README.md

---
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](https://huggingface.co/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](https://huggingface.co/spaces/arnabdhar/Zero-Shot-Classification-DeBERTa-Quantized).

The source code for the Huggingface Application can be found on [GitHub](https://github.com/arnabd64/Zero-Shot-Text-Classification).

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

1. Install dependencies

```bash
pip install transformers optimum[onnxruntime]
```

2. Run the model:

```python
# 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)
```