Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:179524
loss:OrdinalProxyContrastiveLoss
text-embeddings-inference
Instructions to use swardiantara/bert-tiny-sst5-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-sst5-k5-fixed-euclidean with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("swardiantara/bert-tiny-sst5-k5-fixed-euclidean") sentences = [ "eccentric enough to stave off doldrums , caruso 's self-conscious debut is also eminently forgettable .", "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 .", "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 .", "not even solondz 's thirst for controversy , sketchy characters and immature provocations can fully succeed at cheapening it ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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