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
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
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
処理を実行中です
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く