Adapters for the paper "M2QA: Multi-domain Multilingual Question Answering".
We evaluate 2 setups: MAD-X+Domain and MAD-X²
AdapterHub
university
AI & ML interests
Parameter-Efficient Fine-Tuning
Organization Card
Adapters
A Unified Library for Parameter-Efficient and Modular Transfer Learning
💻 Website • 📚 Documentation • 📜 Paper • 🧪 Notebook Tutorials
Adapters is an add-on library to HuggingFace's Transformers, integrating various adapter methods into state-of-the-art pre-trained language models with minimal coding overhead for training and inference.
pip install adapters
🤗 Hub integration: https://docs.adapterhub.ml/huggingface_hub.html
models
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AdapterHub/m2qa-xlm-roberta-base-mad-x-2-product-reviews
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AdapterHub/m2qa-xlm-roberta-base-mad-x-2-creative-writing
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AdapterHub/m2qa-xlm-roberta-base-mad-x-2-news
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AdapterHub/m2qa-xlm-roberta-base-mad-x-2-wiki
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AdapterHub/m2qa-xlm-roberta-base-mad-x-2-turkish
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AdapterHub/m2qa-xlm-roberta-base-mad-x-2-chinese
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AdapterHub/m2qa-xlm-roberta-base-mad-x-2-german
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AdapterHub/m2qa-xlm-roberta-base-mad-x-2-qa-head
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AdapterHub/m2qa-xlm-roberta-base-mad-x-2-english
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AdapterHub/m2qa-xlm-roberta-base-mad-x-domain-product-reviews
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datasets
None public yet