mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-26 18:06:29 +03:00
236 lines
7.5 KiB
Python
236 lines
7.5 KiB
Python
'''Example of training a named entity recognition system from scratch using spaCy
|
|
|
|
This example is written to be self-contained and reasonably transparent.
|
|
To achieve that, it duplicates some of spaCy's internal functionality.
|
|
|
|
Specifically, in this example, we don't use spaCy's built-in Language class to
|
|
wire together the Vocab, Tokenizer and EntityRecognizer. Instead, we write
|
|
our own simle Pipeline class, so that it's easier to see how the pieces
|
|
interact.
|
|
|
|
Input data:
|
|
https://www.lt.informatik.tu-darmstadt.de/fileadmin/user_upload/Group_LangTech/data/GermEval2014_complete_data.zip
|
|
|
|
Developed for: spaCy 1.7.1
|
|
Last tested for: spaCy 1.7.1
|
|
'''
|
|
from __future__ import unicode_literals, print_function
|
|
import plac
|
|
from pathlib import Path
|
|
import random
|
|
import json
|
|
|
|
import spacy.orth as orth_funcs
|
|
from spacy.vocab import Vocab
|
|
from spacy.pipeline import BeamEntityRecognizer
|
|
from spacy.pipeline import EntityRecognizer
|
|
from spacy.tokenizer import Tokenizer
|
|
from spacy.tokens import Doc
|
|
from spacy.attrs import *
|
|
from spacy.gold import GoldParse
|
|
from spacy.gold import _iob_to_biluo as iob_to_biluo
|
|
from spacy.scorer import Scorer
|
|
|
|
try:
|
|
unicode
|
|
except NameError:
|
|
unicode = str
|
|
|
|
|
|
def init_vocab():
|
|
return Vocab(
|
|
lex_attr_getters={
|
|
LOWER: lambda string: string.lower(),
|
|
SHAPE: orth_funcs.word_shape,
|
|
PREFIX: lambda string: string[0],
|
|
SUFFIX: lambda string: string[-3:],
|
|
CLUSTER: lambda string: 0,
|
|
IS_ALPHA: orth_funcs.is_alpha,
|
|
IS_ASCII: orth_funcs.is_ascii,
|
|
IS_DIGIT: lambda string: string.isdigit(),
|
|
IS_LOWER: orth_funcs.is_lower,
|
|
IS_PUNCT: orth_funcs.is_punct,
|
|
IS_SPACE: lambda string: string.isspace(),
|
|
IS_TITLE: orth_funcs.is_title,
|
|
IS_UPPER: orth_funcs.is_upper,
|
|
IS_STOP: lambda string: False,
|
|
IS_OOV: lambda string: True
|
|
})
|
|
|
|
|
|
def save_vocab(vocab, path):
|
|
path = Path(path)
|
|
if not path.exists():
|
|
path.mkdir()
|
|
elif not path.is_dir():
|
|
raise IOError("Can't save vocab to %s\nNot a directory" % path)
|
|
with (path / 'strings.json').open('w') as file_:
|
|
vocab.strings.dump(file_)
|
|
vocab.dump((path / 'lexemes.bin').as_posix())
|
|
|
|
|
|
def load_vocab(path):
|
|
path = Path(path)
|
|
if not path.exists():
|
|
raise IOError("Cannot load vocab from %s\nDoes not exist" % path)
|
|
if not path.is_dir():
|
|
raise IOError("Cannot load vocab from %s\nNot a directory" % path)
|
|
return Vocab.load(path)
|
|
|
|
|
|
def init_ner_model(vocab, features=None):
|
|
if features is None:
|
|
features = tuple(EntityRecognizer.feature_templates)
|
|
return BeamEntityRecognizer(vocab, features=features)
|
|
|
|
|
|
def save_ner_model(model, path):
|
|
path = Path(path)
|
|
if not path.exists():
|
|
path.mkdir()
|
|
if not path.is_dir():
|
|
raise IOError("Can't save model to %s\nNot a directory" % path)
|
|
model.model.dump((path / 'model').as_posix())
|
|
with (path / 'config.json').open('w') as file_:
|
|
data = json.dumps(model.cfg)
|
|
if not isinstance(data, unicode):
|
|
data = data.decode('utf8')
|
|
file_.write(data)
|
|
|
|
|
|
def load_ner_model(vocab, path):
|
|
return BeamEntityRecognizer.load(path, vocab)
|
|
|
|
|
|
class Pipeline(object):
|
|
@classmethod
|
|
def load(cls, path):
|
|
path = Path(path)
|
|
if not path.exists():
|
|
raise IOError("Cannot load pipeline from %s\nDoes not exist" % path)
|
|
if not path.is_dir():
|
|
raise IOError("Cannot load pipeline from %s\nNot a directory" % path)
