def test_train_load_use_classifier(results_base_path, tasks_base_path):
corpus = NLPTaskDataFetcher.load_corpus(NLPTask.IMDB, base_path=tasks_base_path)
label_dict = corpus.make_label_dictionary()
glove_embedding: WordEmbeddings = WordEmbeddings('en-glove')
document_embeddings: DocumentLSTMEmbeddings = DocumentLSTMEmbeddings([glove_embedding], 128, 1, False, 64, False,
False)
model = TextClassifier(document_embeddings, label_dict, False)
trainer = ModelTrainer(model, corpus)
trainer.train(results_base_path, EvaluationMetric.MICRO_F1_SCORE, max_epochs=2, test_mode=True)
sentence = Sentence("Berlin is a really nice city.")
for s in model.predict(sentence):
for l in s.labels:
assert (l.value is not None)
assert (0.0 <= l.score <= 1.0)
assert (type(l.score) is float)
loaded_model = TextClassifier.load_from_file(results_base_path / 'final-model.pt')
sentence = Sentence('I love Berlin')
sentence_empty = Sentence(' ')
loaded_model.predict(sentence)
loaded_model.predict([sentence, sentence_empty])
loaded_model.predict([sentence_empty])
# clean up results directory
shutil.rmtree(results_base_path)
def test_train_load_use_classifier_multi_label(results_base_path, tasks_base_path):
# corpus = NLPTaskDataFetcher.load_corpus('multi_class', base_path=tasks_base_path)
corpus = NLPTaskDataFetcher.load_classification_corpus(
data_folder=tasks_base_path / "multi_class"
)
label_dict = corpus.make_label_dictionary()
word_embedding: WordEmbeddings = WordEmbeddings("turian")
document_embeddings = DocumentRNNEmbeddings(
embeddings=[word_embedding],
hidden_size=32,
reproject_words=False,
bidirectional=False,
)
model = TextClassifier(document_embeddings, label_dict, multi_label=True)
trainer = ModelTrainer(model, corpus)
trainer.train(
results_base_path,
EvaluationMetric.MICRO_F1_SCORE,
mini_batch_size=1,
max_epochs=100,
test_mode=True,
checkpoint=False,
)
sentence = Sentence("apple tv")
for s in model.predict(sentence):
for l in s.labels:
print(l)
assert l.value is not None
assert 0.0 <= l.score <= 1.0
assert type(l.score) is float
sentence = Sentence("apple tv")
for s in model.predict(sentence):
assert "apple" in sentence.get_label_names()
assert "tv" in sentence.get_label_names()
for l in s.labels:
print(l)
assert l.value is not None
assert 0.0 <= l.score <= 1.0
assert type(l.score) is float
loaded_model = TextClassifier.load_from_file(results_base_path / "final-model.pt")
sentence = Sentence("I love Berlin")
sentence_empty = Sentence(" ")
loaded_model.predict(sentence)
loaded_model.predict([sentence, sentence_empty])
loaded_model.predict([sentence_empty])
# clean up results directory
shutil.rmtree(results_base_path)
def test_train_charlm__nocache_load_use_classifier():
corpus = NLPTaskDataFetcher.fetch_data(NLPTask.IMDB)
label_dict = corpus.make_label_dictionary()
glove_embedding: TokenEmbeddings = CharLMEmbeddings('news-forward-fast', use_cache=False)
document_embeddings: DocumentLSTMEmbeddings = DocumentLSTMEmbeddings([glove_embedding], 128, 1, False, 64,
False,
False)
model = TextClassifier(document_embeddings, label_dict, False)
trainer = TextClassifierTrainer(model, corpus, label_dict, False)
trainer.train('./results', max_epochs=2)
sentence = Sentence("Berlin is a really nice city.")
for s in model.predict(sentence):
for l in s.labels:
assert (l.value is not None)
assert (0.0 <= l.score <= 1.0)
assert (type(l.score) is float)
loaded_model = TextClassifier.load_from_file('./results/final-model.pt')
sentence = Sentence('I love Berlin')
sentence_empty = Sentence(' ')
loaded_model.predict(sentence)
loaded_model.predict([sentence, sentence_empty])
loaded_model.predict([sentence_empty])
# clean up results directory
shutil.rmtree('./results')
def test_train_charlm_nocache_load_use_classifier(results_base_path, tasks_base_path):
corpus = NLPTaskDataFetcher.load_corpus("imdb", base_path=tasks_base_path)
label_dict = corpus.make_label_dictionary()
embedding: TokenEmbeddings = FlairEmbeddings("news-forward-fast", use_cache=False)
document_embeddings: DocumentRNNEmbeddings = DocumentRNNEmbeddings(
[embedding], 128, 1, False, 64, False, False
)
model = TextClassifier(document_embeddings, label_dict, False)
trainer = ModelTrainer(model, corpus)
trainer.train(results_base_path, max_epochs=2, test_mode=True)
sentence = Sentence("Berlin is a really nice city.")
