<!---
Copyright 2020 The AdapterHub Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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<p align="center">
<img style="vertical-align:middle" src="https://raw.githubusercontent.com/Adapter-Hub/adapters/main/docs/img/adapter-bert.png" width="80" />
</p>
<h1 align="center">
<span><i>Adapters</i></span>
</h1>
<h3 align="center">
A Unified Library for Parameter-Efficient and Modular Transfer Learning
</h3>
<h3 align="center">
<a href="https://adapterhub.ml">Website</a>
•
<a href="https://docs.adapterhub.ml">Documentation</a>
•
<a href="https://arxiv.org/abs/2311.11077">Paper</a>
</h3>
[](https://github.com/adapter-hub/adapters/blob/main/LICENSE)
[](https://pypi.org/project/adapters/)
_Adapters_ is an add-on library to [HuggingFace's Transformers](https://github.com/huggingface/transformers), integrating [10+ adapter methods](https://docs.adapterhub.ml/overview.html) into [20+ state-of-the-art Transformer models](https://docs.adapterhub.ml/model_overview.html) with minimal coding overhead for training and inference.
_Adapters_ provides a unified interface for efficient fine-tuning and modular transfer learning, supporting a myriad of features like full-precision or quantized training (e.g. [Q-LoRA, Q-Bottleneck Adapters, or Q-PrefixTuning](https://github.com/Adapter-Hub/adapters/blob/main/notebooks/QLoRA_Llama_Finetuning.ipynb)), [adapter merging via task arithmetics](https://docs.adapterhub.ml/adapter_composition.html#merging-adapters) or the composition of multiple adapters via [composition blocks](https://docs.adapterhub.ml/adapter_composition.html), allowing advanced research in parameter-efficient transfer learning for NLP tasks.
> **Note**: The _Adapters_ library has replaced the [`adapter-transformers`](https://github.com/adapter-hub/adapter-transformers-legacy) package. All previously trained adapters are compatible with the new library. For transitioning, please read: https://docs.adapterhub.ml/transitioning.html.
## Installation
`adapters` currently supports **Python 3.8+** and **PyTorch 1.10+**.
After [installing PyTorch](https://pytorch.org/get-started/locally/), you can install `adapters` from PyPI ...
```
pip install -U adapters
```
... or from source by cloning the repository:
```
git clone https://github.com/adapter-hub/adapters.git
cd adapters
pip install .
```
## Quick Tour
#### Load pre-trained adapters:
```python
from adapters import AutoAdapterModel
from transformers import AutoTokenizer
model = AutoAdapterModel.from_pretrained("roberta-base")
tokenizer = AutoTokenizer.from_pretrained("roberta-base")
model.load_adapter("AdapterHub/roberta-base-pf-imdb", source="hf", set_active=True)
print(model(**tokenizer("This works great!", return_tensors="pt")).logits)
```
**[Learn More](https://docs.adapterhub.ml/loading.html)**
#### Adapt existing model setups:
```python
import adapters
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("t5-base")
adapters.init(model)
model.add_adapter("my_lora_adapter", config="lora")
model.train_adapter("my_lora_adapter")
# Your regular training loop...
```
**[Learn More](https://docs.adapterhub.ml/quickstart.html)**
#### Flexibly configure adapters:
```python
from adapters import ConfigUnion, PrefixTuningConfig, ParBnConfig, AutoAdapterModel
model = AutoAdapterModel.from_pretrained("microsoft/deberta-v3-base")
adapter_config = ConfigUnion(
PrefixTuningConfig(prefix_length=20),
ParBnConfig(reduction_factor=4),
)
model.add_adapter("my_adapter", config=adapter_config, set_active=True)
```
**[Learn More](https://docs.adapterhub.ml/overview.html)**
#### Easily compose adapters in a single model:
```python
from adapters import AdapterSetup, AutoAdapterModel
import adapters.composition as ac
model = AutoAdapterModel.from_pretrained("roberta-base")
qc = model.load_adapter("AdapterHub/roberta-base-pf-trec")
sent = model.load_adapter("AdapterHub/roberta-base-pf-imdb")
with AdapterSetup(ac.Parallel(qc, sent)):
print(model(**tokenizer("What is AdapterHub?", return_tensors="pt")))
```
**[Learn More](https://docs.adapterhub.ml/adapter_composition.html)**
## Useful Resources
HuggingFace's great documentation on getting started with _Transformers_ can be found [here](https://huggingface.co/transformers/index.html). `adapters` is fully compatible with _Transformers_.
