "Woman in white in foreground and a man slightly behind walking with a sign for John's Pizza and Gyro in the background. [SEP] A woman ordering pizza.", "A room full of girls raising their hands. [SEP] The boys are jumping on the trampoline.", "A room full of girls raising their hands. [SEP] The boys are jumping on the trampoline.", "Two boys and a woman standing in front of a Pronto Pups Hamburger stand. [SEP] People are outside." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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