Scaling Laws for Neural Language Models
We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the dependence
MegatronLM: Training Billion+ Parameter Language Models Using GPU Model Parallelism
Larger language models are dramatically more useful for NLP tasks such as article completion, question answering, and dialog systems. Training the largest neural language model has recently been the best way to advance the state of the art in NLP applications. Two recent papers, BERT and GPT-2, demonstrate the benefits of large scale language modeling. Both papers leverage advances in compute and
Deep Tabnine - Tabnine Blog
Update (August 19): We’ve released Tabnine Local, which lets you run Deep Tabnine on your own machine. TL;DR: Tabnine is an autocompleter that helps you write code faster. We’re adding a deep learning model which significantly improves suggestion quality. You can see videos below and you can sign up for it here. There has been a lot of hype about deep learning in the past few years. Neural network
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Gradio | Hub
Measuring Coding Challenge Competence With APPS
Scaling Laws for Autoregressive Generative Modeling
As Good as New. How to Successfully Recycle English GPT-2 to Make Models for Other Languages
GeDi: Generative Discriminator Guided Sequence Generation
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ConvAI - a Hugging Face Space by banana-projects