To get started with adapters, refer to these locations:
- **[Colab notebook tutorials](https://github.com/Adapter-Hub/adapters/tree/main/notebooks)**, a series notebooks providing an introduction to all the main concepts of (adapter-)transformers and AdapterHub
- **https://docs.adapterhub.ml**, our documentation on training and using adapters with _adapters_
- **https://adapterhub.ml** to explore available pre-trained adapter modules and share your own adapters
- **[Examples folder](https://github.com/Adapter-Hub/adapters/tree/main/examples/pytorch)** of this repository containing HuggingFace's example training scripts, many adapted for training adapters
## Implemented Methods
Currently, adapters integrates all architectures and methods listed below:
| Method | Paper(s) | Quick Links |
| --- | --- | --- |
| Bottleneck adapters | [Houlsby et al. (2019)](https://arxiv.org/pdf/1902.00751.pdf)<br> [Bapna and Firat (2019)](https://arxiv.org/pdf/1909.08478.pdf) | [Quickstart](https://docs.adapterhub.ml/quickstart.html), [Notebook](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/01_Adapter_Training.ipynb) |
| AdapterFusion | [Pfeiffer et al. (2021)](https://aclanthology.org/2021.eacl-main.39.pdf) | [Docs: Training](https://docs.adapterhub.ml/training.html#train-adapterfusion), [Notebook](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/03_Adapter_Fusion.ipynb) |
| MAD-X,<br> Invertible adapters | [Pfeiffer et al. (2020)](https://aclanthology.org/2020.emnlp-main.617/) | [Notebook](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/04_Cross_Lingual_Transfer.ipynb) |
| AdapterDrop | [Rücklé et al. (2021)](https://arxiv.org/pdf/2010.11918.pdf) | [Notebook](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/05_Adapter_Drop_Training.ipynb) |
| MAD-X 2.0,<br> Embedding training | [Pfeiffer et al. (2021)](https://arxiv.org/pdf/2012.15562.pdf) | [Docs: Embeddings](https://docs.adapterhub.ml/embeddings.html), [Notebook](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/08_NER_Wikiann.ipynb) |
| Prefix Tuning | [Li and Liang (2021)](https://arxiv.org/pdf/2101.00190.pdf) | [Docs](https://docs.adapterhub.ml/methods.html#prefix-tuning) |
| Parallel adapters,<br> Mix-and-Match adapters | [He et al. (2021)](https://arxiv.org/pdf/2110.04366.pdf) | [Docs](https://docs.adapterhub.ml/method_combinations.html#mix-and-match-adapters) |
| Compacter | [Mahabadi et al. (2021)](https://arxiv.org/pdf/2106.04647.pdf) | [Docs](https://docs.adapterhub.ml/methods.html#compacter) |
| LoRA | [Hu et al. (2021)](https://arxiv.org/pdf/2106.09685.pdf) | [Docs](https://docs.adapterhub.ml/methods.html#lora) |
| (IA)^3 | [Liu et al. (2022)](https://arxiv.org/pdf/2205.05638.pdf) | [Docs](https://docs.adapterhub.ml/methods.html#ia-3) |
| UniPELT | [Mao et al. (2022)](https://arxiv.org/pdf/2110.07577.pdf) | [Docs](https://docs.adapterhub.ml/method_combinations.html#unipelt) |
| Prompt Tuning | [Lester et al. (2021)](https://aclanthology.org/2021.emnlp-main.243/) | [Docs](https://docs.adapterhub.ml/methods.html#prompt-tuning) |
| QLoRA | [Dettmers et al. (2023)](https://arxiv.org/pdf/2305.14314.pdf) | [Notebook](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/QLoRA_Llama_Finetuning.ipynb) |
| ReFT | [Wu et al. (2024)](https://arxiv.org/pdf/2404.03592) | [Docs](https://docs.adapterhub.ml/methods.html#reft) |
| Adapter Task Arithmetics | [Chronopoulou et al. (2023)](https://arxiv.org/abs/2311.09344)<br> [Zhang et al. (2023)](https://proceedings.neurips.cc/paper_files/paper/2023/hash/299a08ee712d4752c890938da99a77c6-Abstract-Conference.html) | [Docs](https://docs.adapterhub.ml/merging_adapters.html), [Notebook](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/06_Task_Arithmetics.ipynb)|
## Supported Models
We currently support the PyTorch versions of all models listed on the **[Model Overview](https://docs.adapterhub.ml/model_overview.html) page** in our documentation.
