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

# Adapter `distilbert-base-uncased_nli_rte_pfeiffer` for distilbert-base-uncased

Adapter for distilbert-base-uncased in Pfeiffer 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("distilbert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/distilbert-base-uncased_nli_rte_pfeiffer")
model.set_active_adapters(adapter_name)
```

## Architecture & Training

- Adapter architecture: pfeiffer
- 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/distilbert-base-uncased_nli_rte_pfeiffer.yaml*.