def recognize(test_file_name: str, split_by_paragraphs: bool,
recognizer: BERT_NER, results_file_name: str):
X_test, y_test = load_dataset_from_bio(
test_file_name,
paragraph_separators=({'-DOCSTART-'} if split_by_paragraphs else None),
stopwords={'-DOCSTART-'})
print('The CoNLL-2003 data for final testing have been loaded...')
print('Number of samples is {0}.'.format(len(y_test)))
print('')
y_pred = recognizer.predict(X_test)
f1, precision, recall, quality_by_entities = calculate_prediction_quality(
y_test, y_pred, classes_list=recognizer.classes_list_)
print('All entities:')
print(' F1-score is {0:.2%}.'.format(f1))
print(' Precision is {0:.2%}.'.format(precision))
print(' Recall is {0:.2%}.'.format(recall))
for ne_type in sorted(list(quality_by_entities.keys())):
print(' {0}'.format(ne_type))
print(' F1-score is {0:.2%}.'.format(
quality_by_entities[ne_type][0]))
print(' Precision is {0:.2%}.'.format(
quality_by_entities[ne_type][1]))
print(' Recall is {0:.2%}.'.format(quality_by_entities[ne_type][2]))
print('')
save_dataset_as_bio(test_file_name,
X_test,
y_pred,
results_file_name,
stopwords={'-DOCSTART-'})
def train(train_file_name: str, valid_file_name: str,
split_by_paragraphs: bool, bert_will_be_tuned: bool,
lstm_layer_size: Union[int, None], l2: float, max_epochs: int,
batch_size: int, gpu_memory_frac: float,
model_name: str) -> BERT_NER:
if os.path.isfile(model_name):
with open(model_name, 'rb') as fp:
recognizer = pickle.load(fp)
assert isinstance(recognizer, BERT_NER)
print('The NER has been successfully loaded from the file `{0}`...'.
format(model_name))
print('')
else:
X_train, y_train = load_dataset_from_bio(
train_file_name,
paragraph_separators=({'-DOCSTART-'}
if split_by_paragraphs else None),
stopwords={'-DOCSTART-'})
X_val, y_val = load_dataset_from_bio(
valid_file_name,
paragraph_separators=({'-DOCSTART-'}
if split_by_paragraphs else None),
stopwords={'-DOCSTART-'})
print(
'The CoNLL-2003 data for training and validation have been loaded...'
)
print('Number of samples for training is {0}.'.format(len(y_train)))
print('Number of samples for validation is {0}.'.format(len(y_val)))
print('')
if BERT_NER.PATH_TO_BERT is None:
bert_hub_module_handle = 'https://tfhub.dev/google/bert_cased_L-12_H-768_A-12/1'
else:
bert_hub_module_handle = None
recognizer = BERT_NER(finetune_bert=bert_will_be_tuned,
batch_size=batch_size,
l2_reg=l2,
bert_hub_module_handle=bert_hub_module_handle,
lstm_units=lstm_layer_size,
max_epochs=max_epochs,
patience=5,
gpu_memory_frac=gpu_memory_frac,
verbose=True,
random_seed=42,
lr=1e-6 if bert_will_be_tuned else 1e-4)
recognizer.fit(X_train, y_train, validation_data=(X_val, y_val))
print('')
print(
'The NER has been successfully fitted and saved into the file `{0}`...'
.format(model_name))
y_pred = recognizer.predict(X_val)
f1, precision, recall, quality_by_entities = calculate_prediction_quality(
y_val, y_pred, classes_list=recognizer.classes_list_)
print('All entities:')
print(' F1-score is {0:.2%}.'.format(f1))
print(' Precision is {0:.2%}.'.format(precision))
print(' Recall is {0:.2%}.'.format(recall))
for ne_type in sorted(list(quality_by_entities.keys())):
print(' {0}'.format(ne_type))
print(' F1-score is {0:.2%}.'.format(
quality_by_entities[ne_type][0]))
print(' Precision is {0:.2%}.'.format(
quality_by_entities[ne_type][1]))
print(' Recall is {0:.2%}.'.format(
quality_by_entities[ne_type][2]))
print('')
with open(model_name, 'wb') as fp:
pickle.dump(recognizer, fp)
return recognizer