spaCy

Current discussion: https://github.com/explosion/spaCy/issues/1508 I'm getting around 8k words per second on the smallest Google Cloud instances. You couldn't run spaCy 1 on these instances (or on AWS lambda) due to memory usage problems, especially problems predicting memory usage for long-running processes. This is why we say spaCy 2 is cheaper to run in a cents-per-word sense than spaCy 1. This is the performance measure that we think is most important. However, users are still reporting performance problems, so I wouldn't call the issue resolved. spaCy 1 managed to avoid depending on numpy during prediction, making it easy to ensure that performance didn't depend on anyone's environment. spaCy 2 currently does use numpy, introducing these questions around configuration. I'm working to fix this by implementing the forward pass entirely

This is amazing -- we have a small mountain of Cython code, and almost everything is working out of the box! Tests failing: * https://github.com/explosion/spaCy Confirmed working: * https://github.com/explosion/thinc * https://github.com/explosion/preshed * https://github.com/explosion/cymem * https://github.com/explosion/murmurhash I doubt spaCy will ever be faster on PyPy (the neural network library Thinc is currently 50% slower). It'd still be really great to get it running, so people who benefit from PyPy for other parts of their stack don't have to manage two Python environments.

https://github.com/explosion/spaCy/blob/develop/examples/tra... You'll need to `pip install spacy-nightly` -- it uses spaCy 2 alpha. Until spaCy 2 stabilises it'll be a bit unstable, and unfortunately it's not in the docs yet. You'll likely get better results by running `spacy download en_vectors_web_lg` and doing `nlp = spacy.load('en_vectors_web_lg')`. This will download word vectors, trained on an enormous web dump by Stanford NLP using their GloVe algorithm. Once the model is trained you can do `nlp.to_disk(output_directory)`, and then run `spacy package <model directory> <package directory>`. This will setup the model data as a Python package, so that you can run `setup.py sdist`. You'll then get a self-contained Python package that exposes a `load()` function, to give you back the `nlp` object. (Note that if you do base the model on