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onnx-models/
all-MiniLM-L6-v2-fine-tuned-sent-sent-epochs-10-iter-10-batch-32-onnx

Name: all-MiniLM-L6-v2-fine-tuned-sent-sent-epochs-10-iter-10-batch-32-onnx
Availability: InStock

light-embed

onnx

bert

sentence-transformers

feature-extraction

sentence-similarity

text-embeddings-inference

endpoints_compatible

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onnx-models/all-MiniLM-L6-v2-fine-tuned-sent-sent-epochs-10-iter-10-batch-32-onnx

This is the ONNX-ported version of the event-nlp/all-MiniLM-L6-v2-fine-tuned-sent-sent-epochs-10-iter-10-batch-32 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:

python

from light_embed import TextEmbedding
sentences = [
	"This is an example sentence",
	"Each sentence is converted"
]

model = TextEmbedding('event-nlp/all-MiniLM-L6-v2-fine-tuned-sent-sent-epochs-10-iter-10-batch-32')
embeddings = model.encode(sentences)
print(embeddings)

or by specifying the onnx model name like this:

python

from light_embed import TextEmbedding
sentences = [
	"This is an example sentence",
	"Each sentence is converted"
]

model = TextEmbedding('onnx-models/all-MiniLM-L6-v2-fine-tuned-sent-sent-epochs-10-iter-10-batch-32-onnx')
embeddings = model.encode(sentences)
print(embeddings)

Citing & Authors

Binh Nguyen / binhcode25@gmail.com