for s in model.predict(sentence):
for l in s.labels:
assert l.value is not None
assert 0.0 <= l.score <= 1.0
assert type(l.score) is float
loaded_model = TextClassifier.load_from_file(results_base_path / "final-model.pt")
sentence = Sentence("I love Berlin")
sentence_empty = Sentence(" ")
loaded_model.predict(sentence)
loaded_model.predict([sentence, sentence_empty])
loaded_model.predict([sentence_empty])
# clean up results directory
shutil.rmtree(results_base_path)
def final_test(self, base_path: Path, embeddings_in_memory: bool,
evaluation_metric: EvaluationMetric,
eval_mini_batch_size: int):
log_line(log)
log.info('Testing using best model ...')
self.model.eval()
if (base_path / 'best-model.pt').exists():
if isinstance(self.model, TextClassifier):
self.model = TextClassifier.load_from_file(base_path /
'best-model.pt')
if isinstance(self.model, SequenceTagger):
self.model = SequenceTagger.load_from_file(base_path /
'best-model.pt')
test_metric, test_loss = self.evaluate(
self.model,
self.corpus.test,
eval_mini_batch_size=eval_mini_batch_size,
embeddings_in_memory=embeddings_in_memory)
log.info(
f'MICRO_AVG: acc {test_metric.micro_avg_accuracy()} - f1-score {test_metric.micro_avg_f_score()}'
)
log.info(
f'MACRO_AVG: acc {test_metric.macro_avg_accuracy()} - f1-score {test_metric.macro_avg_f_score()}'
)
for class_name in test_metric.get_classes():
log.info(
f'{class_name:<10} tp: {test_metric.get_tp(class_name)} - fp: {test_metric.get_fp(class_name)} - '
f'fn: {test_metric.get_fn(class_name)} - tn: {test_metric.get_tn(class_name)} - precision: '
f'{test_metric.precision(class_name):.4f} - recall: {test_metric.recall(class_name):.4f} - '
f'accuracy: {test_metric.accuracy(class_name):.4f} - f1-score: '
f'{test_metric.f_score(class_name):.4f}')
log_line(log)
# if we are training over multiple datasets, do evaluation for each
if type(self.corpus) is MultiCorpus:
for subcorpus in self.corpus.corpora:
log_line(log)
self._calculate_evaluation_results_for(subcorpus.name,
subcorpus.test,
evaluation_metric,
embeddings_in_memory,
eval_mini_batch_size,
base_path / 'test.tsv')
# get and return the final test score of best model
if evaluation_metric == EvaluationMetric.MACRO_ACCURACY:
final_score = test_metric.macro_avg_accuracy()
elif evaluation_metric == EvaluationMetric.MICRO_ACCURACY:
final_score = test_metric.micro_avg_accuracy()
elif evaluation_metric == EvaluationMetric.MACRO_F1_SCORE:
final_score = test_metric.macro_avg_f_score()
else:
final_score = test_metric.micro_avg_f_score()
return final_score
def test_train_charlm_load_use_classifier(results_base_path, tasks_base_path):
corpus = NLPTaskDataFetcher.load_corpus(u'imdb', base_path=tasks_base_path)
label_dict = corpus.make_label_dictionary()
glove_embedding = FlairEmbeddings(u'news-forward-fast')
document_embeddings = DocumentLSTMEmbeddings([glove_embedding], 128, 1,
False, 64, False, False)
model = TextClassifier(document_embeddings, label_dict, False)
trainer = ModelTrainer(model, corpus)
trainer.train(results_base_path,
EvaluationMetric.MACRO_F1_SCORE,
max_epochs=2,
test_mode=True)
sentence = Sentence(u'Berlin is a really nice city.')
