tags:
- bert
- adapterhub:sentiment/rotten_tomatoes
- adapter-transformers
datasets:
- rotten_tomatoes
language:
- en
Adapter AdapterHub/bert-base-uncased-pf-rotten_tomatoes for bert-base-uncased
An adapter for the bert-base-uncased model that was trained on the sentiment/rotten_tomatoes 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-rotten_tomatoes", 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-what-to-pre-train-on,
title={What to Pre-Train on? Efficient Intermediate Task Selection},
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2104.08247",
pages = "to appear",
}