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-v2 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-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cassador/indobert-base-p2-nli-v2") sentences = [ "\"Berbagai macam jenis minuman sehat untuk mengembalikan ion ataupun mengandung vitamin, dapat kita temui dengan mudah di sekitar.\"", "Moody's tidak memiliki metrik peringkat untuk penerbit sekuritas yang dikenai pajak.", "Lupa olahraga adalah alasan yang selalu digunakan untuk tak berolahraga.", "Minuman sehat sulit ditemui." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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