## Developing & Contributing
To get started with developing on _Adapters_ yourself and learn more about ways to contribute, please see https://docs.adapterhub.ml/contributing.html.
## Citation
If you use _Adapters_ in your work, please consider citing our library paper: [Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning](https://arxiv.org/abs/2311.11077)
```
@inproceedings{poth-etal-2023-adapters,
title = "Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning",
author = {Poth, Clifton and
Sterz, Hannah and
Paul, Indraneil and
Purkayastha, Sukannya and
Engl{\"a}nder, Leon and
Imhof, Timo and
Vuli{\'c}, Ivan and
Ruder, Sebastian and
Gurevych, Iryna and
Pfeiffer, Jonas},
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-demo.13",
pages = "149--160",
}
```
Alternatively, for the predecessor `adapter-transformers`, the Hub infrastructure and adapters uploaded by the AdapterHub team, please consider citing our initial paper: [AdapterHub: A Framework for Adapting Transformers](https://arxiv.org/abs/2007.07779)
```
@inproceedings{pfeiffer2020AdapterHub,
title={AdapterHub: A Framework for Adapting Transformers},
author={Pfeiffer, Jonas and
R{\"u}ckl{\'e}, Andreas and
Poth, Clifton and
Kamath, Aishwarya and
Vuli{\'c}, Ivan and
Ruder, Sebastian and
Cho, Kyunghyun and
Gurevych, Iryna},
booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
pages={46--54},
year={2020}
}
```
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"description": "<!---\r\nCopyright 2020 The AdapterHub Team. All rights reserved.\r\n\r\nLicensed under the Apache License, Version 2.0 (the \"License\");\r\nyou may not use this file except in compliance with the License.\r\nYou may obtain a copy of the License at\r\n\r\n http://www.apache.org/licenses/LICENSE-2.0\r\n\r\nUnless required by applicable law or agreed to in writing, software\r\ndistributed under the License is distributed on an \"AS IS\" BASIS,\r\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\nSee the License for the specific language governing permissions and\r\nlimitations under the License.\r\n-->\r\n\r\n<p align=\"center\">\r\n<img style=\"vertical-align:middle\" src=\"https://raw.githubusercontent.com/Adapter-Hub/adapters/main/docs/img/adapter-bert.png\" width=\"80\" />\r\n</p>\r\n<h1 align=\"center\">\r\n<span><i>Adapters</i></span>\r\n</h1>\r\n\r\n<h3 align=\"center\">\r\nA Unified Library for Parameter-Efficient and Modular Transfer Learning\r\n</h3>\r\n<h3 align=\"center\">\r\n <a href=\"https://adapterhub.ml\">Website</a>\r\n \u2022 \r\n <a href=\"https://docs.adapterhub.ml\">Documentation</a>\r\n \u2022 \r\n <a href=\"https://arxiv.org/abs/2311.11077\">Paper</a>\r\n</h3>\r\n\r\n\r\n[](https://github.com/adapter-hub/adapters/blob/main/LICENSE)\r\n[](https://pypi.org/project/adapters/)\r\n\r\n_Adapters_ is an add-on library to [HuggingFace's Transformers](https://github.com/huggingface/transformers), integrating [10+ adapter methods](https://docs.adapterhub.ml/overview.html) into [20+ state-of-the-art Transformer models](https://docs.adapterhub.ml/model_overview.html) with minimal coding overhead for training and inference.