Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
Generated from Trainer
dataset_size:5749
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use cahya/NusaBert-v1.2-sts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cahya/NusaBert-v1.2-sts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cahya/NusaBert-v1.2-sts") sentences = [ "A chef is preparing some food.", "Five birds stand on the snow.", "A chef prepared a meal.", "There is no 'still' that is not relative to some other object." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a4b51364009cecf683ad2caef6da321f94ebad6adae3d8753c35ff117679a788
- Size of remote file:
- 641 MB
- SHA256:
- c6dbb6442f94434182774c80455e211f95934d59f5ac44bd057b9b61a7ddb563
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