for s in model.predict(sentence):
for l in s.labels:
assert (l.value is not None)
assert (0.0 <= l.score <= 1.0)
assert (type(l.score) is float)
loaded_model = TextClassifier.load_from_file(
(results_base_path / u'final-model.pt'))
sentence = Sentence(u'I love Berlin')
sentence_empty = Sentence(u' ')
loaded_model.predict(sentence)
loaded_model.predict([sentence, sentence_empty])
loaded_model.predict([sentence_empty])
shutil.rmtree(results_base_path)
def test_train_load_use_classifier_multi_label(results_base_path,
tasks_base_path):
# corpus = NLPTaskDataFetcher.load_corpus('multi_class', base_path=tasks_base_path)
corpus = NLPTaskDataFetcher.load_classification_corpus(
data_folder=tasks_base_path / 'multi_class')
label_dict = corpus.make_label_dictionary()
glove_embedding: WordEmbeddings = WordEmbeddings('en-glove')
document_embeddings = DocumentLSTMEmbeddings(embeddings=[glove_embedding],
hidden_size=32,
reproject_words=False,
bidirectional=False)
model = TextClassifier(document_embeddings, label_dict, multi_label=True)
trainer = ModelTrainer(model, corpus)
trainer.train(results_base_path,
EvaluationMetric.MICRO_F1_SCORE,
max_epochs=100,
test_mode=True,
checkpoint=False)
sentence = Sentence('apple tv')
for s in model.predict(sentence):
for l in s.labels:
print(l)
assert (l.value is not None)
assert (0.0 <= l.score <= 1.0)
assert (type(l.score) is float)
sentence = Sentence("apple tv")
for s in model.predict(sentence):
assert ('apple' in sentence.get_label_names())
assert ('tv' in sentence.get_label_names())
for l in s.labels:
print(l)
assert (l.value is not None)
assert (0.0 <= l.score <= 1.0)
assert (type(l.score) is float)
loaded_model = TextClassifier.load_from_file(results_base_path /
'final-model.pt')
sentence = Sentence('I love Berlin')
sentence_empty = Sentence(' ')
loaded_model.predict(sentence)
loaded_model.predict([sentence, sentence_empty])
loaded_model.predict([sentence_empty])
# clean up results directory
shutil.rmtree(results_base_path)
def __init__(self, nlp, model_file):
self.nlp = nlp
self.clf = TextClassifier.load_from_file(model_file)
for label in self.clf.label_dictionary.get_items():
self.nlp.vocab.strings.add(label)
#split = tag.split('-')
# add tags without iob prefix to string store
#if len(split) == 2:
# self.nlp.vocab.strings.add(split[1])
Doc.set_extension('rels', default=[])
def tagging(self, dataset):
classifier = TextClassifier.load_from_file(os.path.join(self.model_path, "best-model.pt"))
sentences = dataset.df['clean_text'].values
results = []
for sent in sentences:
if sent:
sentence = Sentence(sent)
classifier.predict(sentence)
result = sentence.labels[0].value
else:
result = 0
results.append(result)
return results
def classify(data, labels, test, train, validation):
train_data = [k for k in data.keys() if k in train]
train_labels = [labels[k] for k in train_data]
train_data = [data[k] for k in train_data]
test_data = [k for k in data.keys() if k in test]
test_labels = [labels[k] for k in test_data]
test_data = [data[k] for k in test_data]
validation_data = [k for k in data.keys() if k in validation]
validation_labels = [labels[k] for k in validation_data]
validation_data = [data[k] for k in validation_data]
save_training_files(train_data, train_labels, test_data, test_labels,
validation_data, validation_labels)
corpus = NLPTaskDataFetcher.load_classification_corpus(
Path('./'),
test_file='test.txt',
dev_file='dev.txt',
train_file='train.txt')
word_embeddings = [
WordEmbeddings('pl'),
FlairEmbeddings('polish-forward'),
FlairEmbeddings('polish-backward')
]
doc_embeddings = DocumentRNNEmbeddings(word_embeddings,
hidden_size=512,
reproject_words=True,
reproject_words_dimension=256)
classifier = TextClassifier(
doc_embeddings,
label_dictionary=corpus.make_label_dictionary(),
multi_label=False)
trainer = ModelTrainer(classifier, corpus)
trainer.train('./', max_epochs=25)
classifier = TextClassifier.load_from_file('./best-model.pt')
validation_data = [Sentence(x) for x in validation_data]
for x in validation_data:
classifier.predict(x)
predicted = [int(x.labels[0].value) for x in validation_data]
remove_training_files()
precision, recall, f1, _ = precision_recall_fscore_support(
validation_labels, predicted, average='binary')
return {
'accuracy': float("{:.3f}".format(round(precision, 3))),
'recall': float("{:.3f}".format(round(recall, 3))),
'f1': float("{:.3f}".format(round(f1, 3)))
}
def load_models(self) -> None:
"""Load intent classifier and named entity recognizers."""
