# PoeticTextGenerator_GPT2

## 🖋️ Overview

This model is a **GPT-2 Small** variant fine-tuned specifically for the task of **unconditional and conditional poetic text generation**. It has been trained on a curated corpus of classical and contemporary English poetry, allowing it to generate text that mimics meter, rhyme, and figurative language patterns. The model is configured as a `GPT2LMHeadModel` for Language Modeling.

## 🧠 Model Architecture

The model leverages the powerful transformer architecture of the GPT-2 Small base model.

* **Base Model:** `gpt2` (124M parameters)
* **Task:** Causal Language Modeling (`GPT2LMHeadModel`)
* **Tokenization:** Standard GPT-2 Byte Pair Encoding (BPE) tokenizer.
* **Training Data:** Approximately 20,000 poems spanning multiple centuries and styles (e.g., sonnets, free verse, haikus).
* **Hyperparameters:** Fine-tuned with a low learning rate to preserve the linguistic capabilities of the base model while acquiring poetic style.
* **Key Config:** `do_sample=True` and `temperature=0.8` are set as default generation parameters to encourage creative and diverse outputs.


## 💡 Intended Use

* **Creative Writing Assistance:** Providing prompts, completing stanzas, or generating entire poems for writers.
* **Artistic Installations:** Generating dynamic, ever-changing poetic text for digital art or interactive projects.
* **Stylometric Research:** Studying the model's ability to imitate different poetic styles by adjusting the prompt or conditioning data.
* **Educational Tool:** Demonstrating the capabilities of large language models in creative domains.

### How to use

```python
from transformers import pipeline, set_seed

generator = pipeline(
    "text-generation", 
    model="[YOUR_HF_USERNAME]/PoeticTextGenerator_GPT2"
)
set_seed(42)

# Conditional Generation (Prompting a theme)
prompt = "The shadow of the moon fell upon the silent street,"
output = generator(
    prompt, 
    max_length=50, 
    num_return_sequences=1,
    temperature=0.9,
    top_p=0.95,
    do_sample=True
)
print(output[0]['generated_text'])

# Unconditional Generation (Starting from a single word)
# output = generator("A", max_length=100, num_return_sequences=1)