Update README.md

README.md

@@ -6,4 +6,42 @@ 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)
+```