def benchmark_bert_mdl():
bert = load_bert_ner_model()
start = time.time()
predictions = []
for i, sentence in enumerate(sentences_tokens):
_, pred_ents = bert.predict(sentence)
predictions.append(pred_ents)
print('BERT:')
print_speed_performance(start, num_sentences, num_tokens)
assert len(predictions) == num_sentences
print(f1_report(sentences_entities, remove_miscs(predictions), bio=True))
def benchmark_bert_mdl():
bert = load_bert_ner_model()
start = time.time()
predictions = []
for i, sentence in enumerate(sentences_tokens):
_, pred_ents = bert.predict(sentence)
predictions.append(pred_ents)
print('bert:')
print("Made predictions on {} sentences and {} tokens in {}s".format(
num_sentences, num_tokens,
time.time() - start))
assert len(predictions) == num_sentences
print(
classification_report(sentences_entities,
remove_miscs(predictions),
digits=4))
def setup(self):
self.model = dm.load_bert_ner_model()
# general
import numpy as np
import time
import torch
# models
from danlp.models import load_bert_ner_model #, load_flair_ner_model
# dataset
from danlp.datasets import DDT
# utils
#from flair.data import Sentence, Token
# load models
bert = load_bert_ner_model()
'''flair = load_flair_ner_model()'''
# CUDA for PyTorch
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
torch.backends.cudnn.benchmark = True
# get data (splitted into a training set, a validation set, and a test set)
ddt = DDT()
train, valid, test = ddt.load_as_simple_ner(True)
# divide the observations and the targets of the testset into new variables
sentences, categories = test
batch_size = 64