File size: 1,538 Bytes
dd017ff |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
---
tags:
- adapter-transformers
- adapterhub:sentiment/sst-2
- roberta
license: "apache-2.0"
---
# Adapter `roberta-base-sst_pfeiffer` for roberta-base
Pfeiffer Adapter trained on the SST-2 task.
**This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/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("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-sst_pfeiffer")
model.set_active_adapters(adapter_name)
```
## Architecture & Training
- Adapter architecture: pfeiffer
- Prediction head: None
- Dataset: [SST-2](https://nlp.stanford.edu/sentiment/index.html)
## Author Information
- Author name(s): Jonas Pfeiffer
- Author email: jonas@pfeiffer.ai
- Author links: [Website](https://pfeiffer.ai), [GitHub](https://github.com/JoPfeiff), [Twitter](https://twitter.com/@PfeiffJo)
## Citation
```bibtex
@article{Pfeiffer2020AdapterFusion,
author = {Pfeiffer, Jonas and Kamath, Aishwarya and R{\"{u}}ckl{\'{e}}, Andreas and Cho, Kyunghyun and Gurevych, Iryna},
journal = {arXiv preprint},
title = {{AdapterFusion}: Non-Destructive Task Composition for Transfer Learning},
url = {https://arxiv.org/pdf/2005.00247.pdf},
year = {2020}
}
```
*This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/roberta-base-sst_pfeiffer.yaml*. |