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AdapterHub/
bert-base-uncased_nli_rte_pfeiffer

Name: bert-base-uncased_nli_rte_pfeiffer
Availability: InStock

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Adapter `bert-base-uncased_nli_rte_pfeiffer` for bert-base-uncased

Adapter in Pfeiffer architecture trained on the RTE task for 20 epochs with early stopping and a learning rate of 1e-4. See https://arxiv.org/pdf/2007.07779.pdf.

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("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased_nli_rte_pfeiffer")
model.set_active_adapters(adapter_name)

Architecture & Training

Adapter architecture: pfeiffer
Prediction head: classification
Dataset: RTE

Author Information

Author name(s): Clifton Poth
Author email: calpt@mail.de
Author links: Website, GitHub, Twitter

Citation

bibtex

@article{pfeiffer2020AdapterHub,
    title={AdapterHub: A Framework for Adapting Transformers},
    author={Jonas Pfeiffer and
            Andreas R\"uckl\'{e} and
            Clifton Poth and
            Aishwarya Kamath and
            Ivan Vuli\'{c} and
            Sebastian Ruder and
            Kyunghyun Cho and
            Iryna Gurevych},
    journal={arXiv preprint},
    year={2020},
    url={https://arxiv.org/abs/2007.07779}
}

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

AdapterHub/bert-base-uncased_nli_rte_pfeiffer

Adapter bert-base-uncased_nli_rte_pfeiffer for bert-base-uncased