onnx-models/multi-qa-mpnet-base-dot-v1-onnx
This is the ONNX-ported version of the sentence-transformers/multi-qa-mpnet-base-dot-v1 for generating text embeddings.
Model details
- Embedding dimension: 768
- Max sequence length: 512
- File size on disk: 0.41 GB
- Modules incorporated in the onnx: Transformer, Pooling
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/multi-qa-mpnet-base-dot-v1')
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/multi-qa-mpnet-base-dot-v1-onnx')
embeddings = model.encode(sentences)
print(embeddings)
Citing & Authors
Binh Nguyen / binhcode25@gmail.com
- Downloads last month
- 3
Inference API (serverless) does not yet support light-embed models for this pipeline type.