| 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-base-zeroshot-v1 | |
| # 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, | |
| ) | |
| 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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