from practical_2.train import train_model
### For reproducibility.
prepare()
train_dataset = TreeDataset("trees/train.txt")
eval_testset = TreeDataset("trees/dev.txt")
### Now we need to set the tranformation function
model = create_attention_classifier()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
v = model.vocab
transform = lambda example: prepare_example(example, v)
train_dataset.transform = transform
eval_testset.transform = transform
train_dataloader = DataLoader(train_dataset, batch_size=512, collate_fn=pad_batch)
eval_dataloader = DataLoader(eval_testset, batch_size=512, collate_fn=pad_batch)
optimizer = Adam(model.parameters())
eval_callback = ListCallback([
AccuracyCallback()
])
history = train_model(model, optimizer, train_dataloader, eval_dataloader, eval_callback=eval_callback, n_epochs=150,
eval_every=5)
from practical_2.models.BOW import *
from torch.optim import *
from practical_2.callbacks.callbacks import *
from practical_2.models.CBOW import create_cbow_model
from practical_2.prepare import prepare
from practical_2.utils import *
from practical_2.train import train_model
### For reproducibility.
prepare()
train_dataset = TreeDataset("trees/train.txt")
eval_testset = TreeDataset("trees/dev.txt")
### Now we need to set the tranformation function
transform = lambda example: prepare_example(example, train_dataset.v)
train_dataset.transform = transform
eval_testset.transform = transform
collate_fn = lambda x: pad_batch(x, v)
train_dataloader = DataLoader(train_dataset, batch_size=128, collate_fn=collate_fn)
eval_dataloader = DataLoader(eval_testset, batch_size=128, collate_fn=collate_fn)
v = train_dataset.v
model = create_cbow_model(v)
optimizer = Adam(model.parameters(), lr=0.0005)
eval_callback = ListCallback([
AccuracyCallback()
from practical_2.models.DeepCBOW import create_deep_cbow_model, zero_init_function, create_glove_deep_cbow_model, \
create_w2v_deep_cbow_model
from practical_2.prepare import prepare
from practical_2.utils import *
from practical_2.train import train_model
### For reproducibility.
prepare()
train_dataset = TreeDataset("trees/train.txt")
eval_testset = TreeDataset("trees/dev.txt")
model = create_w2v_deep_cbow_model()
### Now we need to set the tranformation function
transform = lambda example: prepare_example(example, model.vocab)
train_dataset.transform = transform
eval_testset.transform = transform
train_dataloader = DataLoader(train_dataset,
batch_size=128,
collate_fn=pad_batch)
eval_dataloader = DataLoader(eval_testset,
batch_size=128,
collate_fn=pad_batch)
optimizer = Adam(model.parameters(), lr=0.0005)
eval_callback = ListCallback([AccuracyCallback()])