Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
bert
mteb
Sentence Transformers
Eval Results (legacy)
Eval Results
text-embeddings-inference
Instructions to use intfloat/multilingual-e5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/multilingual-e5-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/multilingual-e5-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] - Inference
- Notebooks
- Google Colab
- Kaggle
Add new SentenceTransformer models with an onnx backend
#11
by tomaarsen HF Staff - opened
Hello!
The models from this PR have been generated with Sentence Transformers, see export_dynamic_quantized_onnx_model, export_optimized_onnx_model.
Tip
Consider testing this pull request before merging by loading the model from this PR with the revision argument:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer(
"intfloat/multilingual-e5-small",
revision=f"refs/pr/11",
backend="onnx",
model_kwargs={
"file_name": "model_O3.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)
- Tom Aarsen
tomaarsen changed pull request status to closed