\r\n\r\n_Adapters_ provides a unified interface for efficient fine-tuning and modular transfer learning, supporting a myriad of features like full-precision or quantized training (e.g. [Q-LoRA, Q-Bottleneck Adapters, or Q-PrefixTuning](https://github.com/Adapter-Hub/adapters/blob/main/notebooks/QLoRA_Llama_Finetuning.ipynb)), [adapter merging via task arithmetics](https://docs.adapterhub.ml/adapter_composition.html#merging-adapters) or the composition of multiple adapters via [composition blocks](https://docs.adapterhub.ml/adapter_composition.html), allowing advanced research in parameter-efficient transfer learning for NLP tasks.\r\n\r\n> **Note**: The _Adapters_ library has replaced the [`adapter-transformers`](https://github.com/adapter-hub/adapter-transformers-legacy) package. All previously trained adapters are compatible with the new library. For transitioning, please read: https://docs.adapterhub.ml/transitioning.html.\r\n\r\n\r\n## Installation\r\n\r\n`adapters` currently supports **Python 3.8+** and **PyTorch 1.10+**.\r\nAfter [installing PyTorch](https://pytorch.org/get-started/locally/), you can install `adapters` from PyPI ...\r\n\r\n```\r\npip install -U adapters\r\n```\r\n\r\n... or from source by cloning the repository:\r\n\r\n```\r\ngit clone https://github.com/adapter-hub/adapters.git\r\ncd adapters\r\npip install .\r\n```\r\n\r\n\r\n## Quick Tour\r\n\r\n#### Load pre-trained adapters:\r\n\r\n```python\r\nfrom adapters import AutoAdapterModel\r\nfrom transformers import AutoTokenizer\r\n\r\nmodel = AutoAdapterModel.from_pretrained(\"roberta-base\")\r\ntokenizer = AutoTokenizer.from_pretrained(\"roberta-base\")\r\n\r\nmodel.load_adapter(\"AdapterHub/roberta-base-pf-imdb\", source=\"hf\", set_active=True)\r\n\r\nprint(model(**tokenizer(\"This works great!\", return_tensors=\"pt\")).logits)\r\n```\r\n\r\n**[Learn More](https://docs.adapterhub.ml/loading.html)**\r\n\r\n#### Adapt existing model setups:\r\n\r\n```python\r\nimport adapters\r\nfrom transformers import AutoModelForSequenceClassification\r\n\r\nmodel = AutoModelForSequenceClassification.from_pretrained(\"t5-base\")\r\n\r\nadapters.init(model)\r\n\r\nmodel.add_adapter(\"my_lora_adapter\", config=\"lora\")\r\nmodel.train_adapter(\"my_lora_adapter\")\r\n\r\n# Your regular training loop...\r\n```\r\n\r\n**[Learn More](https://docs.adapterhub.ml/quickstart.html)**\r\n\r\n#### Flexibly configure adapters:\r\n\r\n```python\r\nfrom adapters import ConfigUnion, PrefixTuningConfig, ParBnConfig, AutoAdapterModel\r\n\r\nmodel = AutoAdapterModel.from_pretrained(\"microsoft/deberta-v3-base\")\r\n\r\nadapter_config = ConfigUnion(\r\n PrefixTuningConfig(prefix_length=20),\r\n ParBnConfig(reduction_factor=4),\r\n)\r\nmodel.add_adapter(\"my_adapter\", config=adapter_config, set_active=True)\r\n```\r\n\r\n**[Learn More](https://docs.adapterhub.ml/overview.html)**\r\n\r\n#### Easily compose adapters in a single model:\r\n\r\n```python\r\nfrom adapters import AdapterSetup, AutoAdapterModel\r\nimport adapters.composition as ac\r\n\r\nmodel = AutoAdapterModel.from_pretrained(\"roberta-base\")\r\n\r\nqc = model.load_adapter(\"AdapterHub/roberta-base-pf-trec\")\r\nsent = model.load_adapter(\"AdapterHub/roberta-base-pf-imdb\")\r\n\r\nwith AdapterSetup(ac.Parallel(qc, sent)):\r\n print(model(**tokenizer(\"What is AdapterHub?