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spacy/about.py
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# https://github.com/pypa/warehouse/blob/master/warehouse/__about__.py
__title__ = 'spacy'
__version__ = '1.8.2'
__version__ = '2.0.0'
__summary__ = 'Industrial-strength Natural Language Processing (NLP) with Python and Cython'
__uri__ = 'https://spacy.io'
__author__ = 'Matthew Honnibal'
__email__ = 'matt@explosion.ai'
__author__ = 'Explosion AI'
__email__ = 'contact@explosion.ai'
__license__ = 'MIT'
__docs_models__ = 'https://spacy.io/docs/usage/models'

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website/_includes/_mixins.jade
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@ -382,3 +382,4 @@ mixin annotation-row(annots, style)
+cell #[code=cell]
else
+cell=cell
block

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website/docs/api/language.jade
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@ -141,10 +141,10 @@ p
p Update the models in the pipeline.
+aside-code("Example").
with nlp.begin_training(gold, use_gpu=True) as (trainer, optimizer):
for epoch in trainer.epochs(gold):
for docs, golds in epoch:
state = nlp.update(docs, golds, sgd=optimizer)
for raw_text, entity_offsets in train_data:
doc = nlp.make_doc(raw_text)
gold = GoldParse(doc, entities=entity_offsets)
nlp.update([doc], [gold], drop=0.5, sgd=optimizer)
+table(["Name", "Type", "Description"])
+row
@ -173,17 +173,13 @@ p Update the models in the pipeline.
+cell Results from the update.
+h(2, "begin_training") Language.begin_training
+tag contextmanager
+tag method
p
| Allocate models, pre-process training data and acquire a trainer and
| optimizer. Used as a contextmanager.
| Allocate models, pre-process training data and acquire an optimizer.
+aside-code("Example").
with nlp.begin_training(gold, use_gpu=True) as (trainer, optimizer):
for epoch in trainer.epochs(gold):
for docs, golds in epoch:
state = nlp.update(docs, golds, sgd=optimizer)
optimizer = nlp.begin_training(gold_tuples)
+table(["Name", "Type", "Description"])
+row
@ -199,7 +195,7 @@ p
+footrow
+cell yields
+cell tuple
+cell A trainer and an optimizer.
+cell An optimizer.
+h(2, "use_params") Language.use_params
+tag contextmanager

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website/docs/usage/_spacy-101/_serialization.jade
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@ -1,12 +1,12 @@
//- 💫 DOCS > USAGE > SPACY 101 > SERIALIZATION
p
| If you've been modifying the pipeline, vocabulary vectors and entities, or made
| updates to the model, you'll eventually want
| to #[strong save your progress] – for example, everything that's in your #[code nlp]
| object. This means you'll have to translate its contents and structure
| into a format that can be saved, like a file or a byte string. This
| process is called serialization. spaCy comes with
| If you've been modifying the pipeline, vocabulary, vectors and entities,
| or made updates to the model, you'll eventually want to
| #[strong save your progress] – for example, everything that's in your
| #[code nlp] object. This means you'll have to translate its contents and
| structure into a format that can be saved, like a file or a byte string.
| This process is called serialization. spaCy comes with
| #[strong built-in serialization methods] and supports the
| #[+a("http://www.diveintopython3.net/serializing.html#dump") Pickle protocol].
@ -45,11 +45,7 @@ p
| #[code Vocab] holds the context-independent information about the words,
| tags and labels, and their #[strong hash values]. If the #[code Vocab]
| wasn't saved with the #[code Doc], spaCy wouldn't know how to resolve
| those IDs – for example, the word text or the dependency labels. You
| might be saving #[code 446] for "whale", but in a different vocabulary,
| this ID could map to "VERB". Similarly, if your document was processed by
| a German model, its vocab will include the specific
| #[+a("/docs/api/annotation#dependency-parsing-german") German dependency labels].
| those IDs back to strings.
