Sentence Similarity
ONNX
sentence-transformers
light-embed
bert
feature-extraction
text-embeddings-inference
Instructions to use onnx-models/all-MiniLM-L6-v2-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use onnx-models/all-MiniLM-L6-v2-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("onnx-models/all-MiniLM-L6-v2-onnx") 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] - Notebooks
- Google Colab
- Kaggle
onnx-models/all-MiniLM-L6-v2-onnx
This is the ONNX-ported version of the sentence-transformers/all-MiniLM-L6-v2 for generating text embeddings.
Model details
- Embedding dimension: 384
- Max sequence length: 256
- File size on disk: 0.08 GB
- Modules incorporated in the onnx: Transformer, Pooling, Normalize
Usage
Using this model becomes easy when you have light-embed installed:
pip install -U light-embed
Then you can use the model by specifying the original model name like this:
from light_embed import TextEmbedding
sentences = [
"This is an example sentence",
"Each sentence is converted"
]
model = TextEmbedding('sentence-transformers/all-MiniLM-L6-v2')
embeddings = model.encode(sentences)
print(embeddings)
or by specifying the onnx model name like this:
from light_embed import TextEmbedding
sentences = [
"This is an example sentence",
"Each sentence is converted"
]
model = TextEmbedding('onnx-models/all-MiniLM-L6-v2-onnx')
embeddings = model.encode(sentences)
print(embeddings)
Citing & Authors
Binh Nguyen / [email protected]
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