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:
# 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") - Inference
- Notebooks
- Google Colab
- Kaggle
Add exported onnx model 'model.onnx'
#1
by itsdevice - opened
Hello!
This pull request has been automatically generated from the Sentence Transformers backend-export Space.
Pull Request overview
- Add exported ONNX model
model.onnx.
Tip:
Consider testing this pull request before merging by loading the model from this PR with the revision argument:
from sentence_transformers import SentenceTransformer
# TODO: Fill in the PR number
pr_number = 2
model = SentenceTransformer(
"LazarusNLP/all-indo-e5-small-v4",
revision=f"refs/pr/{pr_number}",
backend="onnx",
)
# Verify that everything works as expected
embeddings = model.encode(["The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium."])
print(embeddings.shape)
similarities = model.similarity(embeddings, embeddings)
print(similarities)
w11wo changed pull request status to merged