+code.
moby_dick = open('moby_dick.txt', 'r') # open a large document

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@ -1,3 +1,52 @@
//- 💫 DOCS > USAGE > SPACY 101 > TRAINING
+under-construction
p
| spaCy's models are #[strong statistical] and every "decision" they make –
| for example, which part-of-speech tag to assign, or whether a word is a
| named entity – is a #[strong prediction]. This prediction is based
| on the examples the model has seen during #[strong training]. To train
| a model, you first need training data – examples of text, and the
| labels you want the model to predict. This could be a part-of-speech tag,
| a named entity or any other information.
p
| The model is then shown the unlabelled text and will make a prediction.
| Because we know the correct answer, we can give the model feedback on its
| prediction in the form of an #[strong error gradient] of the
| #[strong loss function] that calculates the difference between the training
| example and the expected output. The greater the difference, the more
| significant the gradient and the updates to our model.
+aside
| #[strong Training data:] Examples and their annotations.#[br]
| #[strong Text:] The input text the model should predict a label for.#[br]
| #[strong Label:] The label the model should predict.#[br]
| #[strong Gradient:] Gradient of the loss function calculating the
| difference between input and expected output.
+image
include ../../../assets/img/docs/training.svg
.u-text-right
+button("/assets/img/docs/training.svg", false, "secondary").u-text-tag View large graphic
p
| When training a model, we don't just want it to memorise our examples –
| we want it to come up with theory that can be
| #[strong generalised across other examples]. After all, we don't just want
| the model to learn that this one instance of "Amazon" right here is a
| company – we want it to learn that "Amazon", in contexts #[em like this],
| is most likely a company. That's why the training data should always be
| representative of the data we want to process. A model trained on
| Wikipedia, where sentences in the first person are extremely rare, will
| likely perform badly on Twitter. Similarly, a model trained on romantic
| novels will likely perform badly on legal text.
p
| This also means that in order to know how the model is performing,
| and whether it's learning the right things, you don't only need
| #[strong training data] – you'll also need #[strong evaluation data]. If
| you only test the model with the data it was trained on, you'll have no
| idea how well it's generalising. If you want to train a model from scratch,
| you usually need at least a few hundred examples for both training and
| evaluation. To update an existing model, you can already achieve decent
| results with very few examples – as long as they're representative.

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website/docs/usage/entity-recognition.jade
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@ -154,40 +154,29 @@ p
| To provide training examples to the entity recogniser, you'll first need
| to create an instance of the #[+api("goldparse") #[code GoldParse]] class.
| You can specify your annotations in a stand-off format or as token tags.
+code.
import random
import spacy
from spacy.gold import GoldParse
from spacy.pipeline import EntityRecognizer
train_data = [('Who is Chaka Khan?', [(7, 17, 'PERSON')]),
('I like London and Berlin.', [(7, 13, 'LOC'), (18, 24, 'LOC')])]
nlp = spacy.load('en', entity=False, parser=False)
ner = EntityRecognizer(nlp.vocab, entity_types=['PERSON', 'LOC'])
for itn in range(5):
random.shuffle(train_data)
for raw_text, entity_offsets in train_data:
doc = nlp.make_doc(raw_text)
gold = GoldParse(doc, entities=entity_offsets)
nlp.tagger(doc)
ner.update(doc, gold)
p
| If a character offset in your entity annotations don't fall on a token
| boundary, the #[code GoldParse] class will treat that annotation as a
| missing value. This allows for more realistic training, because the
| entity recogniser is allowed to learn from examples that may feature
| tokenizer errors.
+aside-code("Example").
+code.
train_data = [('Who is Chaka Khan?', [(7, 17, 'PERSON')]),
('I like London and Berlin.', [(7, 13, 'LOC'), (18, 24, 'LOC')])]
+code.
doc = Doc(nlp.vocab, [u'rats', u'make', u'good', u'pets'])
gold = GoldParse(doc, [u'U-ANIMAL', u'O', u'O', u'O'])
ner = EntityRecognizer(nlp.vocab, entity_types=['ANIMAL'])
ner.update(doc, gold)
+infobox
| For more details on #[strong training and updating] the named entity
| recognizer, see the usage guides on #[+a("/docs/usage/training") training]
| and #[+a("/docs/usage/training-ner") training the named entity recognizer],
| or check out the runnable
| #[+src(gh("spaCy", "examples/training/train_ner.py")) training script]
| on GitHub.
