mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-27 18:36:36 +03:00
115 lines
4.6 KiB
Plaintext
115 lines
4.6 KiB
Plaintext
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
|
|
|
|
p
|
|
| To update the model, you first need to create an instance of
|
|
| #[+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
|
|
|
|
p
|
|
| 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
|
|
| 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.
|
|
|
|
+h(2, "example") Example
|
|
|
|
+under-construction
|
|
|
|
+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
|
|
|
|
p
|
|
| 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')
|
|
|
|
p
|
|
| 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").
|
|
python -m spacy package /home/me/data/en_technology /home/me/my_models
|
|
|
|
p
|
|
| 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].
|