def main(config: dict):
''' entry point '''
# print tag.
print(10 * '*', __file__, config)
if not os.path.exists(config['saved_dir']):
os.makedirs(config['saved_dir'])
# create datasets. provide train and eval data.
dataset = OverallZeshelDataset(
'../datasets/mel_zeshel_overall/', {
'TRAIN_TASKS_NUM': 3000,
'VALID_TASKS_NUM': 300,
'WAYS_NUM_PRE_TASK': config['way_num'][0],
'SUPPORT_USING_CONTEXT': config['context'],
'QUERY_USING_CONTEXT': config['context']
})
# tensorizer. convert an example to tensors.
tensorizer = EasyBertTokenizer.from_pretrained(
'../pretrain/uncased_L-12_H-768_A-12', {
'FIXED_LEN': 72,
'DO_LOWER_CASE': True
})
# adapter. call tensorizer, convert a batch of examples to big tensors.
adapter = MelAdapter(tensorizer, tensorizer)
# embedding model. for predication.
bert = Bert.from_pretrained('../pretrain/uncased_L-12_H-768_A-12', {
'POOLING_METHOD': 'avg',
'FINETUNE_LAYER_RANGE': '9:12'
})
# prototypical network for training.
model = PrototypicalNetwork(bert, bert)
# trainer. to train siamese bert.
trainer = Trainer({
'dataset': dataset,
'adapter': adapter,
'model': model,
'DEVICE': torch.device(config['device']),
'TRAIN_BATCH_SIZE': 2,
'VALID_BATCH_SIZE': 5,
'ROUND': 3
})
# train start here.
trainer.train()
# test here.
for way_num in config['way_num']:
test_dataset = OverallZeshelDataset(
'../datasets/mel_zeshel_overall/', {
'TRAIN_TASKS_NUM': 0,
'VALID_TASKS_NUM': 0,
'WAYS_NUM_PRE_TASK': way_num,
'SUPPORT_USING_CONTEXT': config['context'],
'QUERY_USING_CONTEXT': config['context']
})
trainer.test(test_dataset.test_data())
# save model
bert.save_pretrained(config['saved_dir'])
def main(config: dict):
''' entry point '''
# print tag.
print(10 * '*', __file__, config)
if not os.path.exists(config['saved_dir']):
os.makedirs(config['saved_dir'])
# create datasets. provide train and eval data.
dataset = NormalCrosselDataset(
'../datasets/mel_crossel_normal/',
{
'TRAIN_TASKS_NUM': 3000,
'VALID_TASKS_NUM': 300,
'WAYS_NUM_PRE_TASK': config['way_num'][0],
'SHOTS_NUM_PRE_WAYS': config['shot_num'],
'TRAIN_WAY_PORTION': 0.9,
'TEST_LANGUAGE': 'bn' # it doesn't matter. we do not test in here.
})
# tensorizer. convert an example to tensors.
tensorizer = EasyBertTokenizer.from_pretrained(
'../pretrain/multi_cased_L-12_H-768_A-12', {
'FIXED_LEN': 16,
'DO_LOWER_CASE': True
})
# adapter. call tensorizer, convert a batch of examples to big tensors.
adapter = MelAdapter(tensorizer, tensorizer)
# embedding model. for predication.
bert = Bert.from_pretrained('../pretrain/multi_cased_L-12_H-768_A-12', {
'POOLING_METHOD': 'avg',
'FINETUNE_LAYER_RANGE': '9:12'
})
# prototypical network for training.
model = PrototypicalNetwork(bert, bert)
# trainer. to train siamese bert.
trainer = Trainer({
'dataset': dataset,
'adapter': adapter,
'model': model,
'DEVICE': torch.device(config['device']),
'TRAIN_BATCH_SIZE': 2,
'VALID_BATCH_SIZE': 5,
'ROUND': 3
})
# train start here.
trainer.train()
# test here.
for way_num in config['way_num']:
for lan in config['lan']:
test_dataset = NormalCrosselDataset(
'../datasets/mel_crossel_normal/',
{
'TRAIN_TASKS_NUM': 0,
'VALID_TASKS_NUM': 0,
'WAYS_NUM_PRE_TASK': way_num,
'SHOTS_NUM_PRE_WAYS': config['shot_num'],
'TEST_LANGUAGE': lan # we test here.
})
print(f'[TEST]: WAY_NUM: {way_num}, LANGUAGE: {lan}')
trainer.test(test_dataset.test_data())
# save model
bert.save_pretrained(config['saved_dir'])
def main(config: dict):
'''
zel pipeline example.
'''
# print tag.
print(10 * '*', __file__, config)
saved_dir = os.path.dirname(config['saved_file'])
if not os.path.exists(saved_dir):
os.makedirs(saved_dir)
# create datasets. provide train and eval data.
dataset = Crossel(
'../datasets/crossel',
{
'TRAIN_WAY_PORTION': 0.9,
'CANDIDATE_USING_TEXT': config['context'],
'TEST_LANGUAGE': 'uk' # it doesn't matter.
})
# tensorizer. convert an example to tensors.
tensorizer = EasyBertTokenizer.from_pretrained(
'../pretrain/multi_cased_L-12_H-768_A-12', {
'FIXED_LEN': config['fixed_len'],
'DO_LOWER_CASE': True
})
# adapter. call tensorizer, convert a batch of examples to big tensors.
adapter = ZelAdapter(tensorizer, tensorizer)
# embedding model. for predication.
bert = Bert.from_pretrained('../pretrain/multi_cased_L-12_H-768_A-12', {
'POOLING_METHOD': 'avg',
'FINETUNE_LAYER_RANGE': '9:12'
})
# siamese bert for training.
model = SimilarNet(bert, bert, bert.config.hidden_size, {
'DROP_OUT_PROB': 0.1,
'ACT_NAME': 'relu',
'USE_BIAS': False
})
# trainer. to train siamese bert.
trainer = Trainer({
'dataset': dataset,
'adapter': adapter,
'model': model,
'DEVICE': torch.device(config['device']),
'TRAIN_BATCH_SIZE': 150,
'VALID_BATCH_SIZE': 500,
'ROUND': 10
})
# train start here.
trainer.train()
# train done, fetch bert model to prediction.
tester = ZelPredictor(
model, adapter, {
'TEST_BATCH_SIZE': 200,
'EMB_BATCH_SIZE': 1000,
'DEVICE': torch.device(config['device'])
})
# add candidates.
tester.set_candidates(dataset.all_candidates())
tester.save(config['saved_file'])
# we start test here.
for lan in config['lan']:
print(f'we are testing lan: {lan}')
test_dataset = Crossel('../datasets/crossel', {'TEST_LANGUAGE': lan})
test_data = test_dataset.test_data()
for i in config['top_what']:
print(f'now top-{i} ACC:')
tester.test(test_data, i)