def prepare_data(self):
"""
Downloads the ag_news or 20newsgroup dataset and initializes bert tokenizer
"""
np.random.seed(self.RANDOM_SEED)
torch.manual_seed(self.RANDOM_SEED)
if self.dataset == "20newsgroups":
num_samples = self.args["num_samples"]
self.news_group_df = (
get_20newsgroups(num_samples)
if self.args["dataset"] == "20newsgroups"
else get_ag_news(num_samples)
)
else:
train_iter, test_iter = AG_NEWS()
self.train_dataset = to_map_style_dataset(train_iter)
self.test_dataset = to_map_style_dataset(test_iter)
self.tokenizer = BertTokenizer.from_pretrained(self.PRE_TRAINED_MODEL_NAME)
# is used here to adjust the learning rate through epochs.
#
from torch.utils.data.dataset import random_split
from torchtext.data.functional import to_map_style_dataset
# Hyperparameters
EPOCHS = 10 # epoch
LR = 5 # learning rate
BATCH_SIZE = 64 # batch size for training
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=LR)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.1)
total_accu = None
train_iter, test_iter = AG_NEWS()
train_dataset = to_map_style_dataset(train_iter)
test_dataset = to_map_style_dataset(test_iter)
num_train = int(len(train_dataset) * 0.95)
split_train_, split_valid_ = \
random_split(train_dataset, [num_train, len(train_dataset) - num_train])
train_dataloader = DataLoader(split_train_,
batch_size=BATCH_SIZE,
shuffle=True,
collate_fn=collate_batch)
valid_dataloader = DataLoader(split_valid_,
batch_size=BATCH_SIZE,
shuffle=True,
collate_fn=collate_batch)
test_dataloader = DataLoader(test_dataset,
batch_size=BATCH_SIZE,
def main():
num_args = len(sys.argv)
# Checking if filename input is specified
if num_args < 2:
sys.exit("Please specify an input file")
filename = str(sys.argv[1])
p = Path(filename)
# Checking if filepath is valid and/or file exists
if not (p.exists()):
sys.exit("File not found")
# Prepare data processing pipelines
tokenizer = get_tokenizer('basic_english')
train_iter = AG_NEWS(split='train')
vocab = build_vocab_from_iterator(yield_tokens(train_iter, tokenizer),
specials=["<unk>"])
vocab.set_default_index(vocab["<unk>"])
text_pipeline = lambda x: vocab(tokenizer(x))
label_pipeline = lambda x: int(x) - 1
# Generate data batch and iterator
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def collate_batch(batch):
label_list, text_list, offsets = [], [], [0]
for (_label, _text) in batch:
label_list.append(label_pipeline(_label))
processed_text = torch.tensor(text_pipeline(_text),
dtype=torch.int64)
text_list.append(processed_text)
offsets.append(processed_text.size(0))
label_list = torch.tensor(label_list, dtype=torch.int64)
offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)
text_list = torch.cat(text_list)
return label_list.to(device), text_list.to(device), offsets.to(device)
# This variable needs to be initialized twice or else an IndexError occurs
train_iter = AG_NEWS(split='train')
dataloader = DataLoader(train_iter,
batch_size=8,
shuffle=False,
collate_fn=collate_batch)
# Build an instance
num_class = len(set([label for (label, text) in train_iter]))
vocab_size = len(vocab)
emsize = 64
model = TextClassificationModel(vocab_size, emsize, num_class).to(device)
# Split the dataset and run the model
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=LR)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.1)
total_accu = None
train_iter, test_iter = AG_NEWS()
train_dataset = to_map_style_dataset(train_iter)
test_dataset = to_map_style_dataset(test_iter)
num_train = int(len(train_dataset) * 0.95)
split_train_, split_valid_ = \
random_split(train_dataset,
[num_train, len(train_dataset) - num_train])
train_dataloader = DataLoader(split_train_,
batch_size=BATCH_SIZE,
shuffle=True,
collate_fn=collate_batch)
valid_dataloader = DataLoader(split_valid_,
batch_size=BATCH_SIZE,
shuffle=True,
collate_fn=collate_batch)
test_dataloader = DataLoader(test_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
collate_fn=collate_batch)
# Run epochs
for epoch in range(1, EPOCHS + 1):
epoch_start_time = time.time()
train(train_dataloader, model, optimizer, criterion, epoch)
accu_val = evaluate(valid_dataloader, model, criterion)
if total_accu is not None and total_accu > accu_val:
scheduler.step()
else:
total_accu = accu_val
print('-' * 59)
print('| end of epoch {:3d} | time: {:5.2f}s | '
'valid accuracy {:8.3f} '.format(epoch,
time.time() - epoch_start_time,
accu_val))
print('-' * 59)
print('Checking the results of test dataset.')
accu_test = evaluate(test_dataloader, model, criterion)
print('test accuracy {:8.3f}'.format(accu_test))
# Run article prediction
ag_news_label = {1: "World", 2: "Sports", 3: "Business", 4: "Sci/Tec"}
with p.open() as readfile:
ex_text_str = readfile.read()
model = model.to("cpu")
print("This is a %s news" %
ag_news_label[predict(ex_text_str, text_pipeline, model)])