def bilstm_crf_train_eval(config,
import_model,
train_examples,
dev_examples=None,
test_examples=None):
processor = DataProcessor(config.data_dir, config.do_lower_case)
word2id = processor.get_vocab()
config.vocab_size = len(word2id)
train_features = convert_examples_to_features_crf(
examples=train_examples,
word2id=word2id,
label_list=config.label_list,
)
train_dataset = BuildDataSet(train_features)
train_loader = DataLoader(train_dataset,
batch_size=config.batch_size,
shuffle=True)
if dev_examples:
dev_features = convert_examples_to_features_crf(
examples=dev_examples,
word2id=word2id,
label_list=config.label_list,
)
dev_dataset = BuildDataSet(dev_features)
dev_loader = DataLoader(dev_dataset,
batch_size=config.batch_size,
shuffle=True)
else:
dev_loader = None
if test_examples:
test_features = convert_examples_to_features_crf(
examples=test_examples,
word2id=word2id,
label_list=config.label_list,
)
test_dataset = BuildDataSet(test_features)
test_loader = DataLoader(test_dataset,
batch_size=config.batch_size,
shuffle=False)
else:
test_loader = None
logger.info("self config:\n {}".format(config_to_json_string(config)))
model = import_model.Model(config).to(config.device)
best_model = model_train(config,
model,
train_loader,
dev_loader,
to_crf=True)
model_test(config, best_model, test_loader, to_crf=True)
def Task(config):
if config.device.type == 'cuda':
torch.cuda.set_device(config.device_id)
tokenizer = BertTokenizer.from_pretrained(
config.tokenizer_file, do_lower_case=config.do_lower_case)
processor = DataProcessor(data_dir=config.data_dir,
do_lower_case=config.do_lower_case,
language=config.language,
do_preprocessing=config.do_preprocessing,
split=config.split)
config.class_list = processor.get_labels()
config.num_labels = len(config.class_list)
config.speaker_dict = processor.get_speaker_map(
config.speaker_threshold) if config.speaker_tag else None
train_examples = processor.get_train_examples()
dev_examples = processor.get_dev_examples()
test_examples = processor.get_test_examples(config.test_file)
cur_model = MODEL_CLASSES[config.use_model]
model = cur_model(config)
logger.info("self config: {}\n".format(config_to_json_string(config)))
model_example, metrics_result, predict_label = cross_validation(
config=config,
train_examples=train_examples,
dev_examples=dev_examples,
model=model,
tokenizer=tokenizer,
pattern=config.pattern,
test_examples=test_examples)
if config.pattern == 'k_fold':
logger.info('K({})-fold models dev acc: {}'.format(
config.k_fold, metrics_result[0]))
logger.info('K({})-fold models dev f1: {}'.format(
config.k_fold, metrics_result[1]))
dev_acc = np.array(metrics_result[0]).mean()
dev_f1 = np.array(metrics_result[1]).mean()
predict_label = combined_result(predict_label, pattern='average')
else:
dev_acc = metrics_result[0]
dev_f1 = metrics_result[1]
logger.info("dev evaluate average Acc: {}, F1:{}".format(dev_acc, dev_f1))
file_name = '{}_{}_{:>.6f}.csv'.format(
strftime("%m%d-%H%M%S", localtime()), config.language, dev_f1)
predict_to_save(predict_label,
path=config.result_save_path,
file=file_name,
prob_threshold=config.prob_threshold)