---
library_name: transformers
tags:
- dante
- literature
- italian
license: cc-by-sa-4.0
datasets:
- maiurilorenzo/divina-commedia
language:
- it
base_model:
- openai-community/gpt2
pipeline_tag: text-generation
---

# Model Card for DanteGPT

<!-- Provide a quick summary of what the model is/does. -->

This model, **DanteGPT**, is a fine-tuned version of GPT-2 designed to generate text in the style of Dante Alighieri’s *Divina Commedia*. The model emulates Dante's poetic structure, including his use of tercets with a specific rhyme scheme (ABA BCB CDC) and thematic elements of his work, such as divine justice and moral reflection.


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** Lorenzo Maiuri  
- **Funded by:** Independent research  
- **Shared by:** Lorenzo Maiuri
- **Model type:** Fine-tuned GPT-2  
- **Language(s) (NLP):** Italian (`it`)  
- **License:** CC BY-SA 4.0  
- **Finetuned from model:** GPT-2 (base version by OpenAI) 

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** [Hugging Face Model Repository](https://huggingface.co/maiurilorenzo/dante-gpt)
- **Dataset:** [Divina Commedia](https://huggingface.co/datasets/maiurilorenzo/divina-commedia)
- **Kaggle Notebook:** [Link to Kaggle Notebook](https://www.kaggle.com/code/lorenzomaiuri/dante-gpt)
- **Demo:** [DanteGPT Space](https://huggingface.co/spaces/maiurilorenzo/dante-gpt-space)

## Uses

### Try It Out

You can try this model interactively using the [DanteGPT Space](https://huggingface.co/spaces/maiurilorenzo/dante-gpt-space-gpt).  
Simply enter a text prompt, and the model will generate verses in the style of Dante Alighieri!


### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

The model is designed for generating text in the style of the *Divina Commedia* and can be used for literary exploration, creative writing, and educational purposes.

### Downstream Use

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

Users may adapt the model for additional fine-tuning on similar literary texts or use it to generate other forms of poetic or stylistic writing.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

The model may produce inaccurate or nonsensical text when used outside its intended domain. It is not suitable for tasks requiring factual accuracy or ethical decision-making.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

### Biases

- The model reflects the content and biases of the original dataset, which is a historical text. Modern ethical, cultural, and social considerations may not align with the themes or language of Dante's work.

### Risks

- The model may inadvertently generate offensive or inappropriate content when prompted with ambiguous or unrelated topics.
- Over-reliance on this model for literary generation without proper human oversight may lead to misrepresentation of Dante’s work.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should validate generated content for coherence and appropriateness. It is recommended to use the model in combination with literary expertise to ensure quality.

## How to Get Started with the Model

To use the model for text generation, run the following code snippet:

```python
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load model and tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("maiurilorenzo/dante-gpt")
model = GPT2LMHeadModel.from_pretrained("maiurilorenzo/dante-gpt")

# Generate text
prompt = "Nel mezzo del cammin di nostra vita,"
input_ids = tokenizer.encode(prompt, return_tensors="pt")
output = model.generate(input_ids, max_length=100, num_beams=5, no_repeat_ngram_size=2)

print(tokenizer.decode(output[0], skip_special_tokens=True))
```

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

The model was fine-tuned on the Divina Commedia dataset sourced from the Hugging Face Datasets library (`maiurilorenzo/divina-commedia`). The dataset contains cleaned and tokenized text from the original work.

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing

- Removed text exceeding 1024 tokens to ensure compatibility with GPT-2's input limits.
- Split the dataset into training and test subsets.
- Added special tokens `<|startoftext|>` and `<|endoftext|>` to each entry for model training.


#### Training Hyperparameters

Training Hyperparameters

- **Training regime**: FP16 mixed precision
- **Learning rate**: 2e-5
- **Batch size**: 16 (with gradient accumulation to simulate larger batch sizes)
- **Epochs: 5**
- **Optimizer**: AdamW
- **Scheduler**: Linear warm-up with decay

#### Speeds, Sizes, Times

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

- **Training Time**: ~1.5 hours on NVIDIA Tesla P100 (16 GB)
- **Model Size**: ~500 MB

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

A subset of 20 samples from the dataset was held out for testing purposes.

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

Evaluation focused on:

- Coherence of generated text.
- Thematic relevance to the Divina Commedia.

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

<!--- **Perplexity**: A quantitative measure of the model's predictive performance.-->
- **Human Evaluation**: Subjective assessment of the generated text's quality.

### Results

<!--- Perplexity: [Enter Perplexity Score]-->
- Human Evaluation: 75% accuracy in replicating Dante’s style (based on thematic and stylistic criteria).

#### Summary

The model successfully generates stylistically accurate text that aligns with the poetic form and thematic elements of Dante’s work. Inconsistencies in rhyme and coherence may occur in longer outputs.

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** NVIDIA Tesla P100 (16 GB)
- **Hours used:** ~1.5 hours
- **Cloud Provider:** Kaggle
- **Carbon Emitted:** 0.21

## Technical Specifications

### Model Architecture and Objective

- **Base Model**: GPT-2
- **Objective**: Minimize cross-entropy loss between predicted and target tokens in fine-tuned training data.

### Compute Infrastructure

[More Information Needed]

#### Hardware

- **GPU:** NVIDIA Tesla P100 (16 GB)
- **RAM** 32 GB

#### Software

- Hugging Face Transformers
- PyTorch

## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```
@misc{maiurilorenzo/dante-gpt,
  author = {Lorenzo Maiuri},
  title = {DanteGPT: Generating Text in the Style of Dante Alighieri},
  year = {2024},
  publisher = {Hugging Face Hub},
  url = {https://huggingface.co/maiurilorenzo/dante-gpt}
}
```

**APA:**

[Lorenzo Maiuri]. (2024). DanteGPT: Generating Text in the Style of Dante Alighieri. Hugging Face Hub.