stanfordnlp/imdb
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How to use protectai/deberta-v3-large-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-large-zeroshot-v1-onnx") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("protectai/deberta-v3-large-zeroshot-v1-onnx")
model = AutoModelForSequenceClassification.from_pretrained("protectai/deberta-v3-large-zeroshot-v1-onnx")This model is a conversion of MoritzLaurer/deberta-v3-large-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).
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-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)
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Base model
MoritzLaurer/deberta-v3-large-zeroshot-v1