Sentence Similarity
sentence-transformers
ONNX
Safetensors
Indonesian
feature-extraction
indonesian
embedding
quantized
Eval Results (legacy)
Instructions to use asmud/indonesian-embedding-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use asmud/indonesian-embedding-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("asmud/indonesian-embedding-small") 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] - Notebooks
- Google Colab
- Kaggle
Download pytorch/tokenizer.json from asmud/indonesian-embedding-small: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/asmud/indonesian-embedding-small/resolve/main/pytorch/tokenizer.json
- Command line
-
hf download hf://asmud/indonesian-embedding-small/pytorch/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/asmud/indonesian-embedding-small/resolve/main/pytorch/tokenizer.json
17.1 MB
- Xet hash:
- b22173df038a7b4d9502767d8e89b4103644bb459fdd7268f534c06006c86592
- Size of remote file:
- 17.1 MB
- SHA256:
- f94d4ae9b29d30e995a4d22edde16921dfd0f47b0bafbfca1cacd0cd34e2c929
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