mirror of https://github.com/explosion/spaCy.git
* Add tagger training script
This commit is contained in:
parent
c07eea8563
commit
320ced276a
|
@ -0,0 +1,175 @@
|
|||
#!/usr/bin/env python
|
||||
from __future__ import division
|
||||
from __future__ import unicode_literals
|
||||
from __future__ import print_function
|
||||
|
||||
import os
|
||||
from os import path
|
||||
import shutil
|
||||
import codecs
|
||||
import random
|
||||
|
||||
import plac
|
||||
import re
|
||||
|
||||
import spacy.util
|
||||
from spacy.en import English
|
||||
|
||||
from spacy.tagger import Tagger
|
||||
|
||||
from spacy.syntax.util import Config
|
||||
from spacy.gold import read_json_file
|
||||
from spacy.gold import GoldParse
|
||||
|
||||
from spacy.scorer import Scorer
|
||||
|
||||
|
||||
def score_model(scorer, nlp, raw_text, annot_tuples):
|
||||
if raw_text is None:
|
||||
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
|
||||
else:
|
||||
tokens = nlp.tokenizer(raw_text)
|
||||
nlp.tagger(tokens)
|
||||
gold = GoldParse(tokens, annot_tuples)
|
||||
scorer.score(tokens, gold)
|
||||
|
||||
|
||||
def _merge_sents(sents):
|
||||
m_deps = [[], [], [], [], [], []]
|
||||
m_brackets = []
|
||||
i = 0
|
||||
for (ids, words, tags, heads, labels, ner), brackets in sents:
|
||||
m_deps[0].extend(id_ + i for id_ in ids)
|
||||
m_deps[1].extend(words)
|
||||
m_deps[2].extend(tags)
|
||||
m_deps[3].extend(head + i for head in heads)
|
||||
m_deps[4].extend(labels)
|
||||
m_deps[5].extend(ner)
|
||||
m_brackets.extend((b['first'] + i, b['last'] + i, b['label']) for b in brackets)
|
||||
i += len(ids)
|
||||
return [(m_deps, m_brackets)]
|
||||
|
||||
|
||||
def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
|
||||
seed=0, gold_preproc=False, n_sents=0, corruption_level=0,
|
||||
beam_width=1, verbose=False,
|
||||
use_orig_arc_eager=False):
|
||||
if n_sents > 0:
|
||||
gold_tuples = gold_tuples[:n_sents]
|
||||
|
||||
templates = Tagger.default_templates()
|
||||
nlp = Language(data_dir=model_dir, tagger=False)
|
||||
nlp.tagger = Tagger.blank(nlp.vocab, templates)
|
||||
|
||||
print("Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %")
|
||||
for itn in range(n_iter):
|
||||
scorer = Scorer()
|
||||
loss = 0
|
||||
for raw_text, sents in gold_tuples:
|
||||
if gold_preproc:
|
||||
raw_text = None
|
||||
else:
|
||||
sents = _merge_sents(sents)
|
||||
for annot_tuples, ctnt in sents:
|
||||
words = annot_tuples[1]
|
||||
gold_tags = annot_tuples[2]
|
||||
score_model(scorer, nlp, raw_text, annot_tuples)
|
||||
if raw_text is None:
|
||||
tokens = nlp.tokenizer.tokens_from_list(words)
|
||||
else:
|
||||
tokens = nlp.tokenizer(raw_text)
|
||||
loss += nlp.tagger.train(tokens, gold_tags)
|
||||
random.shuffle(gold_tuples)
|
||||
print('%d:\t%d\t%.3f\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.ents_f,
|
||||
scorer.tags_acc,
|
||||
scorer.token_acc))
|
||||
nlp.end_training(model_dir)
|
||||
|
||||
def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False,
|
||||
beam_width=None):
|
||||
nlp = Language(data_dir=model_dir)
|
||||
if beam_width is not None:
|
||||
nlp.parser.cfg.beam_width = beam_width
|
||||
scorer = Scorer()
|
||||
for raw_text, sents in gold_tuples:
|
||||
if gold_preproc:
