Efficient Contextualized Representation:Language Model Pruning for Sequence Labeling
Table 4: Speed comparison in the CoNLL03 NER task. We can observe that LD-Net (9, pruned) achieved about 5 times speed up on the wall-clock time over LD-Net (9, origin).
['Network (LMs Ind.#)', 'FLOPs', 'Batch size', 'Peak RAM', 'Time (s)', 'Speed 103words/s', 'Speed 103sents/s']
Specifically, due to the limited size of CoNLL03 test set, we measure such speeds on the training set. Although there is still a large margin between the actual speed-up and the FLOPs speed-up, we think the resulting decode speed (166K words/s) is sufficient for most real-world applications.