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Adapterdrop: على كفاءة المحولات في المحولات
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نماذج المحولات باهظة الثمن لحن النغمة، والبطيئة للتناسم، ولديها متطلبات تخزين كبيرة.تتناول النهج الحديثة هذه أوجه القصور عن طريق تدريب النماذج الأصغر، مما يقلل ديناميكيا حجم النموذج، وتدريب محولات الوزن الخفيف.في هذه الورقة، نقترح Adapterdrop، وإزالة محولات من طبقات محول أقل أثناء التدريب والاستدلال، مما يشتمل على المفاهيم من الاتجاهات الثلاثة.نظهر أن Adapterdrop يمكن أن تقلل ديناميكيا من العلامة الحسابية الحسابية عند إجراء الاستدلال على مهام متعددة في وقت واحد، مع انخفاض الحد الأدنى في عروض العمل.سنقوم بمزيد من المحولات من Adaperfusion، مما يحسن كفاءة الاستدلال مع الحفاظ على أداء العمل بالكامل.
Transformer models are expensive to fine-tune, slow for inference, and have large storage requirements. Recent approaches tackle these shortcomings by training smaller models, dynamically reducing the model size, and by training light-weight adapters. In this paper, we propose AdapterDrop, removing adapters from lower transformer layers during training and inference, which incorporates concepts from all three directions. We show that AdapterDrop can dynamically reduce the computational overhead when performing inference over multiple tasks simultaneously, with minimal decrease in task performances. We further prune adapters from AdapterFusion, which improves the inference efficiency while maintaining the task performances entirely.
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