def train_word_vectors(vectors_loc, lang='zh', model_name='zh_model'):
"""
加载词向量数据 从零开始训练 spacy 模型
:param vectors_loc:
:param lang:
:param model_name:
:return:
"""
if lang is None:
nlp = Language()
else:
# create an empty language class
nlp = spacy.blank(lang)
with open(vectors_loc, 'rb') as file_:
header = file_.readline()
nr_row, nr_dim = header.split()
print(nr_row, nr_dim)
nlp.vocab.reset_vectors(width=int(nr_dim))
count = 0
for line in file_:
line = line.rstrip().decode('utf8')
pieces = line.rsplit(' ', int(nr_dim))
word = pieces[0]
vector = numpy.asarray([float(v) for v in pieces[1:]], dtype='f')
# add the vectors to the vocab
count += 1
print(f'{word} added {count / int(nr_row) * 100} % ')
nlp.vocab.set_vector(word, vector)
nlp.to_disk("data/" + model_name)
print('finishing!!!')
def load_tospacy(self, lang='en'):
"""
loads glove vectors from file specified in intialization, set vectors and save to disk
:param lang:
:return:
"""
if lang is None:
# create blank multilanguage class with 'xx' if lang is None
nlp = Language('xx')
else:
nlp = spacy.blank(lang)
custom_log('PARSING GLOVE MODEL')
with open(self.glovedir, 'r', encoding="utf8") as glove_file:
model = {}
for line in glove_file:
split_line = line.split()
word = split_line[0]
embedding = np.array([float(val) for val in split_line[1:]])
nlp.vocab.set_vector(word, embedding)
custom_log('VECTORS SET, SAVING TO DISK')
nlp.to_disk(r'glove6B/glove-6B')
def main(vectors_loc, lang=None):
if lang is None:
nlp = Language()
else:
# create empty language class – this is required if you're planning to
# save the model to disk and load it back later (models always need a
# "lang" setting). Use 'xx' for blank multi-language class.
nlp = spacy.blank(lang)
with open(vectors_loc, 'rb') as file_:
header = file_.readline()
nr_row, nr_dim = header.split()
print(nr_row, nr_dim)
nlp.vocab.reset_vectors(width=int(nr_dim))
for line in file_:
line = line.rstrip().decode('utf8')
pieces = line.rsplit(' ', int(nr_dim))
word = pieces[0]
vector = numpy.asarray([float(v) for v in pieces[1:]], dtype='f')
nlp.vocab.set_vector(word, vector) # add the vectors to the vocab
print(word)
# test the vectors and similarity
# text = '您好'
# doc = nlp(text)
# print(text, doc[0].similarity(doc[1]))
nlp.to_disk("./zh_model")
def test_package(nlp: Language, chdir):
d = Path().cwd()
modeld = d / "model"
pkgd = d / "package"
pkgd.mkdir()
nlp.to_disk(modeld)
package(modeld, pkgd)
def test_serialize_config_language_specific():
"""Test that config serialization works as expected with language-specific
factories."""
name = "test_serialize_config_language_specific"
@English.factory(name, default_config={"foo": 20})
def custom_factory(nlp: Language, name: str, foo: int):
return lambda doc: doc
nlp = Language()
assert not nlp.has_factory(name)
nlp = English()
assert nlp.has_factory(name)
nlp.add_pipe(name, config={"foo": 100}, name="bar")
pipe_config = nlp.config["components"]["bar"]
assert pipe_config["foo"] == 100
assert pipe_config["factory"] == name
with make_tempdir() as d:
nlp.to_disk(d)
nlp2 = spacy.load(d)
assert nlp2.has_factory(name)
assert nlp2.pipe_names == ["bar"]
assert nlp2.get_pipe_meta("bar").factory == name
pipe_config = nlp2.config["components"]["bar"]
assert pipe_config["foo"] == 100
assert pipe_config["factory"] == name
config = Config().from_str(nlp2.config.to_str())
config["nlp"]["lang"] = "de"
with pytest.raises(ValueError):
# German doesn't have a factory, only English does
load_model_from_config(config)
def test_issue999(train_data):
"""Test that adding entities and resuming training works passably OK.
There are two issues here:
1) We have to readd labels. This isn't very nice.
