File size: 1,355 Bytes
4337e8c |
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 |
---
tags:
- bart
- adapter-transformers
- adapterhub:nli/rte
- text-classification
license: "apache-2.0"
---
# Adapter `facebook-bart-base_nli_rte_houlsby` for facebook/bart-base
Adapter for bart-base in Houlsby architecture trained on the RTE dataset for 15 epochs with early stopping and a learning rate of 1e-4.
**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("facebook/bart-base")
adapter_name = model.load_adapter("AdapterHub/facebook-bart-base_nli_rte_houlsby")
model.set_active_adapters(adapter_name)
```
## Architecture & Training
- Adapter architecture: houlsby
- Prediction head: classification
- Dataset: [RTE](https://aclweb.org/aclwiki/Recognizing_Textual_Entailment)
## Author Information
- Author name(s): Clifton Poth
- Author email: calpt@mail.de
- Author links: [Website](https://calpt.github.io), [GitHub](https://github.com/calpt), [Twitter](https://twitter.com/@clifapt)
## Citation
```bibtex
```
*This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/facebook-bart-base_nli_rte_houlsby.yaml*. |