how to build own transformer model from tabular data with pyTorch The target column of tabular dataset is category type The feature columns of tabular dataset were numerical or text type I want to out

Here is a sample code for building a transformer model from tabular data with PyTorch:

import torch
import torch.nn as nn
import torch.optim as optim
import torchtext
from torchtext.datasets import TabularDataset
from torchtext.data import Field, LabelField, BucketIterator

# Define the fields for the tabular dataset
text_field = Field(sequential=True, use_vocab=True, tokenize='spacy', lower=True)
numeric_field = Field(sequential=False, use_vocab=False)
label_field = LabelField()

# Load the tabular dataset
train_data, valid_data, test_data = TabularDataset.splits(
    path='data',
    train='train.csv',
    validation='valid.csv',
    test='test.csv',
    format='csv',
    fields=[('text', text_field), ('numeric', numeric_field), ('label', label_field)]
)

# Build the vocabulary
text_field.build_vocab(train_data, max_size=10000, vectors='glove.6B.100d', unk_init=torch.Tensor.normal_)
label_field.build_vocab(train_data)

# Set the device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Define the transformer model
class TransformerModel(nn.Module):
    def __init__(self, num_classes, d_model, nhead, num_layers, dropout):
        super().__init__()
        self.transformer_encoder = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dropout=dropout),
            num_layers=num_layers
        )
        self.fc = nn.Linear(d_model, num_classes)

    def forward(self, text, numeric):
        src_padding_mask = (text == text_field.vocab.stoi[text_field.pad_token]).transpose(0, 1)
        src_key_padding_mask = (text == text_field.vocab.stoi[text_field.pad_token]).transpose(0, 1)
        src = torch.cat((text, numeric.unsqueeze(0).repeat(text.size(0), 1, 1)), dim=2)
        src = src.transpose(0, 1)
        output = self.transformer_encoder(src, src_key_padding_mask=src_key_padding_mask, src_padding_mask=src_padding_mask)
        output = output.mean(dim=0)
        output = self.fc(output)
        return output

# Initialize the model
model = TransformerModel(num_classes=len(label_field.vocab), d_model=128, nhead=8, num_layers=3, dropout=0.1).to(device)

# Define the loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=1e-4)

# Define the batch iterator
train_iter, valid_iter, test_iter = BucketIterator.splits(
    datasets=(train_data, valid_data, test_data),
    batch_sizes=(32, 32, 32),
    sort_key=lambda x: len(x.text),
    sort_within_batch=True,
    device=device
)

# Define the training function
def train(model, iterator, optimizer, criterion):
    epoch_loss = 0
    epoch_acc = 0
    model.train()
    for batch in iterator:
        optimizer.zero_grad()
        text = batch.text
        numeric = batch.numeric
        label = batch.label
        output = model(text, numeric)
        loss = criterion(output, label)
        acc = (output.argmax(dim=1) == label).float().mean()
        loss.backward()
        optimizer.step()
        epoch_loss += loss.item()
        epoch_acc += acc.item()
    return epoch_loss / len(iterator), epoch_acc / len(iterator)

# Define the evaluation function
def evaluate(model, iterator, criterion):
    epoch_loss = 0
    epoch_acc = 0
    model.eval()
    with torch.no_grad():
        for batch in iterator:
            text = batch.text
            numeric = batch.numeric
            label = batch.label
            output = model(text, numeric)
            loss = criterion(output, label)
            acc = (output.argmax(dim=1) == label).float().mean()
            epoch_loss += loss.item()
            epoch_acc += acc.item()
    return epoch_loss / len(iterator), epoch_acc / len(iterator)

# Train the model
best_valid_loss = float('inf')
for epoch in range(10):
    train_loss, train_acc = train(model, train_iter, optimizer, criterion)
    valid_loss, valid_acc = evaluate(model, valid_iter, criterion)
    if valid_loss < best_valid_loss:
        best_valid_loss = valid_loss
        torch.save(model.state_dict(), 'model.pt')
    print(f'Epoch: {epoch+1}')
    print(f'Train Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')
    print(f'Valid Loss: {valid_loss:.3f} | Valid Acc: {valid_acc*100:.2f}%')

# Evaluate the model on the test set
model.load_state_dict(torch.load('model.pt'))
test_loss, test_acc = evaluate(model, test_iter, criterion)
print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%')

# Make predictions on new data
def predict(model, text, numeric):
    model.eval()
    with torch.no_grad():
        text = torch.tensor(text).unsqueeze(1).to(device)
        numeric = torch.tensor(numeric).unsqueeze(0).repeat(text.size(0), 1).to(device)
        output = model(text, numeric)
        probabilities = nn.functional.softmax(output, dim=1)
        _, predicted_label = torch.max(output.data, 1)
        return predicted_label.item(), probabilities[0][predicted_label].item()

# Example usage
text = ['this is an example text']
numeric = [1, 2, 3, 4]
predicted_label, probability = predict(model, text, numeric)
print(f'Predicted Label: {label_field.vocab.itos[predicted_label]} | Probability: {probability:.2f}')
``