---
language:
- en
base_model:
- openai-community/gpt2
tags:
- art
---
# GPT-2 Fine-Tuned on Art Museum Books

## Model Description
This model is a fine-tuned version of GPT-2, adapted to generate text inspired by a self-made dataset of various art museum books. It specializes in creating detailed, context-aware, and stylistically rich descriptions relevant to art and museum contexts.

## Training Details
- **Base Model**: GPT-2  
- **Dataset**: Custom-built dataset containing curated texts from art museum books.  
- **Steps**: Trained for 29,900 steps over ~2 hours.  
- **Hardware**: Google Colab with NVIDIA T4 GPU.  
- **Hyperparameters**:
  - Epochs: 5  
  - Batch Size: 8  
  - Mixed Precision (fp16): Enabled  
  - Save Steps: Every 500 steps  
  - Logging Steps: Every 100 steps  
  - Evaluation Strategy: None  

### Training Script
The model was fine-tuned using the `transformers` library with the following configuration:  

```python
from transformers import TrainingArguments, Trainer
from transformers import DataCollatorForLanguageModeling

output_dir = "/content/drive/MyDrive/gpt2-art"
training_args = TrainingArguments(
    output_dir=output_dir,
    overwrite_output_dir=True,
    num_train_epochs=5,
    per_device_train_batch_size=8,
    save_steps=500,
    save_total_limit=2,
    logging_steps=100,
    evaluation_strategy="no",
    fp16=True,
)

data_collator = DataCollatorForLanguageModeling(
    tokenizer=tokenizer,
    mlm=False,
)