def __init__(
self,
model=None,
model_path: str = None,
entities_to_keep: List[str] = None,
verbose: bool = False,
):
"""
Evaluator for Flair models
:param model: model of type SequenceTagger
:param model_path:
:param entities_to_keep:
:param verbose:
and model expected entity types
"""
super().__init__(
entities_to_keep=entities_to_keep,
verbose=verbose,
)
if model is None:
if model_path is None:
raise ValueError(
"Either model_path or model object must be supplied")
self.model = SequenceTagger.load(model_path)
else:
self.model = model
self.spacy_tokenizer = SpacyTokenizer(
model=spacy.load("en_core_web_lg"))
def build_spacy_tokenizer(model) -> Callable[[str], List[Token]]:
from flair.tokenization import SpacyTokenizer
spacy_tokenizer = SpacyTokenizer(model)
def tokenizer(text: str) -> List[Token]:
return spacy_tokenizer.tokenize(text)
return tokenizer
def test_create_sentence_with_spacy_tokenizer():
sentence:Sentence = Sentence("I love Berlin.", use_tokenizer=SpacyTokenizer("en_core_sci_sm"))
assert 4 == len(sentence.tokens)
assert "I" == sentence.tokens[0].text
assert "love" == sentence.tokens[1].text
assert "Berlin" == sentence.tokens[2].text
assert "." == sentence.tokens[3].text
def predict():
model = SequenceTagger.load('../SI/SI_model.pt')
files = sorted(glob.glob(os.path.join(TEST_FOLDER, '*.txt')))
for file in files:
filename = os.path.basename(file)
with open(file, 'r') as inputs, open(
os.path.join('../SI-predictions', filename), 'w') as output:
article = inputs.read()
splitter = SegtokSentenceSplitter(
tokenizer=SpacyTokenizer('en_core_web_sm'))
sentences = splitter.split(article)
model.predict(sentences, mini_batch_size=16, verbose=True)
for sentence in sentences:
output.write(sentence.to_tagged_string())
def __init__(
self,
model=None,
model_path: str = None,
entities_to_keep: List[str] = None,
verbose: bool = False,
entity_mapping: Dict[str, str] = PRESIDIO_SPACY_ENTITIES,
):
super().__init__(
entities_to_keep=entities_to_keep,
verbose=verbose,
entity_mapping=entity_mapping,
)
if model is None:
if model_path is None:
raise ValueError(
"Either model_path or model object must be supplied")
self.model = SequenceTagger.load(model_path)
else:
self.model = model
self.spacy_tokenizer = SpacyTokenizer(
model=spacy.load("en_core_web_sm"))
s = re.sub("[\x00-\x1f\x7f\u0080-\u009f]+", '', s)
# replace `NO-BREAK SPACE`
return re.sub("\u00a0+", ' ', s)
components = {
"au": [
"house_name", "unit", "house_number", "street", "suburb", "city",
"state", "postcode", "country"
],
"jp":
["state", "city", "street", "house_number", "unit", "postcode", "country"]
}
tokenizers = {
"au": SpacyTokenizer(spacy.load("en_core_web_sm")),
"jp": SpacyTokenizer(spacy.load("ja_core_news_sm"))
}
embeddings = StackedEmbeddings(
[FlairEmbeddings('multi-forward'),
FlairEmbeddings('multi-backward')])
# stacked embedding is recommended but traditional embeddings are trained on single languages
# embeddings = {
# "au": StackedEmbeddings([
# WordEmbeddings('en'),
# FlairEmbeddings('mix-forward'),
# FlairEmbeddings('mix-backward'),
# ]),
# "jp": StackedEmbeddings([
def get_french_tokenizer():
nlp = spacy.blank(name="fr")
nlp.tokenizer = get_tokenizer(model=nlp)
return SpacyTokenizer(nlp)