Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:8894
loss:OrdinalProxyContrastiveLoss
text-embeddings-inference
Instructions to use swardiantara/bert-tiny-sst5-k5-adaptive-cosine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use swardiantara/bert-tiny-sst5-k5-adaptive-cosine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("swardiantara/bert-tiny-sst5-k5-adaptive-cosine") sentences = [ "a fun ride .", "not even solondz 's thirst for controversy , sketchy characters and immature provocations can fully succeed at cheapening it .", "contrived as this may sound , mr. rose 's updating works surprisingly well .", "contrived as this may sound , mr. rose 's updating works surprisingly well ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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