
# ONNX version of MoritzLaurer/deberta-v3-large-zeroshot-v1

**This model is a conversion of [MoritzLaurer/deberta-v3-large-zeroshot-v1](https://huggingface.co/MoritzLaurer/deberta-v3-large-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-large-zeroshot-v1-onnx")
model = ORTModelForSequenceClassification.from_pretrained("laiyer/deberta-v3-large-zeroshot-v1-onnx")
classifier = pipeline(
    task="zero-shot-classification",
    model=model,
    tokenizer=tokenizer,
)

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)
```

### LLM Guard

[Ban Topics scanner](https://llm-guard.com/input_scanners/ban_topics/)

## Community

Join our Slack to give us feedback, connect with the maintainers and fellow users, ask questions, 
or engage in discussions about LLM security!

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{"language": ["en"], "license": "mit", "tags": ["NLI", "deberta-v3"], "datasets": ["mnli", "anli", "fever", "wanli", "ling", "amazonpolarity", "imdb", "appreviews"], "inference": false, "pipeline_tag": "zero-shot-classification"}
transformers onnx deberta-v2 text-classification NLI deberta-v3 zero-shot-classification en dataset:mnli dataset:anli dataset:fever dataset:wanli dataset:ling dataset:amazonpolarity dataset:imdb dataset:appreviews license:mit autotrain_compatible region:us