+h(3, "updating-biluo") The BILUO Scheme
p
| You can also provide token-level entity annotation, using the

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website/docs/usage/spacy-101.jade
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@ -252,6 +252,12 @@ include _spacy-101/_serialization
include _spacy-101/_training
+infobox
| To learn more about #[strong training and updating] models, how to create
| training data and how to improve spaCy's named entity recognition models,
| see the usage guides on #[+a("/docs/usage/training") training] and
| #[+a("/docs/usage/training-ner") training the named entity recognizer].
+h(2, "architecture") Architecture
+under-construction

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@ -8,6 +8,8 @@ p
| particularly useful as a "quick and dirty solution", if you have only a
| few corrections or annotations.
+under-construction
+h(2, "improving-accuracy") Improving accuracy on existing entity types
p
@ -15,16 +17,7 @@ p
| #[+api("goldparse") #[code spacy.gold.GoldParse]], with the entity labels
| you want to learn. You will then pass this instance to the
| #[+api("entityrecognizer#update") #[code EntityRecognizer.update()]]
| method. For example:
+code.
import spacy
from spacy.gold import GoldParse
nlp = spacy.load('en')
doc = nlp.make_doc(u'Facebook released React in 2014')
gold = GoldParse(doc, entities=['U-ORG', 'O', 'U-TECHNOLOGY', 'O', 'U-DATE'])
nlp.entity.update(doc, gold)
| method.
p
| You'll usually need to provide many examples to meaningfully improve the
@ -44,100 +37,6 @@ p
| #[strong experiment on your own data] to find a solution that works best
| for you.
+h(2, "adding") Adding a new entity type
p
| You can add new entity types to an existing model. Let's say we want to
| recognise the category #[code TECHNOLOGY]. The new category will include
| programming languages, frameworks and platforms. First, we need to
| register the new entity type:
+code.
nlp.entity.add_label('TECHNOLOGY')
p
| Next, iterate over your examples, calling #[code entity.update()]. As
| above, we want to avoid iterating over only a small number of sentences.
| A useful compromise is to run the model over a number of plain-text
| sentences, and pass the entities to #[code GoldParse], as "true"
| annotations. This encourages the optimizer to find a solution that
| predicts the new category with minimal difference from the previous
| output.
+h(2, "example") Example: Adding and training an #[code ANIMAL] entity
+under-construction
p
| This script shows how to add a new entity type to an existing pre-trained
| NER model. To keep the example short and simple, only four sentences are
| provided as examples. In practice, you'll need many more —
| #[strong a few hundred] would be a good start. You will also likely need
| to mix in #[strong examples of other entity types], which might be
| obtained by running the entity recognizer over unlabelled sentences, and
| adding their annotations to the training set.
p
| For the full, runnable script of this example, see
| #[+src(gh("spacy", "examples/training/train_new_entity_type.py")) train_new_entity_type.py].
+code("Training the entity recognizer").
import spacy
from spacy.pipeline import EntityRecognizer
from spacy.gold import GoldParse
from spacy.tagger import Tagger
import random
model_name = 'en'
entity_label = 'ANIMAL'
output_directory = '/path/to/model'
train_data = [
("Horses are too tall and they pretend to care about your feelings",
[(0, 6, 'ANIMAL')]),
("horses are too tall and they pretend to care about your feelings",
[(0, 6, 'ANIMAL')]),
("horses pretend to care about your feelings",
[(0, 6, 'ANIMAL')]),
("they pretend to care about your feelings, those horses",
[(48, 54, 'ANIMAL')])
]
nlp = spacy.load(model_name)
nlp.entity.add_label(entity_label)
ner = train_ner(nlp, train_data, output_directory)
def train_ner(nlp, train_data, output_dir):
# Add new words to vocab
for raw_text, _ in train_data:
doc = nlp.make_doc(raw_text)
for word in doc:
_ = nlp.vocab[word.orth]
for itn in range(20):
random.shuffle(train_data)
for raw_text, entity_offsets in train_data:
gold = GoldParse(doc, entities=entity_offsets)
doc = nlp.make_doc(raw_text)
nlp.tagger(doc)
loss = nlp.entity.update(doc, gold)
nlp.save_to_directory(output_dir)
p
+button(gh("spaCy", "examples/training/train_new_entity_type.py"), false, "secondary") Full example
p
| The actual training is performed by looping over the examples, and
| calling #[code nlp.entity.update()]. The #[code update()] method steps
| through the words of the input. At each word, it makes a prediction. It
| then consults the annotations provided on the #[code GoldParse] instance,
| to see whether it was right. If it was wrong, it adjusts its weights so
| that the correct action will score higher next time.