2) There's no way to set the learning rate for the weight update, so we
end up out-of-scale, causing it to learn too fast.
"""
TRAIN_DATA = [
["hey", []],
["howdy", []],
["hey there", []],
["hello", []],
["hi", []],
["i'm looking for a place to eat", []],
[
"i'm looking for a place in the north of town",
[[31, 36, "LOCATION"]]
],
["show me chinese restaurants", [[8, 15, "CUISINE"]]],
["show me chines restaurants", [[8, 14, "CUISINE"]]],
]
nlp = Language()
ner = nlp.create_pipe("ner")
nlp.add_pipe(ner)
for _, offsets in TRAIN_DATA:
for start, end, label in offsets:
ner.add_label(label)
nlp.begin_training()
ner.model.learn_rate = 0.001
for itn in range(100):
random.shuffle(TRAIN_DATA)
for raw_text, entity_offsets in TRAIN_DATA:
nlp.update([raw_text], [{"entities": entity_offsets}])
with make_tempdir() as model_dir:
nlp.to_disk(model_dir)
nlp2 = Language().from_disk(model_dir)
for raw_text, entity_offsets in TRAIN_DATA:
doc = nlp2(raw_text)
ents = {(ent.start_char, ent.end_char): ent.label_ for ent in doc.ents}
for start, end, label in entity_offsets:
if (start, end) in ents:
assert ents[(start, end)] == label
break
else:
if entity_offsets:
raise Exception(ents)
def test_entity_ruler_in_pipeline_from_issue(patterns, en_vocab):
nlp = Language(vocab=en_vocab)
ruler = EntityRuler(nlp, overwrite_ents=True)
ruler.add_patterns([{"label": "ORG", "pattern": "Apple"}])
nlp.add_pipe(ruler)
with make_tempdir() as tmpdir:
nlp.to_disk(tmpdir)
ruler = nlp.get_pipe("entity_ruler")
assert ruler.patterns == [{"label": "ORG", "pattern": "Apple"}]
assert ruler.overwrite is True
nlp2 = load(tmpdir)
new_ruler = nlp2.get_pipe("entity_ruler")
assert new_ruler.patterns == [{"label": "ORG", "pattern": "Apple"}]
assert new_ruler.overwrite is True
def test_issue_3526_4(en_vocab):
nlp = Language(vocab=en_vocab)
patterns = [{"label": "ORG", "pattern": "Apple"}]
config = {"overwrite_ents": True}
ruler = nlp.add_pipe("entity_ruler", config=config)
ruler.add_patterns(patterns)
with make_tempdir() as tmpdir:
nlp.to_disk(tmpdir)
ruler = nlp.get_pipe("entity_ruler")
assert ruler.patterns == [{"label": "ORG", "pattern": "Apple"}]
assert ruler.overwrite is True
nlp2 = load(tmpdir)
new_ruler = nlp2.get_pipe("entity_ruler")
assert new_ruler.patterns == [{"label": "ORG", "pattern": "Apple"}]
assert new_ruler.overwrite is True
def test_issue999(train_data):
"""Test that adding entities and resuming training works passably OK.
There are two issues here:
1) We have to readd labels. This isn't very nice.
2) There's no way to set the learning rate for the weight update, so we
end up out-of-scale, causing it to learn too fast.
"""
TRAIN_DATA = [
["hey", []],
["howdy", []],
["hey there", []],
["hello", []],
["hi", []],
["i'm looking for a place to eat", []],
["i'm looking for a place in the north of town", [[31, 36, "LOCATION"]]],
["show me chinese restaurants", [[8, 15, "CUISINE"]]],
["show me chines restaurants", [[8, 14, "CUISINE"]]],
]
nlp = Language()
ner = nlp.create_pipe("ner")
nlp.add_pipe(ner)
for _, offsets in TRAIN_DATA:
for start, end, label in offsets:
ner.add_label(label)
nlp.begin_training()
ner.model.learn_rate = 0.001
for itn in range(100):
random.shuffle(TRAIN_DATA)
for raw_text, entity_offsets in TRAIN_DATA:
nlp.update([raw_text], [{"entities": entity_offsets}])
with make_tempdir() as model_dir:
nlp.to_disk(model_dir)
nlp2 = Language().from_disk(model_dir)
for raw_text, entity_offsets in TRAIN_DATA:
doc = nlp2(raw_text)
ents = {(ent.start_char, ent.end_char): ent.label_ for ent in doc.ents}
for start, end, label in entity_offsets:
if (start, end) in ents:
assert ents[(start, end)] == label
break
else:
if entity_offsets:
raise Exception(ents)
def main(lang=None):
if lang is None:
nlp = Language()
else:
