Note: This repository holds the codebase of the Adapters library, which has replaced adapter-transformers. For the legacy codebase, go to: https://github.com/adapter-hub/adapter-transformers-legacy.

Adapters

A Unified Library for Parameter-Efficient and Modular Transfer Learning

adapters is an add-on to HuggingFace's Transformers library, integrating adapters into state-of-the-art language models by incorporating AdapterHub, a central repository for pre-trained adapter modules.

Installation

adapters currently supports Python 3.8+ and PyTorch 1.10+. After installing PyTorch, 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
git checkout adapters
cd adapters
pip install .

Quick Tour

Load pre-trained adapters:

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)

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Adapt existing model setups:

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...

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Flexibly configure adapters:

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)

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Easily compose adapters in a single model:

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

Useful Resources

HuggingFace's great documentation on getting started with Transformers can be found here. adapters is fully compatible with Transformers.

To get started with adapters, refer to these locations:

• Colab notebook tutorials, 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 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:

Supported Models

We currently support the PyTorch versions of all models listed on the Model Overview 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 this library for your work, please consider citing our paper AdapterHub: A Framework for Adapting Transformers:

@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}
}