metadata

license: apache-2.0
language:
  - en
library_name: transformers
pipeline_tag: zero-shot-classification
tags:
  - ORTModelForSequenceClassification

DeBERTa-v3-base-onnx-quantized

This model has been quantized using the base model: sileod/deberta-v3-base-tasksource-nli, To use this model you need to have onnxruntime installed on your machine. To use this model, you can check out my Huggingface Spaces.

The source code for the Huggingface Application can be found on GitHub.

To run this model on your machine use the following code. Note that this model is optimized for CPU with AVX2 support.

Install dependencies

pip install transformers optimum[onnxruntime]

Run the model:

# load libraries
from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForSequenceClassification
from optimum.pipelines import pipeline

# load model components
MODEL_ID = "pitangent-ds/deberta-v3-nli-onnx-quantized"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = ORTModelForSequenceClassification.from_pretrained(MODEL_ID)

# load the pipeline
classifier = pipeline("zero-shot-classification", tokenizer=tokenizer, model=model)

# inference
text = "The jacket that I bought is awesome"
candidate_labels = ["positive", "negative"]

results = classifier(text, candidate_labels)