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--- |
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language: |
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- en |
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license: mit |
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tags: |
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- NLI |
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- deberta-v3 |
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datasets: |
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- mnli |
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- facebook/anli |
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- fever |
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- wanli |
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- ling |
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- amazonpolarity |
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- imdb |
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- appreviews |
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inference: false |
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pipeline_tag: zero-shot-classification |
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base_model: MoritzLaurer/deberta-v3-base-zeroshot-v1 |
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--- |
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# ONNX version of MoritzLaurer/deberta-v3-base-zeroshot-v1 |
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**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. |
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`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). |
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## Usage |
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Loading the model requires the [🤗 Optimum](https://huggingface.co/docs/optimum/index) library installed. |
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```python |
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from optimum.onnxruntime import ORTModelForSequenceClassification |
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from transformers import AutoTokenizer, pipeline |
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tokenizer = AutoTokenizer.from_pretrained("laiyer/deberta-v3-base-zeroshot-v1-onnx") |
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model = ORTModelForSequenceClassification.from_pretrained("laiyer/deberta-v3-base-zeroshot-v1-onnx") |
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classifier = pipeline( |
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task="zero-shot-classification", |
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model=model, |
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tokenizer=tokenizer, |
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) |
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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"]) |
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print(classifier_output) |
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``` |
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### LLM Guard |
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[Ban Topics scanner](https://llm-guard.com/input_scanners/ban_topics/) |
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## Community |
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Join our Slack to give us feedback, connect with the maintainers and fellow users, ask questions, |
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or engage in discussions about LLM security! |
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<a href="https://join.slack.com/t/laiyerai/shared_invite/zt-28jv3ci39-sVxXrLs3rQdaN3mIl9IT~w"><img src="https://github.com/laiyer-ai/llm-guard/blob/main/docs/assets/join-our-slack-community.png?raw=true" width="200"></a> |
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