def train(Language,
gold_tuples,
model_dir,
n_iter=15,
feat_set=u'basic',
seed=0,
gold_preproc=False,
force_gold=False):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(pos_model_dir)
Config.write(dep_model_dir,
'config',
features=feat_set,
seed=seed,
labels=ArcEager.get_labels(gold_tuples))
nlp = Language(data_dir=model_dir,
tagger=False,
parser=False,
entity=False)
nlp.tagger = Tagger.blank(nlp.vocab, Tagger.default_templates())
nlp.parser = Parser.from_dir(dep_model_dir, nlp.vocab.strings, ArcEager)
print("Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %")
for itn in range(n_iter):
scorer = Scorer()
loss = 0
for _, sents in gold_tuples:
for annot_tuples, _ in sents:
if len(annot_tuples[1]) == 1:
continue
score_model(scorer, nlp, None, annot_tuples, verbose=False)
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
nlp.tagger(tokens)
gold = GoldParse(tokens, annot_tuples, make_projective=True)
if not gold.is_projective:
raise Exception(
"Non-projective sentence in training, after we should "
"have enforced projectivity: %s" % annot_tuples)
loss += nlp.parser.train(tokens, gold)
nlp.tagger.train(tokens, gold.tags)
random.shuffle(gold_tuples)
print('%d:\t%d\t%.3f\t%.3f\t%.3f' %
(itn, loss, scorer.uas, scorer.tags_acc, scorer.token_acc))
print('end training')
nlp.end_training(model_dir)
print('done')
def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic', seed=0,
gold_preproc=False, force_gold=False):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(pos_model_dir)
Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
labels=ArcEager.get_labels(gold_tuples))
nlp = Language(data_dir=model_dir, tagger=False, parser=False, entity=False)
nlp.tagger = Tagger.blank(nlp.vocab, Tagger.default_templates())
nlp.parser = Parser.from_dir(dep_model_dir, nlp.vocab.strings, ArcEager)
print("Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %")
for itn in range(n_iter):
scorer = Scorer()
loss = 0
for _, sents in gold_tuples:
for annot_tuples, _ in sents:
if len(annot_tuples[1]) == 1:
continue
score_model(scorer, nlp, None, annot_tuples, verbose=False)
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
nlp.tagger(tokens)
gold = GoldParse(tokens, annot_tuples, make_projective=True)
if not gold.is_projective:
raise Exception(
"Non-projective sentence in training, after we should "
"have enforced projectivity: %s" % annot_tuples
)
loss += nlp.parser.train(tokens, gold)
nlp.tagger.train(tokens, gold.tags)
random.shuffle(gold_tuples)
print('%d:\t%d\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas,
scorer.tags_acc, scorer.token_acc))
print('end training')
nlp.end_training(model_dir)
print('done')
def main(train_loc, dev_loc, model_dir, tag_map_loc):
with open(tag_map_loc) as file_:
tag_map = json.loads(file_.read())
train_sents = list(read_conllx(train_loc))
labels = ArcEager.get_labels(train_sents)
templates = get_templates('basic')
TreebankParser.setup_model_dir(model_dir, labels, templates)
nlp = TreebankParser.from_dir(tag_map, model_dir)
for itn in range(15):
for _, doc_sents in train_sents:
for (ids, words, tags, heads, deps, ner), _ in doc_sents:
nlp.train(words, tags, heads, deps)
random.shuffle(train_sents)
scorer = score_model(nlp, read_conllx(dev_loc))
print('%d:\t%.3f\t%.3f' % (itn, scorer.uas, scorer.tags_acc))
nlp.end_training(model_dir)
scorer = score_model(nlp, read_conllx(dev_loc))
print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))
def main(train_loc, dev_loc, model_dir, tag_map_loc):
with open(tag_map_loc) as file_:
tag_map = json.loads(file_.read())
train_sents = list(read_conllx(train_loc))
labels = ArcEager.get_labels(train_sents)
templates = get_templates('basic')
TreebankParser.setup_model_dir(model_dir, labels, templates)
nlp = TreebankParser.from_dir(tag_map, model_dir)
for itn in range(15):
for _, doc_sents in train_sents:
for (ids, words, tags, heads, deps, ner), _ in doc_sents:
nlp.train(words, tags, heads, deps)
random.shuffle(train_sents)
scorer = score_model(nlp, read_conllx(dev_loc))
