Building an efficient neural language model over a billion words

What this post added

Introduced adaptive softmax, a novel softmax function approximation tailored for GPUs that exploits word distribution to form clusters, minimizing computational complexity for training neural language models over large vocabularies. Open-sourced torch-rnnlib, a library for designing and testing recurrent models on GPUs, with seamless access to fast baselines through torch.cudnn bindings. Demonstrated achieving 12,500 words/sec on a single GPU with adaptive softmax and torch-rnnlib, enabling state-of-the-art model training with limited resources.