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Update training guide

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ines 2017-06-01 11:53:23 +02:00
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website/docs/usage/training.jade
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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