'LEARN_RATE': LEARN_RATE,
'EPOCHS': EPOCHS,
'WARMUP_STEPS': WARMUP_STEPS,
'SEQUENCE_LENGTH': SEQUENCE_LENGTH,
}, config_file, sort_keys=True, indent=4, separators=(',', ': '))
# Load and initialize model
MODEL_CLASS = load_model(MODEL_PREFIX)
TOKENIZER = MODEL_CLASS[0].from_pretrained(MODEL_NAME)
CONFIG = MODEL_CLASS[1].from_pretrained(MODEL_NAME, num_labels=3)
MODEL = MODEL_CLASS[2].from_pretrained(MODEL_NAME, config=CONFIG)
# Load training data
train_dataset = dataset(
tokenize(chain(*(load_semeval(DATASET, 'train', lang) for lang in LANGS)), TOKENIZER, SEQUENCE_LENGTH))
train_sampler = RandomSampler(train_dataset)
train_dataset = DataLoader(train_dataset, sampler=train_sampler, batch_size=TRAIN_BATCH_SIZE, drop_last=True)
# Run Training
training(
train_dataset,
val_datasets(TOKENIZER, SEQUENCE_LENGTH),
MODEL,
EXPERIMENT,
LEARN_RATE,
WARMUP_STEPS,
TRAIN_BATCH_SIZE,
EPOCHS,
ACCUMULATION_STEPS
)
'LEARN_RATE': LEARN_RATE,
'EPOCHS': EPOCHS,
'WARMUP_STEPS': WARMUP_STEPS,
'SEQUENCE_LENGTH': SEQUENCE_LENGTH,
},
config_file,
sort_keys=True,
indent=4,
separators=(',', ': '))
# Load and initialize model
MODEL_CLASS = load_model(MODEL_PREFIX)
TOKENIZER = MODEL_CLASS[0].from_pretrained(MODEL_NAME)
CONFIG = MODEL_CLASS[1].from_pretrained(MODEL_NAME, num_labels=3)
MODEL = MODEL_CLASS[2].from_pretrained(MODEL_NAME, config=CONFIG)
# Load training data
train_dataset = dataset(
tokenize(chain(*(load_semeval(DATASET, 'train', lang) for lang in LANGS)),
TOKENIZER, SEQUENCE_LENGTH))
train_sampler = RandomSampler(train_dataset)
train_dataset = DataLoader(train_dataset,
sampler=train_sampler,
batch_size=TRAIN_BATCH_SIZE,
drop_last=True)
# Run Training
training(train_dataset, val_datasets(TOKENIZER, SEQUENCE_LENGTH), MODEL,
EXPERIMENT, LEARN_RATE, WARMUP_STEPS, TRAIN_BATCH_SIZE, EPOCHS,
ACCUMULATION_STEPS)