|
|
vocab = load_vocab(path / 'vocab')
|
|
tokenizer = Tokenizer(vocab, {}, None, None, None)
|
|
ner_model = load_ner_model(vocab, path / 'ner')
|
|
return cls(vocab, tokenizer, ner_model)
|
|
|
|
def __init__(self, vocab=None, tokenizer=None, ner_model=None):
|
|
if vocab is None:
|
|
self.vocab = init_vocab()
|
|
if tokenizer is None:
|
|
tokenizer = Tokenizer(vocab, {}, None, None, None)
|
|
if ner_model is None:
|
|
self.entity = init_ner_model(self.vocab)
|
|
self.pipeline = [self.entity]
|
|
|
|
def __call__(self, input_):
|
|
doc = self.make_doc(input_)
|
|
for process in self.pipeline:
|
|
process(doc)
|
|
return doc
|
|
|
|
def make_doc(self, input_):
|
|
if isinstance(input_, bytes):
|
|
input_ = input_.decode('utf8')
|
|
if isinstance(input_, unicode):
|
|
return self.tokenizer(input_)
|
|
else:
|
|
return Doc(self.vocab, words=input_)
|
|
|
|
def make_gold(self, input_, annotations):
|
|
doc = self.make_doc(input_)
|
|
gold = GoldParse(doc, entities=annotations)
|
|
return gold
|
|
|
|
def update(self, input_, annot):
|
|
doc = self.make_doc(input_)
|
|
gold = self.make_gold(input_, annot)
|
|
for ner in gold.ner:
|
|
if ner not in (None, '-', 'O'):
|
|
action, label = ner.split('-', 1)
|
|
self.entity.add_label(label)
|
|
return self.entity.update(doc, gold)
|
|
|
|
def evaluate(self, examples):
|
|
scorer = Scorer()
|
|
for input_, annot in examples:
|
|
gold = self.make_gold(input_, annot)
|
|
doc = self(input_)
|
|
scorer.score(doc, gold)
|
|
return scorer.scores
|
|
|
|
def average_weights(self):
|
|
self.entity.model.end_training()
|
|
|
|
def save(self, path):
|
|
path = Path(path)
|
|
if not path.exists():
|
|
path.mkdir()
|
|
elif not path.is_dir():
|
|
raise IOError("Can't save pipeline to %s\nNot a directory" % path)
|
|
save_vocab(self.vocab, path / 'vocab')
|
|
save_ner_model(self.entity, path / 'ner')
|
|
|
|
|
|
def train(nlp, train_examples, dev_examples, nr_epoch=5):
|
|
next_epoch = train_examples
|
|
print("Iter", "Loss", "P", "R", "F")
|
|
for i in range(nr_epoch):
|
|
this_epoch = next_epoch
|
|
next_epoch = []
|
|
loss = 0
|
|
for input_, annot in this_epoch:
|
|
loss += nlp.update(input_, annot)
|
|
if (i+1) < nr_epoch:
|
|
next_epoch.append((input_, annot))
|
|
random.shuffle(next_epoch)
|
|
scores = nlp.evaluate(dev_examples)
|
|
precision = '%.2f' % scores['ents_p']
|
|
recall = '%.2f' % scores['ents_r']
|
|
f_measure = '%.2f' % scores['ents_f']
|
|
print(i, int(loss), precision, recall, f_measure)
|
|
nlp.average_weights()
|
|
scores = nlp.evaluate(dev_examples)
|
|
print("After averaging")
|
|
print(scores['ents_p'], scores['ents_r'], scores['ents_f'])
|
|
|
|
|
|
def read_examples(path):
|
|
path = Path(path)
|
|
with path.open() as file_:
|
|
sents = file_.read().strip().split('\n\n')
|
|
for sent in sents:
|
|
if not sent.strip():
|
|
continue
|
|
tokens = sent.split('\n')
|
|
while tokens and tokens[0].startswith('#'):
|
|
tokens.pop(0)
|
|
words = []
|
|
iob = []
|
|
for token in tokens:
|
|
if token.strip():
|
|
pieces = token.split()
|
|
words.append(pieces[1])
|
|
iob.append(pieces[2])
|
|
yield words, iob_to_biluo(iob)
|
|
|
|
|
|
@plac.annotations(
|
|
model_dir=("Path to save the model", "positional", None, Path),
|
|
train_loc=("Path to your training data", "positional", None, Path),
|
|
dev_loc=("Path to your development data", "positional", None, Path),
|
|
)
|
|
def main(model_dir, train_loc, dev_loc, nr_epoch=10):
|
|
train_examples = read_examples(train_loc)
|
|
dev_examples = read_examples(dev_loc)
|
|
nlp = Pipeline()
|
|
|
|
train(nlp, train_examples, list(dev_examples), nr_epoch)
|
|
|
|
nlp.save(model_dir)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
plac.call(main)
|