print('%d:\t%.3f\t%.3f' % (itn, scorer.uas, scorer.tags_acc))
nlp.end_training(model_dir)
scorer = score_model(nlp, read_conllx(dev_loc))
print('%d:\t%.3f\t%.3f\t%.3f' %
(itn, scorer.uas, scorer.las, scorer.tags_acc))
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):
dep_model_dir = path.join(model_dir, 'deps')
ner_model_dir = path.join(model_dir, 'ner')
pos_model_dir = path.join(model_dir, 'pos')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(ner_model_dir):
shutil.rmtree(ner_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(ner_model_dir)
os.mkdir(pos_model_dir)
Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
labels=ArcEager.get_labels(gold_tuples),
beam_width=beam_width)
Config.write(ner_model_dir, 'config', features='ner', seed=seed,
labels=BiluoPushDown.get_labels(gold_tuples),
beam_width=0)
if n_sents > 0:
gold_tuples = gold_tuples[:n_sents]
nlp = Language(data_dir=model_dir, tagger=False, parser=False, entity=False)
nlp.tagger = Tagger.blank(nlp.vocab, Tagger.default_templates())
nlp.parser = Parser.from_dir(dep_model_dir, nlp.vocab.strings, ArcEager)
nlp.entity = Parser.from_dir(ner_model_dir, nlp.vocab.strings, BiluoPushDown)
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:
if len(annot_tuples[1]) == 1:
continue
score_model(scorer, nlp, raw_text, annot_tuples,
verbose=verbose if itn >= 2 else False)
if raw_text is None:
words = add_noise(annot_tuples[1], corruption_level)
tokens = nlp.tokenizer.tokens_from_list(words)
else:
raw_text = add_noise(raw_text, corruption_level)
tokens = nlp.tokenizer(raw_text)
nlp.tagger(tokens)
gold = GoldParse(tokens, annot_tuples, make_projective=True)
if not gold.is_projective:
raise Exception(
"Non-projective sentence in training, after we should "
"have enforced projectivity: %s" % annot_tuples
)
loss += nlp.parser.train(tokens, gold)
nlp.entity.train(tokens, gold)
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))
print('end training')
nlp.end_training(model_dir)
print('done')
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, pseudoprojective=False):
dep_model_dir = path.join(model_dir, 'deps')
ner_model_dir = path.join(model_dir, 'ner')
pos_model_dir = path.join(model_dir, 'pos')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(ner_model_dir):
shutil.rmtree(ner_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(ner_model_dir)
os.mkdir(pos_model_dir)
if pseudoprojective:
# preprocess training data here before ArcEager.get_labels() is called
gold_tuples = PseudoProjectivity.preprocess_training_data(gold_tuples)
Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
labels=ArcEager.get_labels(gold_tuples),
beam_width=beam_width,projectivize=pseudoprojective)
Config.write(ner_model_dir, 'config', features='ner', seed=seed,
labels=BiluoPushDown.get_labels(gold_tuples),
beam_width=0)
if n_sents > 0:
gold_tuples = gold_tuples[:n_sents]
nlp = Language(data_dir=model_dir, tagger=False, parser=False, entity=False)
if nlp.lang == 'de':
nlp.vocab.morphology.lemmatizer = lambda string,pos: set([string])
nlp.tagger = Tagger.blank(nlp.vocab, Tagger.default_templates())
nlp.parser = Parser.from_dir(dep_model_dir, nlp.vocab.strings, ArcEager)
nlp.entity = Parser.from_dir(ner_model_dir, nlp.vocab.strings, BiluoPushDown)
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:
if len(annot_tuples[1]) == 1:
continue
score_model(scorer, nlp, raw_text, annot_tuples,
verbose=verbose if itn >= 2 else False)
if raw_text is None:
words = add_noise(annot_tuples[1], corruption_level)
tokens = nlp.tokenizer.tokens_from_list(words)
else:
raw_text = add_noise(raw_text, corruption_level)
tokens = nlp.tokenizer(raw_text)
nlp.tagger(tokens)
gold = GoldParse(tokens, annot_tuples)
if not gold.is_projective:
raise Exception("Non-projective sentence in training: %s" % annot_tuples[1])
loss += nlp.parser.train(tokens, gold)
nlp.entity.train(tokens, gold)
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))
print('end training')
nlp.end_training(model_dir)
print('done')
