---
tags:
- token-classification
- roberta
- adapterhub:deprel/ud_ewt
- adapter-transformers
datasets:
- universal_dependencies
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-ud_deprel` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [deprel/ud_ewt](https://adapterhub.ml/explore/deprel/ud_ewt/) dataset and includes a prediction head for tagging.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-ud_deprel", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- bert
- adapter-transformers
datasets:
- quail
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-quail` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [quail](https://huggingface.co/datasets/quail/) dataset and includes a prediction head for multiple choice.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-quail", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@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",
}
```


---
tags:
- token-classification
- roberta
- adapter-transformers
datasets:
- wnut_17
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-wnut_17` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [wnut_17](https://huggingface.co/datasets/wnut_17/) dataset and includes a prediction head for tagging.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-wnut_17", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- adapterhub:rc/race
- bert
- adapter-transformers
datasets:
- race
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-race` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [rc/race](https://adapterhub.ml/explore/rc/race/) dataset and includes a prediction head for multiple choice.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-race", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@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",
}
```


---
tags:
- text-classification
- roberta
- adapterhub:sentiment/rotten_tomatoes
- adapter-transformers
datasets:
- rotten_tomatoes
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-rotten_tomatoes` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [sentiment/rotten_tomatoes](https://adapterhub.ml/explore/sentiment/rotten_tomatoes/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- token-classification
- bert
- adapter-transformers
datasets:
- wnut_17
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-wnut_17` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [wnut_17](https://huggingface.co/datasets/wnut_17/) dataset and includes a prediction head for tagging.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-wnut_17", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- text-classification
- bert
- adapterhub:qa/boolq
- adapter-transformers
datasets:
- boolq
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-boolq` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [qa/boolq](https://adapterhub.ml/explore/qa/boolq/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-boolq", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- text-classification
- roberta
- adapterhub:sts/mrpc
- adapter-transformers
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-mrpc` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [sts/mrpc](https://adapterhub.ml/explore/sts/mrpc/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-mrpc", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- text-classification
- adapter-transformers
- adapterhub:sts/qqp
- roberta
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-qqp` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [sts/qqp](https://adapterhub.ml/explore/sts/qqp/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-qqp", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- question-answering
- bert
- adapter-transformers
datasets:
- hotpot_qa
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-hotpotqa` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [hotpot_qa](https://huggingface.co/datasets/hotpot_qa/) dataset and includes a prediction head for question answering.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-hotpotqa", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- text-classification
- bert
- adapter-transformers
datasets:
- emo
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-emo` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [emo](https://huggingface.co/datasets/emo/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-emo", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- roberta
- adapter-transformers
datasets:
- swag
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-swag` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [swag](https://huggingface.co/datasets/swag/) dataset and includes a prediction head for multiple choice.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-swag", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@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",
}
```


---
tags:
- roberta
- adapterhub:comsense/copa
- adapter-transformers
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-copa` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [comsense/copa](https://adapterhub.ml/explore/comsense/copa/) dataset and includes a prediction head for multiple choice.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-copa", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@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",
}
```


