Sentence Similarity
sentence-transformers
Safetensors
Vietnamese
roberta
feature-extraction
dense
Generated from Trainer
dataset_size:100008
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
Instructions to use minhthuan77f1/binhdinh-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use minhthuan77f1/binhdinh-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("minhthuan77f1/binhdinh-embedding") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 138b74029d2eb1956daa8b925486828297215fa6fdaf4f5b38a4a26486a59ddb
- Size of remote file:
- 5.52 kB
- SHA256:
- 46868d582b0f1634cea5507983c428c51a60c783e493443e6403bc4a064822de
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