|
||||
raw_text = None
|
||||
else:
|
||||
sents = _merge_sents(sents)
|
||||
for annot_tuples, brackets in sents:
|
||||
if raw_text is None:
|
||||
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
|
||||
nlp.tagger(tokens)
|
||||
nlp.entity(tokens)
|
||||
nlp.parser(tokens)
|
||||
else:
|
||||
tokens = nlp(raw_text, merge_mwes=False)
|
||||
gold = GoldParse(tokens, annot_tuples)
|
||||
scorer.score(tokens, gold, verbose=verbose)
|
||||
return scorer
|
||||
|
||||
|
||||
def write_parses(Language, dev_loc, model_dir, out_loc, beam_width=None):
|
||||
nlp = Language(data_dir=model_dir)
|
||||
if beam_width is not None:
|
||||
nlp.parser.cfg.beam_width = beam_width
|
||||
gold_tuples = read_json_file(dev_loc)
|
||||
scorer = Scorer()
|
||||
out_file = codecs.open(out_loc, 'w', 'utf8')
|
||||
for raw_text, sents in gold_tuples:
|
||||
sents = _merge_sents(sents)
|
||||
for annot_tuples, brackets in sents:
|
||||
if raw_text is None:
|
||||
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
|
||||
nlp.tagger(tokens)
|
||||
nlp.entity(tokens)
|
||||
nlp.parser(tokens)
|
||||
else:
|
||||
tokens = nlp(raw_text, merge_mwes=False)
|
||||
gold = GoldParse(tokens, annot_tuples)
|
||||
scorer.score(tokens, gold, verbose=False)
|
||||
for t in tokens:
|
||||
out_file.write(
|
||||
'%s\t%s\t%s\t%s\n' % (t.orth_, t.tag_, t.head.orth_, t.dep_)
|
||||
)
|
||||
return scorer
|
||||
|
||||
|
||||
@plac.annotations(
|
||||
train_loc=("Location of training file or directory"),
|
||||
dev_loc=("Location of development file or directory"),
|
||||
model_dir=("Location of output model directory",),
|
||||
eval_only=("Skip training, and only evaluate", "flag", "e", bool),
|
||||
corruption_level=("Amount of noise to add to training data", "option", "c", float),
|
||||
gold_preproc=("Use gold-standard sentence boundaries in training?", "flag", "g", bool),
|
||||
out_loc=("Out location", "option", "o", str),
|
||||
n_sents=("Number of training sentences", "option", "n", int),
|
||||
n_iter=("Number of training iterations", "option", "i", int),
|
||||
verbose=("Verbose error reporting", "flag", "v", bool),
|
||||
debug=("Debug mode", "flag", "d", bool),
|
||||
)
|
||||
def main(train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc="", verbose=False,
|
||||
debug=False, corruption_level=0.0, gold_preproc=False, eval_only=False):
|
||||
if not eval_only:
|
||||
gold_train = list(read_json_file(train_loc))
|
||||
train(English, gold_train, model_dir,
|
||||
feat_set='basic' if not debug else 'debug',
|
||||
gold_preproc=gold_preproc, n_sents=n_sents,
|
||||
corruption_level=corruption_level, n_iter=n_iter,
|
||||
verbose=verbose)
|
||||
#if out_loc:
|
||||
# write_parses(English, dev_loc, model_dir, out_loc, beam_width=beam_width)
|
||||
scorer = evaluate(English, list(read_json_file(dev_loc)),
|
||||
model_dir, gold_preproc=gold_preproc, verbose=verbose)
|
||||
print('TOK', scorer.token_acc)
|
||||
print('POS', scorer.tags_acc)
|
||||
print('UAS', scorer.uas)
|
||||
print('LAS', scorer.las)
|
||||
|
||||
print('NER P', scorer.ents_p)
|
||||
print('NER R', scorer.ents_r)
|
||||
print('NER F', scorer.ents_f)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
plac.call(main)
|
Loading…
Reference in New Issue