---
tags:
- roberta
- adapterhub:dp/ud_ewt
- adapter-transformers
datasets:
- universal_dependencies
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-ud_en_ewt` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [dp/ud_ewt](https://adapterhub.ml/explore/dp/ud_ewt/) dataset and includes a prediction head for dependency parsing.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-ud_en_ewt", source="hf", set_active=True)
```

## Architecture & Training

This adapter was trained using adapter-transformer's example script for dependency parsing.
See https://github.com/Adapter-Hub/adapter-transformers/tree/master/examples/dependency-parsing.

## Evaluation results

Scores achieved by dependency parsing adapters on the test set of UD English EWT after training:

| Model | UAS | LAS |
| --- | --- | --- |
| `bert-base-uncased` | 91.74 | 89.15 |
| `roberta-base` | 91.43 | 88.43 |

## Citation

<!-- Add some description here -->


---
tags:
- text-classification
- roberta
- adapterhub:nli/multinli
- adapter-transformers
datasets:
- multi_nli
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-mnli` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-mnli", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- text-classification
- bert
- adapterhub:nli/scitail
- adapter-transformers
datasets:
- scitail
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-scitail` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/scitail](https://adapterhub.ml/explore/nli/scitail/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-scitail", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- text-classification
- roberta
- adapterhub:rc/record
- adapter-transformers
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-record` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [rc/record](https://adapterhub.ml/explore/rc/record/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-record", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- text-classification
- roberta
- adapter-transformers
datasets:
- yelp_polarity
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-yelp_polarity` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [yelp_polarity](https://huggingface.co/datasets/yelp_polarity/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-yelp_polarity", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- text-classification
- bert
- adapterhub:sentiment/imdb
- adapter-transformers
datasets:
- imdb
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-imdb` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/imdb](https://adapterhub.ml/explore/sentiment/imdb/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-imdb", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- token-classification
- bert
- adapterhub:ged/fce
- adapter-transformers
datasets:
- fce_error_detection
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-fce_error_detection` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [ged/fce](https://adapterhub.ml/explore/ged/fce/) dataset and includes a prediction head for tagging.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-fce_error_detection", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- token-classification
- bert
- adapterhub:ner/mit_movie_trivia
- adapter-transformers
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-mit_movie_trivia` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [ner/mit_movie_trivia](https://adapterhub.ml/explore/ner/mit_movie_trivia/) dataset and includes a prediction head for tagging.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-mit_movie_trivia", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- text-classification
- roberta
- adapter-transformers
datasets:
- emo
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-emo` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [emo](https://huggingface.co/datasets/emo/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-emo", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- text-classification
- bert
- adapter-transformers
datasets:
- trec
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-trec` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [trec](https://huggingface.co/datasets/trec/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-trec", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- text-classification
- adapter-transformers
- bert
- adapterhub:nli/sick
datasets:
- sick
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-sick` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/sick](https://adapterhub.ml/explore/nli/sick/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-sick", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- question-answering
- bert
- adapter-transformers
datasets:
- duorc
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-duorc_p` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [duorc](https://huggingface.co/datasets/duorc/) dataset and includes a prediction head for question answering.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-duorc_p", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- token-classification
- bert
- adapterhub:chunk/conll2000
- adapter-transformers
datasets:
- conll2000
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-conll2000` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [chunk/conll2000](https://adapterhub.ml/explore/chunk/conll2000/) dataset and includes a prediction head for tagging.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-conll2000", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- text-classification
- roberta
- adapter-transformers
datasets:
- emotion
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-emotion` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [emotion](https://huggingface.co/datasets/emotion/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-emotion", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- text-classification
- roberta
- adapterhub:nli/qnli
- adapter-transformers
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-qnli` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [nli/qnli](https://adapterhub.ml/explore/nli/qnli/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-qnli", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- text-classification
- 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](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/rotten_tomatoes](https://adapterhub.ml/explore/sentiment/rotten_tomatoes/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- question-answering
- roberta
- adapterhub:qa/squad2
- adapter-transformers
datasets:
- squad_v2
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-squad_v2` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [qa/squad2](https://adapterhub.ml/explore/qa/squad2/) dataset and includes a prediction head for question answering.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-squad_v2", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- question-answering
- bert
- adapter-transformers
datasets:
- duorc
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-duorc_s` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [duorc](https://huggingface.co/datasets/duorc/) dataset and includes a prediction head for question answering.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-duorc_s", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- question-answering
- roberta
- adapter-transformers
datasets:
- duorc
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-duorc_s` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [duorc](https://huggingface.co/datasets/duorc/) dataset and includes a prediction head for question answering.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-duorc_s", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- question-answering
- roberta
- adapterhub:qa/cq
- adapter-transformers
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-cq` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [qa/cq](https://adapterhub.ml/explore/qa/cq/) dataset and includes a prediction head for question answering.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-cq", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- token-classification
- roberta
- adapterhub:chunk/conll2000
- adapter-transformers
datasets:
- conll2000
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-conll2000` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [chunk/conll2000](https://adapterhub.ml/explore/chunk/conll2000/) dataset and includes a prediction head for tagging.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-conll2000", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- text-classification
- roberta
- adapterhub:qa/boolq
- adapter-transformers
datasets:
- boolq
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-boolq` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [qa/boolq](https://adapterhub.ml/explore/qa/boolq/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-boolq", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- question-answering
- bert
- adapterhub:qa/cq
- adapter-transformers
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-cq` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [qa/cq](https://adapterhub.ml/explore/qa/cq/) dataset and includes a prediction head for question answering.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-cq", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- bert
- adapter-transformers
datasets:
- art
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-art` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [art](https://huggingface.co/datasets/art/) dataset and includes a prediction head for multiple choice.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-art", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@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",
}
```


---
tags:
- text-classification
- bert
- adapterhub:sts/sts-b
- adapter-transformers
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-stsb` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sts/sts-b](https://adapterhub.ml/explore/sts/sts-b/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-stsb", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- text-classification
- bert
- adapter-transformers
datasets:
- emotion
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-emotion` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [emotion](https://huggingface.co/datasets/emotion/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-emotion", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- text-classification
- roberta
- adapterhub:nli/scitail
- adapter-transformers
datasets:
- scitail
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-scitail` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [nli/scitail](https://adapterhub.ml/explore/nli/scitail/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-scitail", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- text-classification
- bert
- adapter-transformers
datasets:
- yelp_polarity
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-yelp_polarity` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [yelp_polarity](https://huggingface.co/datasets/yelp_polarity/) dataset and includes a prediction head for classification.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-yelp_polarity", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- question-answering
- bert
- adapter-transformers
datasets:
- newsqa
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-newsqa` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [newsqa](https://huggingface.co/datasets/newsqa/) dataset and includes a prediction head for question answering.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-newsqa", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```


---
tags:
- bert
- adapter-transformers
datasets:
- social_i_qa
language:
- en
---

# Adapter `AdapterHub/bert-base-uncased-pf-social_i_qa` for bert-base-uncased

An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [social_i_qa](https://huggingface.co/datasets/social_i_qa/) dataset and includes a prediction head for multiple choice.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-social_i_qa", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@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",
}
```


---
tags:
- roberta
- adapterhub:comsense/cosmosqa
- adapter-transformers
datasets:
- cosmos_qa
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-cosmos_qa` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [comsense/cosmosqa](https://adapterhub.ml/explore/comsense/cosmosqa/) dataset and includes a prediction head for multiple choice.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-cosmos_qa", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@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",
}
```


---
tags:
- question-answering
- roberta
- adapter-transformers
datasets:
- duorc
language:
- en
---

# Adapter `AdapterHub/roberta-base-pf-duorc_p` for roberta-base

An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [duorc](https://huggingface.co/datasets/duorc/) dataset and includes a prediction head for question answering.

This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/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](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from transformers import AutoModelWithHeads

model = AutoModelWithHeads.from_pretrained("roberta-base")
adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-duorc_p", 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](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).

## Evaluation results

Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.

## Citation

If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):

```bibtex
@inproceedings{poth-etal-2021-pre,
    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
    author = {Poth, Clifton  and
      Pfeiffer, Jonas  and
      R{"u}ckl{'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.827",
    pages = "10585--10605",
}
```