Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use yahyaabd/bps-indosbert-base-p2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yahyaabd/bps-indosbert-base-p2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yahyaabd/bps-indosbert-base-p2") 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:
- cb1a23ff1d9fa05aaf8096c7d2c15bf5c3f4bbfa6c729ebf0912244bad4978be
- Size of remote file:
- 438 MB
- SHA256:
- 1ee2a7f419365e4f5aed12a0740460c5a4aa84365ced586546df0923664677ee
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