Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use tomaarsen/bert-base-uncased-cnn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tomaarsen/bert-base-uncased-cnn with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomaarsen/bert-base-uncased-cnn") sentences = [ "A man is speaking.", "A man is talking on a phone.", "The boy is jumping into a lake.", "A cat is pouncing on a trampoline." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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