AdapterHub: A Framework for Adapting Transformers (2007.07779v3)

Abstract: The current modus operandi in NLP involves downloading and fine-tuning pre-trained models consisting of millions or billions of parameters. Storing and sharing such large trained models is expensive, slow, and time-consuming, which impedes progress towards more general and versatile NLP methods that learn from and for many tasks. Adapters -- small learnt bottleneck layers inserted within each layer of a pre-trained model -- ameliorate this issue by avoiding full fine-tuning of the entire model. However, sharing and integrating adapter layers is not straightforward. We propose AdapterHub, a framework that allows dynamic "stitching-in" of pre-trained adapters for different tasks and languages. The framework, built on top of the popular HuggingFace Transformers library, enables extremely easy and quick adaptations of state-of-the-art pre-trained models (e.g., BERT, RoBERTa, XLM-R) across tasks and languages. Downloading, sharing, and training adapters is as seamless as possible using minimal changes to the training scripts and a specialized infrastructure. Our framework enables scalable and easy access to sharing of task-specific models, particularly in low-resource scenarios. AdapterHub includes all recent adapter architectures and can be found at https://AdapterHub.ml.

Overview of "AdapterHub: A Framework for Adapting Transformers"

The paper "AdapterHub: A Framework for Adapting Transformers" introduces a comprehensive framework designed to address challenges associated with fine-tuning large pre-trained Transformer models. This paper is of particular interest to researchers in NLP who are grappling with the computational and storage demands posed by the current practices of transferring learning in these large models.

Key Contributions

The authors propose AdapterHub, a robust platform that incorporates small, task-specific layers called adapters into Transformer-based models such as BERT, RoBERTa, and XLM-R. Adapters optimize the fine-tuning process by requiring only these additional bottleneck layers to be trained rather than the entire model. This approach results in substantial reductions in computational overhead and storage requirements, facilitating more efficient sharing and storage of models.

1. Adapter Insertion and Training: Adapters, consisting of a set of newly initialized parameters at each layer of the Transformer, are finely tuned for specific tasks. The pre-trained parameters of the model remain static, allowing efficient task-specific learning.
2. Seamless Integration: Built on the HuggingFace Transformers library, AdapterHub requires minimal modifications to existing scripts. This seamless integration allows broad accessibility and utilization of state-of-the-art NLP models without the computational burden of full model fine-tuning.
3. Storage Efficiency: With adapters requiring as little as 0.9MB for task-specific adaptations, they offer a stark contrast to the gigabyte requirements of fully fine-tuned models. This storage efficiency is pivotal in scaling tasks across numerous models without prohibitive resource demands.
4. Community Sharing and Reproducibility: The platform provides mechanisms for easily storing and sharing adapter configurations, promoting collaboration and reproducibility in NLP research. AdapterHub encourages the research community to openly share their configurations, thereby multiplying the reach and impact of their work.

Numerical Results

The paper demonstrates that adapter-based approaches match the performance of traditional full fine-tuning methods on several benchmarks, such as GLUE. For instance, adapters achieve on-par performance with full model fine-tuning across tasks, highlighting the viability of this strategy.

Implications and Future Directions

The introduction of AdapterHub carries numerous implications for the field of NLP. Firstly, it enhances the modularity of LLMs, allowing adapters trained on distinct tasks or languages to be easily combined or interchanged. Secondly, the framework significantly reduces the ecological footprint of modern NLP by minimizing the need for extensive computational resources and storage space.

Looking forward, the framework opens avenues for extending adapter use beyond NLP, potentially embracing modalities such as computer vision or even transitioning across different architectures. Research could focus on optimizing adapter architectures further, exploring cross-model compatibilities, and expanding the library with user-contributed adapters for new tasks and languages.

Conclusion

AdapterHub serves as a strategic advancement in the field of NLP, offering a methodologically sound and resource-efficient approach for model adaptation. By addressing key limitations in current transfer learning practices, it substantially contributes to the scalable deployment of transformer models in real-world applications, making NLP technology more accessible and sustainable. The framework’s emphasis on community-driven development and sharing further sets a precedent for collaborative research and innovation in machine learning.