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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. Adapters provide a unified interface for efficient fine-tuning and modular transfer learning, supporting a myriad of features like full-precision or quantized training (e.g. Q-LoRA, Q-Bottleneck Adapters, or Q-PrefixTuning), adapter merging via task arithmetics or the composition of multiple adapters...

Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SRU), a light recurrent unit that balances model capacity and scalability. SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate the training of deep models. We demonstrate the effectiveness of SRU on multiple NLP tasks. SRU...

Basic natural language processor capable of solving elementary math word problems such as: 1) John has 7 pencils. He buys 10 more pencils. How many pencils does he have in all? 2) There are 6 boys and 4 girls in a library. How many kids are there altogether? 3) Bob ran 8 miles and Stella ran 2 miles. How many miles did they run altogether? 4) What is the sum of 7 and 2? 5) What is 8 divided by 2? 6) What is the 10 subtracted by 2? 7) What is 2 subtracted from 10? Also shows users...