# Step 1: Prepare the data
- Read the text file containing English and German sentences.
- Split the lines and extract the English and German parts.
- Store the English sentences in the input_texts list and German sentences in the target_texts list.
- Write the sentences to a CSV file named 'deu_deu.csv' with columns 'eng' and 'deu'.

# Step 2: Load the pre-trained T5 model and tokenizer
- Load the 't5-base' model and tokenizer, which are pre-trained on various language tasks.

# Step 3: Tokenize the input and target texts
- Use the tokenizer to convert the input_texts and target_texts into tokenized representations.
- Pad the tokenized sequences to the same length and create attention masks.

# Step 4: Fine-tune the T5 model on the translation task
- Define an optimizer (AdamW) to update the model's parameters.
- Set the model to training mode and iterate over a specified number of epochs.
- Zero the gradients, compute model outputs, calculate the loss, backpropagate, and update the parameters.
- Print the loss for each epoch.