# create empty language class – this is required if you're planning to
# save the model to disk and load it back later (models always need a
# "lang" setting). Use 'xx' for blank multi-language class.
nlp = spacy.blank(lang)
file_loc = './广电全量地址.txt'
#file_loc = '/home/siy/Downloads/guizhou/new/txt/0.txt'
nr_dim = 768
nlp.vocab.reset_vectors(width=int(nr_dim))
cnt = 0
with open(file_loc, 'r') as f:
# df = pd.read_csv(f)
lines = f.readlines()
np.random.shuffle(lines)
lines = lines[:10000]
for line in lines:
# line = line.decode()
# print(line)
line = strQ2B(line)
line.strip()
line = clr(line)
line.strip()
vecs = []
try:
print(line)
vecs = bc.encode(list(line))
except:
traceback.print_exc()
print(list(line))
continue
for char, vec in zip(line, vecs):
try:
nlp.vocab.set_vector(ord(char), bc.encode([char]))
except BaseException:
traceback.print_exc()
print(char)
continue
cnt += 1
print('bingo, i write in %s' % cnt)
nlp.to_disk('./zh_models')
def main(vectors_loc=None, lang=None):
if lang is None:
nlp = Language()
else:
# create empty language class – this is required if you're planning to
# save the model to disk and load it back later (models always need a
# "lang" setting). Use 'xx' for blank multi-language class.
nlp = spacy.blank(lang)
with open(VECTORS_PATH, "rb") as file_:
print("loading vectors...")
header = file_.readline()
nr_row, nr_dim = header.split()
nlp.vocab.reset_vectors(width=int(nr_dim))
for line in file_:
line = line.rstrip().decode("utf8")
pieces = line.rsplit(" ", int(nr_dim))
word = pieces[0]
vector = numpy.asarray([float(v) for v in pieces[1:]], dtype="f")
nlp.vocab.set_vector(word, vector) # add the vectors to the vocab
tagger = nlp.create_pipe("tagger")
# Add the tags. This needs to be done before you start training.
print("trainning tags...")
for tag, values in TAG_MAP.items():
tagger.add_label(tag, values)
nlp.add_pipe(tagger)
optimizer = nlp.begin_training()
for i in range(20):
random.shuffle(TRAIN_DATA)
losses = {}
for text, annotations in TRAIN_DATA:
nlp.update([text], [annotations], sgd=optimizer, losses=losses)
print(losses)
# test the trained model
test_text = "Eu desejo ouvir uma música muito boa"
doc = nlp(test_text)
print("Tags", [(t.text, t.tag_, t.pos_) for t in doc])
print("Saved mode to nl_model_tagger")
nlp.to_disk("/app/model")
def test_serialize_with_custom_tokenizer():
"""Test that serialization with custom tokenizer works without token_match.
See: https://support.prodi.gy/t/how-to-save-a-custom-tokenizer/661/2
"""
prefix_re = re.compile(r"""1/|2/|:[0-9][0-9][A-K]:|:[0-9][0-9]:""")
suffix_re = re.compile(r"""""")
infix_re = re.compile(r"""[~]""")
def custom_tokenizer(nlp):
return Tokenizer(
nlp.vocab,
{},
prefix_search=prefix_re.search,
suffix_search=suffix_re.search,
infix_finditer=infix_re.finditer,
)
nlp = Language()
nlp.tokenizer = custom_tokenizer(nlp)
with make_tempdir() as d:
nlp.to_disk(d)
def main(vectors_loc, lang=None, model_name='zh_model'):
if lang is None:
nlp = Language()
else:
# create an empty language class
nlp = spacy.blank(lang)
with open(vectors_loc, 'rb') as file_:
header = file_.readline()
nr_row, nr_dim = header.split()
print(nr_row, nr_dim)
nlp.vocab.reset_vectors(width=int(nr_dim))
for line in file_:
line = line.rstrip().decode('utf8')
pieces = line.rsplit(' ', int(nr_dim))
word = pieces[0]
vector = numpy.asarray([float(v) for v in pieces[1:]], dtype='f')
# add the vectors to the vocab
nlp.vocab.set_vector(word, vector)
nlp.to_disk("data/" + model_name)
print('finishing!!!')
def test_serialize_with_custom_tokenizer():
"""Test that serialization with custom tokenizer works without token_match.
