GitHub - adapter-hub/adapters: A Unified Library for Parameter ...
Adapters is an add-on library to HuggingFace's Transformers, integrating 10+ adapter methods into 20+ state-of-the-art Transformer models with minimal coding overhead for training and inference.
Using Adapters at Hugging Face
Adapters is an add-on library to 🤗 transformers for efficiently fine-tuning pre-trained language models using adapters and other parameter-efficient methods. Adapters also provides various methods for composition of adapter modules during training and inference. You can learn more about this in the Adapters paper. Exploring Adapters on the Hub
HenningBuhl/adapter-transformers - GitHub
adapter-transformers is an extension of HuggingFace's Transformers library, integrating adapters into state-of-the-art language models by incorporating AdapterHub, a central repository for pre-trained adapter modules.
adapters - PyPI
2024年11月2日 · Adapters is an add-on library to HuggingFace's Transformers, integrating 10+ adapter methods into 20+ state-of-the-art Transformer models with minimal coding overhead for training and inference.
AdapterHub - Home of Adapters, the library for parameter …
"Adapter" refers to a set of newly introduced weights, typically within the layers of a transformer model. Adapters provide an alternative to fully fine-tuning the model for each downstream task, while maintaining performance.
Adapter-Transformers v3 - Unifying Efficient Fine-Tuning
2022年3月21日 · Version 3.0 of adapter-transformers integrates a first batch of new efficient fine-tuning methods. These include Prefix Tuning (Li and Liang, 2021), Parallel adapters, Mix-and-Match adapters (He et al., 2021) and Compacters (Mahabadi et al., 2021).
Adapter Methods — AdapterHub documentation
On this page, we present all adapter methods currently integrated into the adapters library. A tabular overview of adapter methods is provided here. Additionally, options to combine multiple adapter methods in a single setup are presented on the next page. Configuration class: BnConfig.
AdapterHub Documentation — AdapterHub documentation
AdapterHub is a framework simplifying the integration, training and usage of adapters and other efficient fine-tuning methods for Transformer-based language models.
1️⃣ Training an Adapter for a Transformer model
In this notebook, we train an adapter for a RoBERTa (Liu et al., 2019) model for sequence classification on a sentiment analysis task using adapter-transformers, the AdapterHub adaptation of...
Using Adapter Transformers at Hugging Face - Paperspace Blog
The combination of adapter transformers and AdapterHub provides a robust and time-saving method for refining pre-trained language models and enabling transfer learning. You can easily add adapters to your current transformer models and …