Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:111102
loss:OrdinalProxyContrastiveLoss
text-embeddings-inference
Instructions to use swardiantara/bert-tiny-sst5-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-sst5-k3-fixed-euclidean with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("swardiantara/bert-tiny-sst5-k3-fixed-euclidean") sentences = [ "with its parade of almost perpetually wasted characters ... margarita feels like a hazy high that takes too long to shake .", "like the best of godard 's movies ... it is visually ravishing , penetrating , impenetrable .", "a semi-autobiographical film that 's so sloppily written and cast that you can not believe anyone more central to the creation of bugsy than the caterer had anything to do with it .", "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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