See: https://support.prodi.gy/t/how-to-save-a-custom-tokenizer/661/2
"""
prefix_re = re.compile(r"""1/|2/|:[0-9][0-9][A-K]:|:[0-9][0-9]:""")
suffix_re = re.compile(r"""""")
infix_re = re.compile(r"""[~]""")
def custom_tokenizer(nlp):
return Tokenizer(
nlp.vocab,
{},
prefix_search=prefix_re.search,
suffix_search=suffix_re.search,
infix_finditer=infix_re.finditer,
)
nlp = Language()
nlp.tokenizer = custom_tokenizer(nlp)
with make_tempdir() as d:
nlp.to_disk(d)
def recreateWordVectors(vectors_loc="wordfeats/glove.6B/glove.6B.50d.txt",
save_loc="wordfeats"):
lang = "en"
if lang is None:
nlp = Language()
else:
nlp = spacy.blank(lang)
with open(vectors_loc, 'rb') as file_:
header = file_.readline()
# nr_row, nr_dim = header.split()
nr_dim = 50
nlp.vocab.reset_vectors(width=int(nr_dim))
for line in file_:
line = line.rstrip().decode('utf8')
pieces = line.rsplit(' ', int(nr_dim))
word = pieces[0]
vector = np.asarray([float(v) for v in pieces[1:]], dtype='f')
nlp.vocab.set_vector(word, vector) # add the vectors to the vocab
nlp.to_disk(save_loc)
return
def main(vectors_loc, lang=None):
if lang is None:
nlp = Language()
else:
# create empty language class – this is required if you're planning to
# save the model to disk and load it back later (models always need a
# "lang" setting). Use 'xx' for blank multi-language class.
nlp = spacy.blank(lang)
print('=' * 20)
new_model = KeyedVectors.load_word2vec_format(vectors_loc, binary=True)
for word in new_model.wv.index2word:
vector = numpy.asarray([float(v) for v in new_model[word]], dtype='f')
nlp.vocab.set_vector(word, vector) # add the vectors to the vocab
# print(word, vector)
print('=' * 20)
# test the vectors and similarity
# text = '不同'
# doc = nlp(text)
# print(text, doc[0].similarity(doc[1]))
# print('='*20)
nlp.to_disk("./zh_model")
def save_model(nlp: Language, path: Path) -> None:
nlp.to_disk(path)
logger.info(f"Saved the model in {str(path.absolute())}")
def save_model(nlp: Language, output_path: str):
nlp.to_disk(output_path)
def test_serialize_language_meta_disk(meta_data):
language = Language(meta=meta_data)
with make_tempdir() as d:
language.to_disk(d)
new_language = Language().from_disk(d)
assert new_language.meta == language.meta
"neice",
"king",
"queen",
"dude",
"guy",
"gal",
"fire",
"dog",
"cat",
"mouse",
"red",
"bluee",
"green",
"yellow",
"water",
"person",
"family",
"brother",
"sister",
]
nlp = spacy.load("en_core_web_md")
vec_data = {w: nlp(w).vector for w in words}
vocab = Vocab(strings=words)
for word, vector in vec_data.items():
vocab.set_vector(word, vector)
nlp = Language(vocab=vocab, meta={"lang": "en"})
vocab.to_disk("tests/custom_test_vocab")
print("local vocab saved for spacy")
nlp.to_disk("tests/custom_test_lang")
print("local nlp saved for spacy")
def save_model(model: Language, project=None, session=None):
output_dir = get_model_dir(project, session)
logging.debug("Saving model to {}...".format(output_dir))
model.to_disk(output_dir)
return output_dir
def test_serialize_language_meta_disk(meta_data):
language = Language(meta=meta_data)
with make_tempdir() as d:
language.to_disk(d)
new_language = Language().from_disk(d)
assert new_language.meta == language.meta