Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use NeuML/bert-tiny-sts-last-pooling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NeuML/bert-tiny-sts-last-pooling with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NeuML/bert-tiny-sts-last-pooling") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5eee52b314542ab7633f74b37dad02c077bffecb7e99f57b633eba1713c35b9a
- Size of remote file:
- 17.5 MB
- SHA256:
- 68e383c0b8687049f44bab69c24eefc72cf382154736c3607a375dc8e82db181
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