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train.py
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train.py
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import numpy as np
import random
import torch
from torch import nn as nn
import json
import torch.optim as optim
from transformers import AutoModel, AutoTokenizer, AdamW
from src.dataset import data_loader
from src.model import PhoBERTChatBot, PhoBERTCustom
from src.trainer import Trainer
CUDA_LAUNCH_BLOCKING=1
SEED = 3004
def set_seeds(seed):
"""Set seeds for reproducibility."""
np.random.seed(seed)
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
set_seeds(seed=SEED)
def train():
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(device)
EPOCHS = 100
train_dir = './data/intent_train.json'
val_dir = './data/intent_val.json'
LEARNING_RATE = 1e-4
PATIENCE = 20
model = PhoBERTChatBot('vinai/phobert-base', 8)
model.to(device)
tokenizer = AutoTokenizer.from_pretrained('vinai/phobert-base')
loss_criteria = nn.CrossEntropyLoss()
optimizer = optim.AdamW(model.parameters(), lr=LEARNING_RATE, eps=1e-8)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode="min", factor=0.1, patience=3)
trainer = Trainer(model=model, device=device, optimizer=optimizer,
loss_fn=loss_criteria, scheduler=scheduler
)
train_data_loader, val_data_loader = trainer.set_up_training_data(train_dir, val_dir, tokenizer)
best_model = trainer.train(EPOCHS, PATIENCE, train_data_loader, val_data_loader)
return best_model