See also: spaCy, which is an open-source NLP framework that has some integration with Keras as well: https://news.ycombinator.com/item?id=13874787
...and apparently will release a major version update today. Ouch.
...and apparently will release a major version update today. Ouch.
>On the time-honoured benchmark for this task, Parsey McParseface achieves over 94% accuracy, at around 600 words per second. On the same task, spaCy achieves 92.4%, at around 15,000 words per second. The extra accuracy might not sound like much, but for applications, it's likely to be pretty significant.
If spaCy is able to increment the accuracy and maintain the large performance gap, it'll still be my go-to NLP framework!
I do wish SyntaxNet were a bit easier to use. A lot of people have asked for SyntaxNet as a backend for spaCy, and I'd love to be using it in a training ensemble. When I tried this last year, I had a lot of trouble getting it to work as a library. I spent days trying to pass it text in memory from Python, and it seemed like I would have to write a new C++ tensorflow op. Has anyone gotten this to work yet?
I wonder if the spaCy update will go deep :)
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.