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Update v2 docs and add benchmarks stub

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ines 2017-06-04 15:34:28 +02:00
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website/docs/usage/v2.jade
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@ -3,58 +3,69 @@
include ../../_includes/_mixins
p
| We're very excited to finally introduce spaCy v2.0. This release features
| entirely new deep learning-powered models for spaCy's tagger, parser and
| entity recognizer. The new models are #[strong 20x smaller] than the linear
| models that have powered spaCy until now: from 300mb to only 14mb. Speed
| and accuracy are currently comparable to the 1.x models: speed on CPU is
| slightly lower, while accuracy is slightly higher. We expect performance to
| improve quickly between now and the release date, as we run more experiments
| and optimize the implementation.
p
| The main usability improvements you'll notice in spaCy 2 are around the
| defining, training and loading your own models and components. The new neural
| network models make it much easier to train a model from scratch, or update
| an existing model with a few examples. In v1, the statistical models depended
| on the state of the vocab. If you taught the model a new word, you would have
| to save and load a lot of data -- otherwise the model wouldn't correctly
| recall the features of your new example. That's no longer the case. Due to some
| clever use of hashing, the statistical models never change size, even as they
| learn new vocabulary items. The whole pipeline is also now fully differentiable,
| so even if you don't have explicitly annotated data, you can update spaCy using
| all the latest deep learning tricks: adversarial training, noise contrastive
| estimation, reinforcement learning, etc.
p
| Finally, we've made several usability improvements that are particularly helpful
| for production deployments. spaCy 2 now fully supports the Pickle protocol,
| making it easy to use spaCy with Apache Spark. The string-to-integer mapping is
| no longer stateful, making it easy to reconcile annotations made in different
| processes. Models are smaller and use less memory, and the APIs for serialization
| are now much more consistent.
p
| Because we'e made so many architectural changes to the library, we've tried to
| keep breaking changes to a minimum. A lot of projects follow the philosophy that
| if you're going to break anything, you may as well break everything. We think
| migration is easier if there's a logic to what's changed. We've therefore followed
| a policy of avoiding breaking changes to the #[code Doc], #[code Span] and #[code Token]
| objects. This way, you can focus on only migrating the code that does training, loading
| and serialisation --- in other words, code that works with the #[code nlp] object directly.
| Code that uses the annotations should continue to work.
p
| On this page, you'll find a summary of the #[+a("#features") new features],
| information on the #[+a("#incompat") backwards incompatibilities],
| including a handy overview of what's been renamed or deprecated.
| To help you make the most of v2.0, we also
| We're very excited to finally introduce spaCy v2.0! On this page, you'll
| find a summary of the new features, information on the backwards
| incompatibilities, including a handy overview of what's been renamed or
| deprecated. To help you make the most of v2.0, we also
| #[strong re-wrote almost all of the usage guides and API docs], and added
| more real-world examples. If you're new to spaCy, or just want to brush
| up on some NLP basics and the details of the library, check out
| the #[+a("/docs/usage/spacy-101") spaCy 101 guide] that explains the most
| important concepts with examples and illustrations.
+h(2, "summary") Summary
+grid.o-no-block
+grid-col("half")
p This release features
| entirely new #[strong deep learning-powered models] for spaCy's tagger,
| parser and entity recognizer. The new models are #[strong 20x smaller]
| than the linear models that have powered spaCy until now: from 300 MB to
| only 14 MB.
p
| We've also made several usability improvements that are
| particularly helpful for #[strong production deployments]. spaCy
| v2 now fully supports the Pickle protocol, making it easy to use
| spaCy with #[+a("https://spark.apache.org/") Apache Spark]. The
| string-to-integer mapping is #[strong no longer stateful], making
| it easy to reconcile annotations made in different processes.
| Models are smaller and use less memory, and the APIs for serialization
| are now much more consistent.
+table-of-contents
+item #[+a("#summary") Summary]
+item #[+a("#features") New features]
+item #[+a("#features-pipelines") Improved processing pipelines]
+item #[+a("#features-hash-ids") Hash values instead of integer IDs]
+item #[+a("#features-serializer") Saving, loading and serialization]
+item #[+a("#features-displacy") displaCy visualizer]
+item #[+a("#features-language") Language data and lazy loading]
+item #[+a("#features-matcher") Revised matcher API]
+item #[+a("#features-models") Neural network models]
+item #[+a("#incompat") Backwards incompatibilities]
+item #[+a("#migrating") Migrating from spaCy v1.x]
+item #[+a("#benchmarks") Benchmarks]
p
| The main usability improvements you'll notice in spaCy v2.0 are around
| #[strong defining, training and loading your own models] and components.
| The new neural network models make it much easier to train a model from
| scratch, or update an existing model with a few examples. In v1.x, the
| statistical models depended on the state of the #[code Vocab]. If you
| taught the model a new word, you would have to save and load a lot of
| data — otherwise the model wouldn't correctly recall the features of your
| new example. That's no longer the case.
p
| Due to some clever use of hashing, the statistical models
| #[strong never change size], even as they learn new vocabulary items.
| The whole pipeline is also now fully differentiable. Even if you don't
| have explicitly annotated data, you can update spaCy using all the
| #[strong latest deep learning tricks] like adversarial training, noise
| contrastive estimation or reinforcement learning.
+h(2, "features") New features
p
@ -334,19 +345,23 @@ p
+h(2, "migrating") Migrating from spaCy 1.x
p
| Because we'e made so many architectural changes to the library, we've
| tried to #[strong keep breaking changes to a minimum]. A lot of projects
| follow the philosophy that if you're going to break anything, you may as
| well break everything. We think migration is easier if there's a logic to
| what has changed.
+infobox("Some tips")
| Before migrating, we strongly recommend writing a few
| #[strong simple tests] specific to how you're using spaCy in your
| application. This makes it easier to check whether your code requires
| changes, and if so, which parts are affected.
| (By the way, feel free contribute your tests to
| #[+src(gh("spaCy", "spacy/tests")) our test suite] – this will also ensure
| we never accidentally introduce a bug in a workflow that's
| important to you.) If you've trained your own models, keep in mind that
| your train and runtime inputs must match. This means you'll have to
| #[strong retrain your models] with spaCy v2.0 to make them compatible.
p
| We've therefore followed a policy of avoiding breaking changes to the
| #[code Doc], #[code Span] and #[code Token] objects. This way, you can
| focus on only migrating the code that does training, loading and
| serialization — in other words, code that works with the #[code nlp]
| object directly. Code that uses the annotations should continue to work.
+infobox("Important note")
| If you've trained your own models, keep in mind that your train and
| runtime inputs must match. This means you'll have to
| #[strong retrain your models] with spaCy v2.0.
+h(3, "migrating-saving-loading") Saving, loading and serialization
@ -448,3 +463,21 @@ p
| the doc, the index of the current match and all total matches. This lets
| you both accept or reject the match, and define the actions to be
| triggered.
+h(2, "benchmarks") Benchmarks
+table(["Model", "Version", "Type", "UAS", "LAS", "NER F", "POS", "w/s"])
+row
+cell #[code en_core_web_sm]
for cell in ["2.0.0", "neural", "", "", "", "", ""]
+cell=cell
+row
+cell #[code es_dep_web_sm]
for cell in ["2.0.0", "neural", "", "", "", "", ""]
+cell=cell
+row("divider")
+cell #[code en_core_web_sm]
for cell in ["1.1.0", "linear", "", "", "", "", ""]
+cell=cell