FastText.zip:Compressing text classification models
Table 2: Performance on very small models. We use a quantization with k=1, hashing and an extreme pruning. The last row shows the average drop of performance for different size.
['Dataset AG', 'full 65M', 'full 92.1', '64KiB 91.4', '32KiB 90.6', '16 KiB 89.1']
Surprisingly, even at 64KiB and 32KiB, the drop of performance is only around 0.8% and 1.7% despite a compression rate of ×1,000−4,000.