I think spaCy uses perceptrons (essentially a shallow neural network) so it should be faster. Accuracy is pretty similar with SyntaxNet at least on the training data but I'm guessing SyntaxNet works better on long range dependencies.
The current update uses the linear model. I've also been working on neural network models, and more generally, better integration into deep learning workflows. That'll be the 2.0 release.
I've learned a lot while doing the neural network models, though. The 1.7 model takes advantage of this by having a more sophisticated optimizer. Specifically, I use an online L1 penalty and the Adam optimizer with averaged parameters. The L1 penalty allows control of size/accuracy trade-off.
This means we're finally shipping a small model: 50mb in total, compared to the current 1gb. The small model makes about 15-20% more errors.
syllogism|9 years ago
I've learned a lot while doing the neural network models, though. The 1.7 model takes advantage of this by having a more sophisticated optimizer. Specifically, I use an online L1 penalty and the Adam optimizer with averaged parameters. The L1 penalty allows control of size/accuracy trade-off.
This means we're finally shipping a small model: 50mb in total, compared to the current 1gb. The small model makes about 15-20% more errors.