Explanation: Upload a PDF of various poetry and let the model train on it, use a prompt, and allow the generation of poetry.

Libraries:

transformers: Provides pre-trained language models (like GPT-2) and utilities for fine-tuning. datasets: Facilitates loading and processing of text data for training. torch: The underlying deep learning framework for Transformers. Model Setup:

Loads GPT-2 tokenizer and model (pre-trained on a large corpus of text). Data Preparation:

load_pdf_data: Extracts text from the uploaded PDF. build_text_dataset: Creates a dataset from the text for training. Model Training:

Fine-tune the GPT-2 model on the dataset derived from your PDF. This teaches the model the specific style and patterns of your poetry. Poem Generation:

generate_poem: Uses the fine-tuned model to generate a new poem. You can provide an optional prompt to guide the poem's theme or starting point. max_length controls the maximum length of the generated poem. How to Use:

Upload your PDF of poems in Colab. Run the code. It will fine-tune GPT-2 on your poems. A poem in the style of your PDF will be printed. Feel free to experiment with different prompts! Key Improvements:

Uses GPT-2: A powerful language model known for generating creative text. Fine-tuning: Adapts the model to your specific writing style. Flexibility: Allows you to provide a prompt to guide the generated poem. Let me know if you have any other questions.