onnx-models/all-MiniLM-L6-v2-fine-tuned-sent-sent-epochs-10-iter-10-batch-32-onnx
By onnx-models · ↓ 2 · ♥ 0 · Updated 2024-09-25
What's in this repo
This page lists the files in onnx-models/all-MiniLM-L6-v2-fine-tuned-sent-sent-epochs-10-iter-10-batch-32-onnx, including model.onnx.
- 1 ONNX models (.onnx files)
- 8 files, including 4 .json, 1 .md, 1 .onnx, 1 .txt
- Last updated 2024-09-25
ONNX files
| File | Size | Download |
|---|---|---|
| model.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
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:
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:
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
Model info
Created: 2024-09-25
Updated: 2024-09-25
View on Hugging Face