vamsibanda/sbert-all-MiniLM-L12-with-pooler
By vamsibanda · ↓ 3 · ♥ 0 · Updated 2022-09-13
What's in this repo
This page lists the files in vamsibanda/sbert-all-MiniLM-L12-with-pooler, including model.onnx.
- 1 ONNX models (.onnx files)
- 9 files, including 5 .json, 1 .md, 1 .onnx, 1 .txt
- Last updated 2022-09-13
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 convert all-MiniLM-L12-v2
Conversion of sentence-transformers/all-MiniLM-L12-v2
This is a sentence-transformers ONNX model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. This custom model takes last_hidden_state and pooler_output whereas the sentence-transformers exported with default ONNX config only contains last_hidden_state as output.
Usage (HuggingFace Optimum)
Using this model becomes easy when you have optimum installed:
python -m pip install optimum
Then you can use the model like this:
from optimum.onnxruntime.modeling_ort import ORTModelForCustomTasks
model = ORTModelForCustomTasks.from_pretrained("vamsibanda/sbert-all-MiniLM-L12-with-pooler")
tokenizer = AutoTokenizer.from_pretrained("vamsibanda/sbert-all-MiniLM-L12-with-pooler")
inputs = tokenizer("I love burritos!", return_tensors="pt")
pred = model(**inputs)
embedding = pred['pooler_output']
Model info
Created: 2022-07-23
Updated: 2022-09-13
View on Hugging Face