---
tags:
- gpt2
- adapterhub:nli/rte
- adapter-transformers
- text-classification
license: "apache-2.0"
---

# Adapter `gpt2_nli_rte_houlsby` for gpt2

Adapter for gpt2 in Houlsby architecture trained on the RTE dataset for 10 epochs with 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("gpt2")
adapter_name = model.load_adapter("AdapterHub/gpt2_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): Hannah Sterz
- Author email: hannah.sterz@stud.tu-darmstadt.de
- Author links: [Twitter](https://twitter.com/@h_sterz)



## Citation

```bibtex

```

*This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/gpt2_nli_rte_houlsby.yaml*.