# pylint: disable=E0401
from flair.models import TextClassifier, SequenceTagger
# Load mapping from intent id to user intent name
if self.intent_map is None:
intent_map_path = self.profile.read_path(
self.profile.get("training.intent.intent_map", "intent_map.json")
)
with open(intent_map_path, "r") as intent_map_file:
self.intent_map = json.load(intent_map_file)
data_dir = self.profile.read_path(
self.profile.get("intent.flair.data_dir", "flair_data")
)
# Only load intent classifier if there is more than one intent
if (self.class_model is None) and (len(self.intent_map) > 1):
class_model_path = os.path.join(
data_dir, "classification", "final-model.pt"
)
self._logger.debug("Loading classification model from %s", class_model_path)
self.class_model = TextClassifier.load_from_file(class_model_path)
self._logger.debug("Loaded classification model")
if self.ner_models is None:
ner_models = {}
ner_data_dir = os.path.join(data_dir, "ner")
for file_name in os.listdir(ner_data_dir):
ner_model_dir = os.path.join(ner_data_dir, file_name)
if os.path.isdir(ner_model_dir):
# Assume directory is intent name
intent_name = file_name
if intent_name not in self.intent_map:
self._logger.warning(
"%s was not found in intent map", intent_name
)
ner_model_path = os.path.join(ner_model_dir, "final-model.pt")
self._logger.debug("Loading NER model from %s", ner_model_path)
ner_models[intent_name] = SequenceTagger.load_from_file(
ner_model_path
)
self._logger.debug("Loaded NER model(s)")
self.ner_models = ner_models
def __init__(self, nlp, model_file):
self.nlp = nlp
self.clf = TextClassifier.load_from_file(model_file)
# self.concept_map = pickle.load(open(dict_map_path, "rb"))
self.concept_map = {'_UNK': [0], 'Dosing': [1], 'State_of_health': [2], 'Measurement': [3], 'Negation': [4],
'Treatment': [5], 'Medical_condition': [6], 'Process': [7], 'Medication': [8],
'Person': [9], 'Medical_device': [10], 'Time_information': [11], 'Body_part': [12],
'DiagLab_Procedure': [13], 'Local_specification': [14], 'Biological_chemistry': [15],
'Structure_element': [16], 'Biological_parameter': [17], 'Body_Fluid': [18], 'Type': [19],
'Speculation': [20], 'Tissue': [21], 'Degree': [22], 'Medical_specification': [23],
'Temporal_course': [24]}
for label in self.clf.label_dictionary.get_items():
self.nlp.vocab.strings.add(label)
# split = tag.split('-')
# add tags without iob prefix to string store
# if len(split) == 2:
# self.nlp.vocab.strings.add(split[1])
Doc.set_extension('rels', default=[], force=True)
def evaluate(test_file, model_file, dataset_format='macss', semeval_scoring=False):
if semeval_scoring:
eval_script = cached_path(
'https://raw.githubusercontent.com/vzhong/semeval/master/dataset/SemEval2010_task8_scorer-v1.2/semeval2010_task8_scorer-v1.2.pl',
cache_dir='scripts')
chmod(eval_script, 0o777)
classifier: TextClassifier = TextClassifier.load_from_file(model_file)
#sentences_test: List[Sentence] = load_sentences_jsonl(test_file, attach_id=True)
idx2item = load_idx2item(join(dirname(test_file), 'vocabulary/embeddings.csv'))
load_dataset = dataset_loader[dataset_format]
sentences_test: List[Sentence] = load_dataset(test_file, idx2item, is_test=False, attach_id=True)
sentences_pred: List[Sentence] = load_dataset(test_file, idx2item, is_test=True, attach_id=True)
sentences_pred = classifier.predict(sentences_pred)
if semeval_scoring:
id_labels_true = [(sentence.id_, sentence.labels[0]) for sentence in sentences_test]
id_labels_pred = [(sentence.id_, sentence.labels[0]) for sentence in sentences_pred]
input_files = []
for id_labels in [id_labels_true, id_labels_pred]:
tmp_file = NamedTemporaryFile(delete=True)
input_files.append(tmp_file)
with open(tmp_file.name, 'w') as f:
for id_, label in id_labels:
