# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("mschwab/va_bert_classification")
model = AutoModelForSequenceClassification.from_pretrained("mschwab/va_bert_classification")Quick Links
English Vossian Antonomasia Sentence Classifier
This page presents a fine-tuned BERT-base-cased language model for classifying sentences that include Vossian Antonomasia. The label "VA" corresponds to the occurrence of a Vossian Antonomasia in the sentence.
Dataset
The dataset is a labeled Vossian Antonomasia dataset that evolved from Schwab et al. 2019 and was updated in Schwab et al. 2022.
Results
F1 score: 0.974
For more results, please have a look at our paper.
Cite
Please cite the following paper when using this model.
@article{schwab2022rodney,
title={“The Rodney Dangerfield of Stylistic Devices”: End-to-End Detection and Extraction of Vossian Antonomasia Using Neural Networks},
author={Schwab, Michel and J{\"a}schke, Robert and Fischer, Frank},
journal={Frontiers in Artificial Intelligence},
volume={5},
year={2022},
publisher={Frontiers Media SA}
}
Interested in more?
Visit our Website for more research on Vossian Antonomasia, including interactive visualizations for exploration.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mschwab/va_bert_classification")