p
| After training your model, you can
| #[+a("/docs/usage/saving-loading") save it to a directory]. We recommend
| wrapping models as Python packages, for ease of deployment.
+h(2, "saving-loading") Saving and loading
p

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@ -10,68 +10,193 @@ p
include _spacy-101/_training
+h(2, "train-pos-tagger") Training the part-of-speech tagger
+h(3, "training-data") How do I get training data?
p
| Collecting training data may sound incredibly painful – and it can be,
| if you're planning a large-scale annotation project. However, if your main
| goal is to update an existing model's predictions – for example, spaCy's
| named entity recognition – the hard is part usually not creating the
| actual annotations. It's finding representative examples and
| #[strong extracting potential candidates]. The good news is, if you've
| been noticing bad performance on your data, you likely
| already have some relevant text, and you can use spaCy to
| #[strong bootstrap a first set of training examples]. For example,
| after processing a few sentences, you may end up with the following
| entities, some correct, some incorrect.
+aside("How many examples do I need?")
| As a rule of thumb, you should allocate at least 10% of your project
| resources to creating training and evaluation data. If you're looking to
| improve an existing model, you might be able to start off with only a
| handful of examples. Keep in mind that you'll always want a lot more than
| that for #[strong evaluation] – especially previous errors the model has
| made. Otherwise, you won't be able to sufficiently verify that the model
| has actually made the #[strong correct generalisations] required for your
| use case.
+table(["Text", "Entity", "Start", "End", "Label", ""])
- var style = [0, 0, 1, 1, 1]
+annotation-row(["Uber blew through $1 million a week", "Uber", 0, 4, "ORG"], style)
+cell #[+procon("pro")]
+annotation-row(["Android Pay expands to Canada", "Android", 0, 7, "PERSON"], style)
+cell #[+procon("con")]
+annotation-row(["Android Pay expands to Canada", "Canada", 23, 30, "GPE"], style)
+cell #[+procon("pro")]
+annotation-row(["Spotify steps up Asia expansion", "Spotify", 0, 8, "ORG"], style)
+cell #[+procon("pro")]
+annotation-row(["Spotify steps up Asia expansion", "Asia", 17, 21, "NORP"], style)
+cell #[+procon("con")]
p
| Alternatively, the
| #[+a("/docs/usage/rule-based-matching#example3") rule-based matcher]
| can be a useful tool to extract tokens or combinations of tokens, as
| well as their start and end index in a document. In this case, we'll
| extract mentions of Google and assume they're an #[code ORG].
+table(["Text", "Entity", "Start", "End", "Label", ""])
- var style = [0, 0, 1, 1, 1]
+annotation-row(["let me google this for you", "google", 7, 13, "ORG"], style)
+cell #[+procon("con")]
+annotation-row(["Google Maps launches location sharing", "Google", 0, 6, "ORG"], style)
+cell #[+procon("con")]
+annotation-row(["Google rebrands its business apps", "Google", 0, 6, "ORG"], style)
+cell #[+procon("pro")]
+annotation-row(["look what i found on google! 😂", "google", 21, 27, "ORG"], style)
+cell #[+procon("con")]
p
| Based on the few examples above, you can already create six training
| sentences with eight entities in total. Of course, what you consider a
| "correct annotation" will always depend on
| #[strong what you want the model to learn]. While there are some entity
| annotations that are more or less universally correct – like Canada being
| a geopolitical entity – your application may have its very own definition
| of the #[+a("/docs/api/annotation#named-entities") NER annotation scheme].
