tags:
- text-classification
- adapter-transformers
- bert
- adapterhub:nli/sick
datasets:
- sick
language:
- en
Adapter AdapterHub/bert-base-uncased-pf-sick for bert-base-uncased
An adapter for the bert-base-uncased model that was trained on the nli/sick 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-sick", 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",
}