\", return_tensors=\"pt\")))\r\n```\r\n\r\n**[Learn More](https://docs.adapterhub.ml/adapter_composition.html)**\r\n\r\n## Useful Resources\r\n\r\nHuggingFace's great documentation on getting started with _Transformers_ can be found [here](https://huggingface.co/transformers/index.html). `adapters` is fully compatible with _Transformers_.\r\n\r\nTo get started with adapters, refer to these locations:\r\n\r\n- **[Colab notebook tutorials](https://github.com/Adapter-Hub/adapters/tree/main/notebooks)**, a series notebooks providing an introduction to all the main concepts of (adapter-)transformers and AdapterHub\r\n- **https://docs.adapterhub.ml**, our documentation on training and using adapters with _adapters_\r\n- **https://adapterhub.ml** to explore available pre-trained adapter modules and share your own adapters\r\n- **[Examples folder](https://github.com/Adapter-Hub/adapters/tree/main/examples/pytorch)** of this repository containing HuggingFace's example training scripts, many adapted for training adapters\r\n\r\n## Implemented Methods\r\n\r\nCurrently, adapters integrates all architectures and methods listed below:\r\n\r\n| Method | Paper(s) | Quick Links |\r\n| --- | --- | --- |\r\n| Bottleneck adapters | [Houlsby et al. (2019)](https://arxiv.org/pdf/1902.00751.pdf)<br> [Bapna and Firat (2019)](https://arxiv.org/pdf/1909.08478.pdf) | [Quickstart](https://docs.adapterhub.ml/quickstart.html), [Notebook](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/01_Adapter_Training.ipynb) |\r\n| AdapterFusion | [Pfeiffer et al. (2021)](https://aclanthology.org/2021.eacl-main.39.pdf) | [Docs: Training](https://docs.adapterhub.ml/training.html#train-adapterfusion), [Notebook](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/03_Adapter_Fusion.ipynb) |\r\n| MAD-X,<br> Invertible adapters | [Pfeiffer et al. (2020)](https://aclanthology.org/2020.emnlp-main.617/) | [Notebook](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/04_Cross_Lingual_Transfer.ipynb) |\r\n| AdapterDrop | [R\u00fcckl\u00e9 et al. (2021)](https://arxiv.org/pdf/2010.11918.pdf) | [Notebook](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/05_Adapter_Drop_Training.ipynb) |\r\n| MAD-X 2.0,<br> Embedding training | [Pfeiffer et al. (2021)](https://arxiv.org/pdf/2012.15562.pdf) | [Docs: Embeddings](https://docs.adapterhub.ml/embeddings.html), [Notebook](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/08_NER_Wikiann.ipynb) |\r\n| Prefix Tuning | [Li and Liang (2021)](https://arxiv.org/pdf/2101.00190.pdf) | [Docs](https://docs.adapterhub.ml/methods.html#prefix-tuning) |\r\n| Parallel adapters,<br> Mix-and-Match adapters | [He et al. (2021)](https://arxiv.org/pdf/2110.04366.pdf) | [Docs](https://docs.adapterhub.ml/method_combinations.html#mix-and-match-adapters) |\r\n| Compacter | [Mahabadi et al. (2021)](https://arxiv.org/pdf/2106.04647.pdf) | [Docs](https://docs.adapterhub.ml/methods.html#compacter) |\r\n| LoRA | [Hu et al. (2021)](https://arxiv.org/pdf/2106.09685.pdf) | [Docs](https://docs.adapterhub.ml/methods.html#lora) |\r\n| (IA)^3 | [Liu et al. (2022)](https://arxiv.org/pdf/2205.05638.pdf) | [Docs](https://docs.adapterhub.ml/methods.html#ia-3) |\r\n| UniPELT | [Mao et