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from transformers import AutoTokenizer, AutoModelForSequenceClassification, AdamW, get_linear_schedule_with_warmup
from sklearn.metrics import classification_report, f1_score
from torch.utils.data import Dataset, DataLoader
from argparse import ArgumentParser
from str2bool import str2bool
from torch import nn
import pandas as pd
import numpy as np
import torch
parser = ArgumentParser()
parser.add_argument("-dataframe", required=True, help="Path to dataframe with columns ['text', 'label', 'split']") # 'data/small_dataset.csv'
parser.add_argument("-model",required=True, help='Pre-traied model from huggingface or path to local folder with config.json') # '../norbert3-x-small/'
parser.add_argument("-custom_wrapper", default=False, type=lambda x: bool(str2bool(x)), help='Boolean argument - True if use custom wrapper, False if use AutoModelForSequenceClassification') # True
parser.add_argument("-lr", default='1e-05', help='Learning rate.')
parser.add_argument("-max_length", default='512', help='Max lenght of the sequence in tokens.')
parser.add_argument("-warmup", default='2', help='The number of steps for the warmup phase.')
parser.add_argument("-batch_size", default='4', help='Batch size.')
parser.add_argument("-epochs", default='20', help='Number of epochs for training.')
args = parser.parse_args()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class Dataset(Dataset):
def __init__(self, texts, targets, tokenizer, max_len):
self.texts = texts
self.targets = targets
self.tokenizer = tokenizer
self.max_len = max_len
def __len__(self):
return len(self.texts)
def __getitem__(self, item):
text = str(self.texts[item])
target = self.targets[item]
encoding = self.tokenizer.encode_plus(
text,
add_special_tokens=True,
max_length=self.max_len,
return_token_type_ids=False,
pad_to_max_length=True,
return_attention_mask=True,
truncation=True,
return_tensors='pt',
)
return {
'text': text,
'input_ids': encoding['input_ids'].flatten(),
'attention_mask': encoding['attention_mask'].flatten(),
'targets': torch.tensor(target, dtype=torch.long)
}
def create_data_loader(df, tokenizer, max_len, batch_size):
ds = Dataset(
texts=df.text.to_numpy(),
targets=df.label.to_numpy(),
tokenizer=tokenizer,
max_len=max_len
)
return DataLoader(
ds,
batch_size=batch_size
)
class SentimentClassifier(nn.Module):
def __init__(self, n_classes):
super(SentimentClassifier, self).__init__()
if not args.custom_wrapper:
self.bert = AutoModelForSequenceClassification.from_pretrained(args.model, num_labels=n_classes, ignore_mismatched_sizes=True)
if args.custom_wrapper:
from modeling_norbert import NorbertForSequenceClassification
self.bert = NorbertForSequenceClassification.from_pretrained(args.model, num_labels=n_classes, ignore_mismatched_sizes=True)
def forward(self, input_ids, attention_mask):
bert_output = self.bert(
input_ids=input_ids,
attention_mask=attention_mask,
return_dict=True
)
logits = bert_output.logits
return logits
def train_epoch(
model,
data_loader,
loss_fn,
optimizer,
device,
scheduler,
n_examples
):
y_true, y_pred = [], []
model = model.train()
losses = []
correct_predictions = 0
for d in data_loader:
input_ids = d["input_ids"].to(device)
attention_mask = d["attention_mask"].to(device)
targets = d["targets"].to(device)
y_true += targets.tolist()
outputs = model(
input_ids=input_ids,
attention_mask=attention_mask
)
preds_idxs = torch.max(outputs, dim=1).indices
y_pred += preds_idxs.numpy().tolist()
loss = loss_fn(outputs, targets)
correct_predictions += torch.sum(preds_idxs == targets)
losses.append(loss.item())
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
scheduler.step()
optimizer.zero_grad()
f1 = f1_score(y_true, y_pred, average='macro')
return correct_predictions.double() / n_examples, np.mean(losses), f1
def eval_model(model, data_loader, loss_fn, device, n_examples):
model = model.eval()
losses = []
correct_predictions = 0
y_true, y_pred = [], []
with torch.no_grad():
for d in data_loader:
input_ids = d["input_ids"].to(device)
attention_mask = d["attention_mask"].to(device)
targets = d["targets"].to(device)
y_true += targets.tolist()
outputs = model(
input_ids=input_ids,
attention_mask=attention_mask
)
_, preds = torch.max(outputs, dim=1)
y_pred += preds.tolist()
loss = loss_fn(outputs, targets)
correct_predictions += torch.sum(preds == targets)
losses.append(loss.item())
f1 = f1_score(y_true, y_pred, average='macro')
report = classification_report(y_true, y_pred)
return correct_predictions.double() / n_examples, np.mean(losses), f1, report
df = pd.read_csv(args.dataframe)
df_train = df[df['split'] == 'train']
df_val = df[df['split'] == 'dev']
df_test = df[df['split'] == 'test']
print(f'Train samples: {len(df_train)}')
print(f'Validation samples: {len(df_val)}')
print(f'Test samples: {len(df_test)}')
tokenizer = AutoTokenizer.from_pretrained(args.model)
max_length = int(args.max_length)
batch_size = int(args.batch_size)
epochs = int(args.epochs)
train_data_loader = create_data_loader(df_train, tokenizer, max_length, batch_size)
val_data_loader = create_data_loader(df_val, tokenizer, max_length, batch_size)
test_data_loader = create_data_loader(df_test, tokenizer, max_length, batch_size)
class_names = df.label.unique()
model = SentimentClassifier(len(class_names))
model = model.to(device)
loss_fn = nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.AdamW(model.parameters(), lr=float(args.lr))
total_steps = len(train_data_loader) * epochs
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=int(args.warmup),
num_training_steps=total_steps
)
for epoch in range(epochs):
print(f'Epoch {epoch + 1}/{epochs}')
print('-' * 10)
train_acc, train_loss, train_f1 = train_epoch(
model,
train_data_loader,
loss_fn,
optimizer,
device,
scheduler,
len(df_train)
)
print()
print(f'Train loss -- {train_loss} -- accuracy {train_acc} -- f1 {train_f1}')
# save model
model_name = args.model.split('/')[-1] if args.model.split('/')[-1] != '' else args.model.split('/')[-2]
torch.save(model.state_dict(),f'saved_models/{model_name}_epoch_{epochs}.bin')
val_acc, val_loss, val_f1, report = eval_model(
model,
val_data_loader,
loss_fn,
device,
len(df_val)
)
print()
print(f'Val loss {val_loss} -- accuracy -- {val_acc} -- f1 {val_f1}')
print(report)
test_acc, test_loss, test_f1, test_report = eval_model(
model,
test_data_loader,
loss_fn,
device,
len(df_test)
)
print()
print('-------------TESTINGS-----------------')
print()
print(f'Test accuracy {test_acc}, f1 {test_f1}')
print(test_report) |