Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:200120
loss:OrdinalProxyContrastiveLoss
text-embeddings-inference
Instructions to use swardiantara/bert-tiny-amazon_reviews-k3-fixed-cosine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use swardiantara/bert-tiny-amazon_reviews-k3-fixed-cosine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("swardiantara/bert-tiny-amazon_reviews-k3-fixed-cosine") sentences = [ "It doesn’t work Just they take money ! sorry for amazon to sell like this items .", "Broke after one use. Whenever you hit it, the light would go out. Not worth your money.", "Shipping was late box came open and missing pieces. Very bad experience. Came dirty and a stain on umbrella, not balanced at all so if you dont get weights it will not stand at all.", "Shipping was late box came open and missing pieces. Very bad experience. Came dirty and a stain on umbrella, not balanced at all so if you dont get weights it will not stand at all." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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