spaCy/website/docs/usage/training-ner.jade

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