AdapterHub / bert-base-uncased-pf-qqp

Adapter AdapterHub/bert-base-uncased-pf-qqp for bert-base-uncased

An adapter for the bert-base-uncased model that was trained on the sts/qqp dataset and includes a prediction head for classification.

This adapter was created for usage with the adapter-transformers library.

Usage

First, install adapter-transformers :

pip install -U adapter-transformers

Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More

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

from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-qqp", source="hf")
model.active_adapters = adapter_name

Architecture & Training

The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer . In particular, training configurations for all tasks can be found here .

Evaluation results

Refer to the paper for more information on results.

Citation

If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection" :

@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}