f.write('{}\t{}\n'.format(id_, label.name))
tmp_file.file.close()
p = run([eval_script, input_files[0].name, input_files[1].name], stdout=PIPE, encoding='utf-8')
main_result = p.stdout
print(main_result)
else:
y_true = [sentence.labels[0].name for sentence in sentences_test]
y_pred = [sentence.labels[0].name for sentence in sentences_pred]
print(classification_report(y_true, y_pred))
def load_models(
class_model_path: Optional[str] = None,
ner_model_paths: List[str] = []
) -> Tuple[Optional[TextClassifier], Dict[str, SequenceTagger]]:
# Only load intent classifier if there is more than one intent
class_model = None
if (class_model_path is not None) and os.path.exists(class_model_path):
logger.debug(f"Loading classification model from {class_model_path}")
class_model = TextClassifier.load_from_file(class_model_path)
logger.debug("Loaded classification model")
ner_models = {}
for ner_model_path in ner_model_paths:
# Assume directory name is intent name
intent_name = os.path.split(os.path.split(ner_model_path)[0])[1]
logger.debug(
f"Loading NER model from {ner_model_path} for intent {intent_name}"
)
ner_models[intent_name] = SequenceTagger.load_from_file(ner_model_path)
logger.debug("Loaded NER model(s)")
return class_model, ner_models
from __future__ import absolute_import
from bson.objectid import ObjectId
from flask_cors import CORS
from flair.models import TextClassifier
from flair.data import Sentence
from flask import session
import os
model_path = '/root/flask_vue_ML'
expose = Flask(__name__)
expose.secret_key = "super_secret_key"
# expose.config['MONGO_DBNAME'] = 'exposeModel'
# expose.config['MONGO_URI'] = 'mongodb://*****:*****@expose.route('/', methods=['GET'])
def index():
return jsonify("welcome to Arafa API")
@expose.route('/api/tasks', methods=['GET'])
def get_result():
result = []
try:
data_result = session['my_result']
result.append ({'title': data_result['title'], 'tag': data_result['tag'] })
except:
result.append ({'title': 'The txt you input', 'tag': 'spam or harm' })
return jsonify(result)
def __init__(self, c_classifier_file, p_classifier_file):
self.classifier_c = TextClassifier.load_from_file(c_classifier_file)
self.classifier_p = TextClassifier.load_from_file(p_classifier_file)
def main(args):
args = parser.parse_args()
# Loading classifier model:
print("Loading classifier model")
classifier = TextClassifier.load_from_file(join(args.model_dir, 'best-model.pt'))
txt_files = glob.glob(join(args.data_dir, '*.txt'))
sent_splitter = PunktSentenceTokenizer()
tokenizer = TreebankWordTokenizer()
sentence_lookahead = 0
for txt_fn in txt_files:
print("Processing %s" % (txt_fn))
ann_input_fn = join(args.data_dir, basename(txt_fn)[:-3]+'ann')
ents, _ = read_brat_file(ann_input_fn)
ann_output_fn = join(args.output_dir, basename(txt_fn)[:-3]+'ann')
with open(txt_fn, 'r') as myfile:
text = myfile.read()
ann_out = open(ann_output_fn, 'w')
# Write entities right away:
for ent_id in ents.keys():
ent = ents[ent_id]
ent_text = text[ent.start:ent.end].replace('\n', ' ')
ann_out.write('%s\t%s %d %d\t%s\n' % (ent_id, ent.cat, ent.start, ent.end, ent_text))
sent_spans = list(sent_splitter.span_tokenize(text))
rel_ind = 0
rel_attempts = 0
for sent_ind in range(len(sent_spans)):
primary_sent_span = sent_spans[sent_ind]
end_window_ind = min(sent_ind+sentence_lookahead, len(sent_spans)-1)
end_sent_span = sent_spans[end_window_ind]
sent = text[primary_sent_span[0]:end_sent_span[1]].replace('\n', ' ')
drug_ents, att_ents = get_span_ents(primary_sent_span, end_sent_span, ents)
for att_ent in att_ents:
for drug_ent in drug_ents:
## Get index of ents into sent:
a1_start = att_ent.start - primary_sent_span[0]
a1_end = att_ent.end - primary_sent_span[0]
a1_text = sent[a1_start:a1_end]
