Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Indonesian
bert
feature-extraction
text-embeddings-inference
Instructions to use LazarusNLP/simcse-indobert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LazarusNLP/simcse-indobert-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LazarusNLP/simcse-indobert-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-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LazarusNLP/simcse-indobert-base") model = AutoModel.from_pretrained("LazarusNLP/simcse-indobert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ede4be9825f02f77118c735225c0fa485722ca6e3b451647908c16183172af4
- Size of remote file:
- 498 MB
- SHA256:
- b45e3d401265065fd380772e6ca15aee0203b37d84b9bb25ce507c16bf4105e7
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