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
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)
You can try this model interactively using the DanteGPT Space.
Simply enter a text prompt, and the model will generate verses in the style of Dante Alighieri!
Direct Use
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
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
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
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
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
1from transformers import GPT2LMHeadModel, GPT2Tokenizer
23# Load model and tokenizer4tokenizer = GPT2Tokenizer.from_pretrained("maiurilorenzo/dante-gpt")5model = GPT2LMHeadModel.from_pretrained("maiurilorenzo/dante-gpt")67# Generate text8prompt ="Nel mezzo del cammin di nostra vita,"9input_ids = tokenizer.encode(prompt, return_tensors="pt")10output = model.generate(input_ids, max_length=100, num_beams=5, no_repeat_ngram_size=2)1112print(tokenizer.decode(output[0], skip_special_tokens=True))
Training Details
Training Data
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
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 Time: ~1.5 hours on NVIDIA Tesla P100 (16 GB)
Model Size: ~500 MB
Evaluation
Testing Data, Factors & Metrics
Testing Data
A subset of 20 samples from the dataset was held out for testing purposes.
Factors
Evaluation focused on:
Coherence of generated text.
Thematic relevance to the Divina Commedia.
Metrics
Human Evaluation: Subjective assessment of the generated text's quality.
Results
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.
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
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.