a2_start = drug_ent.start - primary_sent_span[0]
a2_end = drug_ent.end - primary_sent_span[0]
a2_text = sent[a2_start:a2_end]
if a1_start < a2_start:
# arg1 occurs before arg2
rel_text = (sent[:a1_start] +
" %sStart %s %sEnd " % (att_ent.cat, a1_text, att_ent.cat) +
sent[a1_end:a2_start] +
" DrugStart %s DrugEnd" % (a2_text) +
sent[a2_end:])
else:
rel_text = (sent[:a2_start] +
" DrugStart %s DrugEnd " % (a2_text) +
sent[a2_end:a1_start] +
" %sStart %s %sEnd " % (att_ent.cat, a1_text, att_ent.cat) +
sent[a1_end:])
# if att_ent.cat == 'Dosage':
# print("working with Dosage ent")
sentence = Sentence(rel_text, use_tokenizer=True)
labels = classifier.predict(sentence)[0].labels
if len(labels) > 1:
print(' This relation has more than one output label')
label = labels[0].value
# print("Comparing ent %s and ent %s and got %s" % (att_ent.id, drug_ent.id, label))
rel_attempts += 1
if not label == 'None':
# Make sure label corresponds to entity type:
if label.find(att_ent.cat) < 0:
# print(" Skipping found relation where label %s doesn't match arg type %s" % (label, att_ent.cat))
continue
ann_out.write('R%d\t%s Arg1:%s Arg2:%s\n' % (rel_ind, label, att_ent.id, drug_ent.id))
rel_ind += 1
# print("Finished: Found %d relations while making %d classification attempts" % (rel_ind, rel_attempts))
ann_out.close()
import sys
from flair.data import Sentence
from flair.models import TextClassifier
tagger = TextClassifier.load_from_file(
'resources/germeval_2018/results/final-model.pt')
test_filename = sys.argv[1]
with open(test_filename, 'rt') as f:
lines = [line.rstrip() for line in f.readlines()]
for line in lines:
sentence, label, x = line.split("\t")
new_line = [
token.replace('#', '') for token in sentence.split()
if not token[0] in ['@', '&', '|']
]
sentence = " ".join(new_line)
s = Sentence(sentence, use_tokenizer=True)
tagger.predict(s)
label = str(s.labels[0]).split()[0]
print(f"{sentence}\t{label}\tNOT USED")
from flair.models import TextClassifier
from flair.data import Sentence
classifier = TextClassifier.load_from_file("model/best-model.pt")
sentence = Sentence("Hi. Yes mum, I will...")
classifier.predict(sentence)
print(sentence.labels)
from pathlib import Path
from flair.models import TextClassifier
from flair.data import Sentence
import pandas as pd
classifier = TextClassifier.load_from_file('C:/Users/jeanc/Documents/reviews/model/final-model.pt')
# create example sentence
test = pd.DataFrame()
# predict tags and print
def fun(x):
sent = Sentence(x)
classifier.predict(sent)
for label in sent.labels:
return label.value
test['label1'] = test['tweet'].apply(fun)
trainer.train('resources/taggers/ag_news',
learning_rate=0.1,
mini_batch_size=32,
anneal_factor=0.5,
patience=5,
max_epochs=150)
# 8. plot training curves (optional)
from flair.visual.training_curves import Plotter
plotter = Plotter()
plotter.plot_training_curves('resources/taggers/ag_news/loss.tsv')
plotter.plot_weights('resources/taggers/ag_news/weights.txt')
### MAIN
classifier = TextClassifier.load_from_file(
'resources/taggers/ag_news/final-model.pt')
# create example sentence
sentence = Sentence('France is the current world cup winner.')
# predict tags and print
classifier.predict(sentence)
print(sentence.labels)
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'''
## MultiLanguage
def __init__(self):
try:
self.classifier = TextClassifier.load_from_file(
BASE_DIR.joinpath('./models/best-model.pt'))
except FileNotFoundError as ex:
print(ex)
from flair.models import TextClassifier
from flair.data import Sentence
classifier = TextClassifier.load_from_file('./best-model.pt')
sentence = Sentence('add your text here for prediction'
) # add your text here whose label is to be predicted
classifier.predict(sentence)
print(sentence.labels)
