challenging-america-year-pr.../hf_roberta_base/03_train_pytorch_regression.py

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import pickle
from datasets import load_dataset
from transformers import AutoTokenizer, RobertaModel, RobertaTokenizer
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification
from transformers import AdamW
from transformers import get_scheduler
import torch
from tqdm.auto import tqdm
BATCH_SIZE = 4
with open('train_dataset.pickle','rb') as f_p:
train_dataset = pickle.load(f_p)
with open('eval_dataset_small.pickle','rb') as f_p:
eval_dataset_small = pickle.load(f_p)
with open('eval_dataset_full.pickle','rb') as f_p:
eval_dataset_full = pickle.load(f_p)
train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=BATCH_SIZE)
eval_dataloader = DataLoader(eval_dataset_small, batch_size=BATCH_SIZE)
model = AutoModelForSequenceClassification.from_pretrained('roberta-base', num_labels=1)
optimizer = AdamW(model.parameters(), lr=1e-6)
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num_epochs = 3
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num_training_steps = num_epochs * len(train_dataloader)
lr_scheduler = get_scheduler(
"linear",
optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=num_training_steps
)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model.to(device)
progress_bar = tqdm(range(num_training_steps))
model.train()
model.train()
model.to(device)
def transform_batch(batch):
batch['input_ids'] = torch.stack(batch['input_ids']).permute(1,0).to(device)
batch['attention_mask'] = torch.stack(batch['attention_mask']).permute(1,0).to(device)
batch['labels'] = batch['year_scaled'].to(device).float()
batch['labels'].to(device)
batch['input_ids'].to(device)
batch['attention_mask'].to(device)
for c in set(batch.keys()) - {'input_ids', 'attention_mask', 'labels'}:
del batch[c]
return batch
def eval():
model.eval()
eval_loss = 0.0
for i, batch in enumerate(eval_dataloader):
batch = transform_batch(batch)
outputs = model(**batch)
loss = outputs.loss
eval_loss += loss.item()
print(f'eval loss: {eval_loss / i }')
model.train()
for epoch in range(num_epochs):
train_loss = 0.0
for i, batch in enumerate(train_dataloader):
batch = transform_batch(batch)
outputs = model(**batch)
loss = outputs.loss
loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)
train_loss += loss.item()
#import pdb; pdb.set_trace()
if i % 5000 == 0 and i > 1 :
print(f'train loss: {train_loss / 5000 }', end = '\t\t')
train_loss = 0.0
eval()
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model.save_pretrained(f'roberta_year_prediction/epoch_{epoch}')