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roberta-base-qnli_pfeiffer – AI Model by AdapterHub | AlphaNeural AI

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AdapterHub/
roberta-base-qnli_pfeiffer

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adapter-transformers

text-classification

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Adapter `roberta-base-qnli_pfeiffer` for roberta-base

QNLI adapter (with head) trained using the run_glue.py script with an extension that retains the best checkpoint (out of 15 epochs).

This adapter was created for usage with the Adapters library.

Usage

First, install adapters:

pip install -U adapters

Now, the adapter can be loaded and activated like this:

python

from adapters import AutoAdapterModel

model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-qnli_pfeiffer")
model.set_active_adapters(adapter_name)

Architecture & Training

Adapter architecture: pfeiffer
Prediction head: classification
Dataset: QNLI

Author Information

Author name(s): Andreas Rücklé
Author email: rueckle@ukp.informatik.tu-darmstadt
Author links: Website, GitHub, Twitter

Citation

bibtex

@article{pfeiffer2020AdapterHub,
    title={AdapterHub: A Framework for Adapting Transformers},
    author={Jonas Pfeiffer,
            Andreas R\"uckl\'{e},
            Clifton Poth,
            Aishwarya Kamath,
            Ivan Vuli\'{c},
            Sebastian Ruder,
            Kyunghyun Cho,
            Iryna Gurevych},
    journal={ArXiv},
    year={2020}
}

This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/roberta-base-qnli_pfeiffer.yaml.