Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Indonesian
albert
feature-extraction
Instructions to use LazarusNLP/simcse-indobert-lite-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LazarusNLP/simcse-indobert-lite-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LazarusNLP/simcse-indobert-lite-base") 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] - Transformers
How to use LazarusNLP/simcse-indobert-lite-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LazarusNLP/simcse-indobert-lite-base") model = AutoModel.from_pretrained("LazarusNLP/simcse-indobert-lite-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ca209b47b9d3a9c825f83a1c07ab0d86b70a70b1f53e79a5f4045d9a1cc575c1
- Size of remote file:
- 46.7 MB
- SHA256:
- 05b45c4bcfcafc29b8e3f99354d94073e9fe4961f06590d7ab43a81e30a47750
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