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---
language:
- en
license: mit
tags:
- NLI
- deberta-v3
datasets:
- mnli
- facebook/anli
- fever
- wanli
- ling
- amazonpolarity
- imdb
- appreviews
inference: false
pipeline_tag: zero-shot-classification
base_model: MoritzLaurer/deberta-v3-large-zeroshot-v1
---
# 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")
tokenizer.model_input_names = ["input_ids", "attention_mask"]
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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