Instructions to use protectai/deberta-v3-base-zeroshot-v1-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use protectai/deberta-v3-base-zeroshot-v1-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="protectai/deberta-v3-base-zeroshot-v1-onnx")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("protectai/deberta-v3-base-zeroshot-v1-onnx") model = AutoModelForSequenceClassification.from_pretrained("protectai/deberta-v3-base-zeroshot-v1-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
THIS PROJECT HAS BEEN ARCHIVED.
This project and its associated code on GitHub are no longer under active development or maintained.
ONNX version of MoritzLaurer/deberta-v3-base-zeroshot-v1
This model is a conversion of MoritzLaurer/deberta-v3-base-zeroshot-v1 to ONNX format using the 🤗 Optimum 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 library installed.
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)
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