Create README.md

README.md

@@ -0,0 +1,50 @@
+# 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)