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:

python

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']