metadata

library_name: light-embed
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity

onnx-models/all-MiniLM-L6-v2-fine-tuned-epochs-50-iter-20-batch-32-onnx

This is the ONNX-ported version of the event-nlp/all-MiniLM-L6-v2-fine-tuned-epochs-50-iter-20-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:

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-epochs-50-iter-20-batch-32')
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-fine-tuned-epochs-50-iter-20-batch-32-onnx')
embeddings = model.encode(sentences)
print(embeddings)

Citing & Authors

Binh Nguyen / [email protected]