Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use tcepi/sts_bertimbau with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tcepi/sts_bertimbau with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tcepi/sts_bertimbau") 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:
- 2ca298d907da780f4eef006db3be862a343d0f93fa3048bdafc70572b1a06628
- Size of remote file:
- 436 MB
- SHA256:
- 95f13240c1f01ecc76ed5047389bf4ac107c62e6a61d29983ca75ee7d75f9415
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