+code.
from spacy.vocab import Vocab
from spacy.tagger import Tagger
from spacy.tokens import Doc
from spacy.gold import GoldParse
train_data = [
("Uber blew through $1 million a week", [(0, 4, 'ORG')]),
("Android Pay expands to Canada", [(0, 11, 'PRODUCT'), (23, 30, 'GPE')]),
("Spotify steps up Asia expansion", [(0, 8, "ORG"), (17, 21, "LOC")]),
("Google Maps launches location sharing", [(0, 11, "PRODUCT")]),
("Google rebrands its business apps", [(0, 6, "ORG")]),
("look what i found on google! 😂", [(21, 27, "PRODUCT")])]
+h(2) Training with annotations
p
| The #[+api("goldparse") #[code GoldParse]] object collects the annotated
| training examples, also called the #[strong gold standard]. It's
| initialised with the #[+api("doc") #[code Doc]] object it refers to,
| and keyword arguments specifying the annotations, like #[code tags]
| or #[code entities]. Its job is to encode the annotations, keep them
| aligned and create the C-level data structures required for efficient access.
| Here's an example of a simple #[code GoldParse] for part-of-speech tags:
+code.
vocab = Vocab(tag_map={'N': {'pos': 'NOUN'}, 'V': {'pos': 'VERB'}})
tagger = Tagger(vocab)
doc = Doc(vocab, words=['I', 'like', 'stuff'])
gold = GoldParse(doc, tags=['N', 'V', 'N'])
tagger.update(doc, gold)
p
+button(gh("spaCy", "examples/training/train_tagger.py"), false, "secondary") Full example
+h(2, "train-entity") Training the named entity recognizer
| Using the #[code Doc] and its gold-standard annotations, the model can be
| updated to learn a sentence of three words with their assigned
| part-of-speech tags. The #[+a("/docs/usage/adding-languages#tag-map") tag map]
| is part of the vocabulary and defines the annotation scheme. If you're
| training a new language model, this will let you map the tags present in
| the treebank you train on to spaCy's tag scheme.
+code.
from spacy.vocab import Vocab
from spacy.pipeline import EntityRecognizer
from spacy.tokens import Doc
vocab = Vocab()
entity = EntityRecognizer(vocab, entity_types=['PERSON', 'LOC'])
doc = Doc(vocab, words=['Who', 'is', 'Shaka', 'Khan', '?'])
entity.update(doc, ['O', 'O', 'B-PERSON', 'L-PERSON', 'O'])
doc = Doc(Vocab(), words=['Facebook', 'released', 'React', 'in', '2014'])
gold = GoldParse(doc, entities=['U-ORG', 'O', 'U-TECHNOLOGY', 'O', 'U-DATE'])
p
+button(gh("spaCy", "examples/training/train_ner.py"), false, "secondary") Full example
| The same goes for named entities. The letters added before the labels
| refer to the tags of the
| #[+a("/docs/usage/entity-recognition#updating-biluo") BILUO scheme] –
| #[code O] is a token outside an entity, #[code U] an single entity unit,
| #[code B] the beginning of an entity, #[code I] a token inside an entity
| and #[code L] the last token of an entity.
+h(2, "extend-entity") Extending the named entity recognizer
+aside
| #[strong Training data]: The training examples.#[br]
| #[strong Text and label]: The current example.#[br]
| #[strong Doc]: A #[code Doc] object created from the example text.#[br]
| #[strong GoldParse]: A #[code GoldParse] object of the #[code Doc] and label.#[br]
| #[strong nlp]: The #[code nlp] object with the model.#[br]
| #[strong Optimizer]: A function that holds state between updates.#[br]
| #[strong Update]: Update the model's weights.#[br]
| #[strong ]
+image
include ../../assets/img/docs/training-loop.svg
.u-text-right
+button("/assets/img/docs/training-loop.svg", false, "secondary").u-text-tag View large graphic
p
| All #[+a("/docs/usage/models") spaCy models] support online learning, so
| you can update a pre-trained model with new examples. You can even add
| new classes to an existing model, to recognise a new entity type,
| part-of-speech, or syntactic relation. Updating an existing model is
| particularly useful as a "quick and dirty solution", if you have only a
| few corrections or annotations.
