# Import necessary libraries
from transformers import BertConfig, BertForSequenceClassification # Changed "Siglip" to "Bert" for consistency
import torch

# Load pre-trained weights from Hugging Face
model_config = BertConfig(num_labels=2) # Set the number of output labels (in this case, 0 for negative and 1 for positive)
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', config=model_config) # Changed "Siglip" to "Bert" for consistency

# Load some data to train on
train_data = ... # This is where you would load your training data from a CSV file or something similar
val_data = ... # Same for validation data
test_data = ... # And test data too!

# Train the model using PyTorch's built-in optimizer and loss function
optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
loss_function = torch.nn.CrossEntropyLoss()
for epoch in range(3): # We're going to train for 3 epochs (iterations through the data)
    for batch, (input_ids, labels) in enumerate(train_data):
        input_ids = input_ids.to(device) # Convert our inputs and labels to GPU memory if we have one available
        labels = labels.to(device)
        
        output = model(input_ids) # Run the model on our input data
        loss = loss_function(output, labels) # Calculate the loss (how far off our predictions are from the true values)
        
        optimizer.zero_grad() # Clear out any accumulated gradients from previous iterations
        loss.backward() # Compute the gradient of the loss with respect to each parameter in the model
        optimizer.step() # Update the parameters based on our computed gradient and learning rate
        
        if batch % 10 == 0: # Print out some progress updates every 10 batches (or whatever interval you prefer)
            print(f"Epoch {epoch+1}/{num_epochs}, Batch {batch+1}/{len(train_data)}")
            print("Loss:", loss.item())

# Explanation:
# - The first line imports the necessary libraries for the script to run.
# - The second line creates a configuration for the Bert model, specifying the number of output labels.
# - The third line loads the pre-trained Bert model from Hugging Face.
# - The fifth line creates placeholders for the training, validation, and test data.
# - The seventh line initializes the optimizer with the model's parameters and a learning rate.
# - The eighth line specifies the loss function to be used.
# - The ninth line starts a for loop to iterate through the data for a specified number of epochs.
# - The tenth line starts a nested for loop to iterate through the batches of data.
# - The eleventh line converts the input and labels to GPU memory if available.
# - The thirteenth line runs the model on the input data.
# - The fourteenth line calculates the loss between the model's output and the true labels.
# - The sixteenth line clears any accumulated gradients from previous iterations.
# - The seventeenth line computes the gradient of the loss with respect to each parameter in the model.
# - The eighteenth line updates the parameters based on the computed gradient and learning rate.
# - The twentieth line prints out progress updates every 10 batches.
# - The twenty-first line prints out the current epoch and batch number, as well as the loss.