Sentence Similarity
sentence-transformers
Safetensors
Indonesian
bert
feature-extraction
Generated from Trainer
dataset_size:10000
loss:SoftmaxLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use cassador/indobert-base-p2-nli-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cassador/indobert-base-p2-nli-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cassador/indobert-base-p2-nli-v1") sentences = [ "Dengan meniupnya, perawat bisa segera mengerti bahwa ia dipanggil dan akan segera datang menolong.", "38% pemilih tidak mendukung meninggalkan Uni Eropa.", "Perawat mengerti bahwa ia dipanggil dan akan segera datang menolong.", "Dari fakta-fakta tersebut dapat diindikasikan pembakaran gereja dilakukan secara sengaja." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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