| Of course, it's not enough to only show a model a single example once.
| Especially if you only have few examples, you'll want to train for a
| #[strong number of iterations]. At each iteration, the training data is
| #[strong shuffled] to ensure the model doesn't make any generalisations
| based on the order of examples. Another technique to improve the learning
| results is to set a #[strong dropout rate], a rate at which to randomly
| "drop" individual features and representations. This makes it harder for
| the model to memorise the training data. For example, a #[code 0.25]
| dropout means that each feature or internal representation has a 1/4
| likelihood of being dropped.
p.o-inline-list
+button(gh("spaCy", "examples/training/train_new_entity_type.py"), true, "secondary") Full example
+button("/docs/usage/training-ner", false, "secondary") Usage guide
+aside
| #[+api("language#begin_training") #[code begin_training()]]: Start the
| training and return an optimizer function to update the model's weights.#[br]
| #[+api("language#update") #[code update()]]: Update the model with the
| training example and gold data.#[br]
| #[+api("language#to_disk") #[code to_disk()]]: Save the updated model to
| a directory.
+h(2, "train-dependency") Training the dependency parser
+code("Example training loop").
optimizer = nlp.begin_training(get_data)
for itn in range(100):
random.shuffle(train_data)
for raw_text, entity_offsets in train_data:
doc = nlp.make_doc(raw_text)
gold = GoldParse(doc, entities=entity_offsets)
nlp.update([doc], [gold], drop=0.5, sgd=optimizer)
nlp.to_disk('/model')
+code.
from spacy.vocab import Vocab
from spacy.pipeline import DependencyParser
from spacy.tokens import Doc
+table(["Name", "Description"])
+row
+cell #[code train_data]
+cell The training data.
vocab = Vocab()
parser = DependencyParser(vocab, labels=['nsubj', 'compound', 'dobj', 'punct'])
+row
+cell #[code get_data]
+cell A function converting the training data to spaCy's JSON format.
doc = Doc(vocab, words=['Who', 'is', 'Shaka', 'Khan', '?'])
parser.update(doc, [(1, 'nsubj'), (1, 'ROOT'), (3, 'compound'), (1, 'dobj'),
(1, 'punct')])
+row
+cell #[code doc]
+cell #[+api("doc") #[code Doc]] objects.
p
+button(gh("spaCy", "examples/training/train_parser.py"), false, "secondary") Full example
+row
+cell #[code gold]
+cell #[+api("goldparse") #[code GoldParse]] objects.
+row
+cell #[code drop]
+cell Dropout rate. Makes it harder for the model to just memorise the data.
+row
+cell #[code optimizer]
+cell Callable to update the model's weights.
+infobox
| For the #[strong full example and more details], see the usage guide on
| #[+a("/docs/usage/training-ner") training the named entity recognizer],
| or the runnable
| #[+src(gh("spaCy", "examples/training/train_ner.py")) training script]
| on GitHub.
+h(2) Examples
+under-construction

10
website/docs/usage/v2.jade
View File

@ -170,7 +170,7 @@ p
python -m spacy download de # default German model
python -m spacy download fr # default French model
python -m spacy download es # default Spanish model
python -m spacy download xx_ent_web_md # multi-language NER
python -m spacy download xx_ent_wiki_sm # multi-language NER
p
| spaCy v2.0 comes with new and improved neural network models for English,
@ -294,9 +294,6 @@ p
+h(2, "migrating") Migrating from spaCy 1.x
p
| If you've mostly been using spaCy for basic text processing, chances are
| you won't even have to change your code at all. For all other cases,
| we've tried to focus...
+infobox("Some tips")
| Before migrating, we strongly recommend writing a few
@ -339,6 +336,11 @@ p
nlp.save_to_directory('/model')
nlp.vocab.dump('/vocab')
p
| If you've trained models with input from v1.x, you'll need to
| #[strong retrain them] with spaCy v2.0. All previous models will not
| be compatible with the new version.
+h(3, "migrating-strings") Strings and hash values
p