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:

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

@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.