Sentence Similarity
sentence-transformers
Safetensors
Transformers
Indonesian
bert
feature-extraction
text-embeddings-inference
Instructions to use LazarusNLP/all-indo-e5-small-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LazarusNLP/all-indo-e5-small-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LazarusNLP/all-indo-e5-small-v2") 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/all-indo-e5-small-v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LazarusNLP/all-indo-e5-small-v2") model = AutoModel.from_pretrained("LazarusNLP/all-indo-e5-small-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from LazarusNLP/all-indo-e5-small-v2: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/LazarusNLP/all-indo-e5-small-v2/resolve/main/tokenizer.json
- Command line
-
hf download hf://LazarusNLP/all-indo-e5-small-v2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/LazarusNLP/all-indo-e5-small-v2/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 6ee172b479247cb76f7ef2d0cd3ac7492cd9ae2a48c679189d61242eb03f7a2f
- Size of remote file:
- 17.1 MB
- SHA256:
- 1130d2072397032d0b72b52b2ce0638976c2e04d47795ed83348615f39215614
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.