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# training/train.py
import pandas as pd
from torch.utils.data import DataLoader, Dataset
from transformers import BertTokenizer
from model.luna_model import LunaAI
import torch
import torch.nn as nn
from transformers import AdamW
class TextDataset(Dataset):
def __init__(self, csv_file):
self.data = pd.read_csv(csv_file)
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
text = self.data.iloc[idx, 0]
label = self.data.iloc[idx, 1]
encoding = self.tokenizer.encode_plus(
text,
add_special_tokens=True,
return_tensors='pt',
padding='max_length',
max_length=128,
truncation=True,
)
return {
'input_ids': encoding['input_ids'].flatten(),
'attention_mask': encoding['attention_mask'].flatten(),
'labels': torch.tensor(label, dtype=torch.long),
}
def train_model(model, dataset):
dataloader = DataLoader(dataset, batch_size=16, shuffle=True)
optimizer = AdamW(model.parameters(), lr=5e-5)
model.train()
for epoch in range(3): # Adjust the number of epochs
for batch in dataloader:
input_ids = batch['input_ids']
attention_mask = batch['attention_mask']
labels = batch['labels']
optimizer.zero_grad()
outputs = model(input_ids, attention_mask)
loss = nn.CrossEntropyLoss()(outputs, labels)
loss.backward()
optimizer.step()
print(f'Epoch {epoch}, Loss: {loss.item()}')
if __name__ == "__main__":
dataset = TextDataset('data/dataset.csv')
model = LunaAI()
train_model(model, dataset)
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