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115 lines
4.6 KiB
Plaintext
115 lines
4.6 KiB
Plaintext
include ../../_includes/_mixins
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p
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| All #[+a("/docs/usage/models") spaCy models] support online learning, so
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| you can update a pre-trained model with new examples. You can even add
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| new classes to an existing model, to recognise a new entity type,
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| part-of-speech, or syntactic relation. Updating an existing model is
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| particularly useful as a "quick and dirty solution", if you have only a
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| few corrections or annotations.
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+h(2, "improving-accuracy") Improving accuracy on existing entity types
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p
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| To update the model, you first need to create an instance of
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| #[+api("goldparse") #[code GoldParse]], with the entity labels
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| you want to learn. You'll usually need to provide many examples to
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| meaningfully improve the system — a few hundred is a good start, although
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| more is better.
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+image
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include ../../assets/img/docs/training-loop.svg
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.u-text-right
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+button("/assets/img/docs/training-loop.svg", false, "secondary").u-text-tag View large graphic
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p
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| You should avoid iterating over the same few examples multiple times, or
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| the model is likely to "forget" how to annotate other examples. If you
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| iterate over the same few examples, you're effectively changing the loss
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| function. The optimizer will find a way to minimize the loss on your
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| examples, without regard for the consequences on the examples it's no
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| longer paying attention to.
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p
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| One way to avoid this "catastrophic forgetting" problem is to "remind"
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| the model of other examples by augmenting your annotations with sentences
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| annotated with entities automatically recognised by the original model.
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| Ultimately, this is an empirical process: you'll need to
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| #[strong experiment on your own data] to find a solution that works best
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| for you.
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+h(2, "example") Example
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+under-construction
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+code.
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import random
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from spacy.lang.en import English
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from spacy.gold import GoldParse, biluo_tags_from_offsets
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def main(model_dir=None):
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train_data = [
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('Who is Shaka Khan?',
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[(len('Who is '), len('Who is Shaka Khan'), 'PERSON')]),
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('I like London and Berlin.',
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[(len('I like '), len('I like London'), 'LOC'),
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(len('I like London and '), len('I like London and Berlin'), 'LOC')])
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]
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nlp = English(pipeline=['tensorizer', 'ner'])
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get_data = lambda: reformat_train_data(nlp.tokenizer, train_data)
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optimizer = nlp.begin_training(get_data)
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for itn in range(100):
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random.shuffle(train_data)
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losses = {}
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for raw_text, entity_offsets in train_data:
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doc = nlp.make_doc(raw_text)
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gold = GoldParse(doc, entities=entity_offsets)
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nlp.update([doc], [gold], drop=0.5, sgd=optimizer, losses=losses)
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nlp.to_disk(model_dir)
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+code.
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def reformat_train_data(tokenizer, examples):
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"""Reformat data to match JSON format"""
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output = []
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for i, (text, entity_offsets) in enumerate(examples):
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doc = tokenizer(text)
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ner_tags = biluo_tags_from_offsets(tokenizer(text), entity_offsets)
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words = [w.text for w in doc]
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tags = ['-'] * len(doc)
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heads = [0] * len(doc)
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deps = [''] * len(doc)
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sentence = (range(len(doc)), words, tags, heads, deps, ner_tags)
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output.append((text, [(sentence, [])]))
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return output
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p.u-text-right
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+button(gh("spaCy", "examples/training/train_ner.py"), false, "secondary").u-text-tag View full example
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+h(2, "saving-loading") Saving and loading
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p
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| After training our model, you'll usually want to save its state, and load
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| it back later. You can do this with the
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| #[+api("language#to_disk") #[code Language.to_disk()]] method:
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+code.
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nlp.to_disk('/home/me/data/en_technology')
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p
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| To make the model more convenient to deploy, we recommend wrapping it as
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| a Python package, so that you can install it via pip and load it as a
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| module. spaCy comes with a handy #[+api("cli#package") #[code package]]
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| CLI command to create all required files and directories.
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+code(false, "bash").
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python -m spacy package /home/me/data/en_technology /home/me/my_models
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p
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| To build the package and create a #[code .tar.gz] archive, run
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| #[code python setup.py sdist] from within its directory.
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+infobox("Saving and loading models")
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| For more information and a detailed guide on how to package your model,
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| see the documentation on
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| #[+a("/docs/usage/saving-loading#models") saving and loading models].
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