Create README.md

README.md

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+---
+language:
+- en
+datasets:
+- mnli
+- anli
+- fever
+- wanli
+- ling
+- amazonpolarity
+- imdb
+- appreviews
+inference: false
+pipeline_tag: zero-shot-classification
+tags:
+- NLI
+- deberta-v3
+license: mit
+---
+
+# ONNX version of MoritzLaurer/deberta-v3-base-zeroshot-v1
+
+**This model is a conversion of [MoritzLaurer/deberta-v3-base-zeroshot-v1](https://huggingface.co/MoritzLaurer/deberta-v3-base-zeroshot-v1) to ONNX** format using the [🤗 Optimum](https://huggingface.co/docs/optimum/index) library.
+
+`MoritzLaurer/deberta-v3-large-zeroshot-v1` is designed for zero-shot classification, capable of determining whether a hypothesis is `true` or `not_true` based on a text, a format based on Natural Language Inference (NLI).
+
+## Usage
+
+Loading the model requires the [🤗 Optimum](https://huggingface.co/docs/optimum/index) library installed.
+
+```python
+from optimum.onnxruntime import ORTModelForSequenceClassification
+from transformers import AutoTokenizer, pipeline
+
+
+tokenizer = AutoTokenizer.from_pretrained("laiyer/deberta-v3-base-zeroshot-v1-onnx")
+model = ORTModelForSequenceClassification.from_pretrained("laiyer/deberta-v3-base-zeroshot-v1-onnx")
+classifier = pipeline(
+    task="zero-shot-classification",
+    model=model,
+    tokenizer=tokenizer,
+    top_k=None,
+)
+
+classifier_output = classifier("Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.", ["mobile", "website", "billing", "account access"])
+print(classifier_output)
+```