README.md

metadata

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 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-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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