  Voice-assistants text classification and named-entity recognition (NER)
models are trained on millions of example utterances. Because of the large
datasets, long training time is one of the bottlenecks for releasing improved
models. In this work, we develop F10-SGD, a fast optimizer for text
classification and NER elastic-net linear models. On internal datasets, F10-SGD
provides 4x reduction in training time compared to the OWL-QN optimizer without
loss of accuracy or increase in model size. Furthermore, we incorporate biased
sampling that prioritizes harder examples towards the end of the training. As a
result, in addition to faster training, we were able to obtain statistically
significant accuracy improvements for NER.
  On public datasets, F10-SGD obtains 22% faster training time compared to
FastText for text classification. And, 4x reduction in training time compared
to CRFSuite OWL-QN for NER.