al. (2022)](https://arxiv.org/pdf/2110.07577.pdf) | [Docs](https://docs.adapterhub.ml/method_combinations.html#unipelt) |\r\n| Prompt Tuning | [Lester et al. (2021)](https://aclanthology.org/2021.emnlp-main.243/) | [Docs](https://docs.adapterhub.ml/methods.html#prompt-tuning) |\r\n| QLoRA | [Dettmers et al. (2023)](https://arxiv.org/pdf/2305.14314.pdf) | [Notebook](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/QLoRA_Llama_Finetuning.ipynb) |\r\n| ReFT | [Wu et al. (2024)](https://arxiv.org/pdf/2404.03592) | [Docs](https://docs.adapterhub.ml/methods.html#reft) |\r\n| Adapter Task Arithmetics | [Chronopoulou et al. (2023)](https://arxiv.org/abs/2311.09344)<br> [Zhang et al. (2023)](https://proceedings.neurips.cc/paper_files/paper/2023/hash/299a08ee712d4752c890938da99a77c6-Abstract-Conference.html) | [Docs](https://docs.adapterhub.ml/merging_adapters.html), [Notebook](https://colab.research.google.com/github/Adapter-Hub/adapters/blob/main/notebooks/06_Task_Arithmetics.ipynb)|\r\n\r\n\r\n## Supported Models\r\n\r\nWe currently support the PyTorch versions of all models listed on the **[Model Overview](https://docs.adapterhub.ml/model_overview.html) page** in our documentation.\r\n\r\n## Developing & Contributing\r\n\r\nTo get started with developing on _Adapters_ yourself and learn more about ways to contribute, please see https://docs.adapterhub.ml/contributing.html.\r\n\r\n## Citation\r\n\r\nIf you use _Adapters_ in your work, please consider citing our library paper: [Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning](https://arxiv.org/abs/2311.11077)\r\n\r\n```\r\n@inproceedings{poth-etal-2023-adapters,\r\n title = \"Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning\",\r\n author = {Poth, Clifton and\r\n Sterz, Hannah and\r\n Paul, Indraneil and\r\n Purkayastha, Sukannya and\r\n Engl{\\\"a}nder, Leon and\r\n Imhof, Timo and\r\n Vuli{\\'c}, Ivan and\r\n Ruder, Sebastian and\r\n Gurevych, Iryna and\r\n Pfeiffer, Jonas},\r\n booktitle = \"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations\",\r\n month = dec,\r\n year = \"2023\",\r\n address = \"Singapore\",\r\n publisher = \"Association for Computational Linguistics\",\r\n url = \"https://aclanthology.org/2023.emnlp-demo.13\",\r\n pages = \"149--160\",\r\n}\r\n```\r\n\r\nAlternatively, for the predecessor `adapter-transformers`, the Hub infrastructure and adapters uploaded by the AdapterHub team, please consider citing our initial paper: [AdapterHub: A Framework for Adapting Transformers](https://arxiv.org/abs/2007.07779)\r\n\r\n```\r\n@inproceedings{pfeiffer2020AdapterHub,\r\n title={AdapterHub: A Framework for Adapting Transformers},\r\n author={Pfeiffer, Jonas and\r\n R{\\\"u}ckl{\\'e}, Andreas and\r\n Poth, Clifton and\r\n Kamath, Aishwarya and\r\n Vuli{\\'c}, Ivan and\r\n Ruder, Sebastian and\r\n Cho, Kyunghyun and\r\n Gurevych, Iryna},\r\n booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},\r\n pages={46--54},\r\n year={2020}\r\n}\r\n```\r\n",
"bugtrack_url": null,
"license": "Apache",
"summary": "A Unified Library for Parameter-Efficient and Modular Transfer Learning",
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"project_urls": {
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},
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