Sentence Similarity
sentence-transformers
ONNX
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use LazarusNLP/all-indo-e5-small-v4 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-v4 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LazarusNLP/all-indo-e5-small-v4") 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] - Transformers
How to use LazarusNLP/all-indo-e5-small-v4 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LazarusNLP/all-indo-e5-small-v4") model = AutoModel.from_pretrained("LazarusNLP/all-indo-e5-small-v4", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from LazarusNLP/all-indo-e5-small-v4: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/LazarusNLP/all-indo-e5-small-v4/resolve/main/tokenizer.json
- Command line
-
hf download hf://LazarusNLP/all-indo-e5-small-v4/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/LazarusNLP/all-indo-e5-small-v4/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 8d94d3ce17be0f8c9540ce715b323b3bbbcd37b1c4f472dcacb2ca79ceec5b91
- Size of remote file:
- 17.1 MB
- SHA256:
- 6c2ccb33b55c4d157419a2105d6e033f7514c2baa6306471c56b86ab02787613
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.