Skillet

🚀 HuggingArtists Skillet Model

This is a model based on Skillet's lyrics, created using huggingartists. You can create your own bot inspired by your favorite artist via the demo!

🚀 Quick Start

You can use this model directly with a pipeline for text generation:

from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/skillet')
generator("I am", num_return_sequences=5)

Or with Transformers library:

from transformers import AutoTokenizer, AutoModelWithLMHead
  
tokenizer = AutoTokenizer.from_pretrained("huggingartists/skillet")

model = AutoModelWithLMHead.from_pretrained("huggingartists/skillet")

✨ Features

• Built upon huggingartists framework.
• Can generate text based on Skillet's lyrics style.

📦 Installation

No specific installation steps are provided in the original README. However, to use the model, you need to have the transformers and datasets libraries installed:

pip install transformers datasets

📚 Documentation

How does it work?

To understand how the model was developed, check the W&B report.

Training data

The model was trained on lyrics from Skillet. Dataset is available here. And can be used with:

from datasets import load_dataset

dataset = load_dataset("huggingartists/skillet")

Explore the data, which is tracked with W&B artifacts at every step of the pipeline.

Training procedure

The model is based on a pre-trained GPT-2 which is fine-tuned on Skillet's lyrics. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned.

Limitations and bias

The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model.

📄 About

Built by Aleksey Korshuk

For more details, visit the project repository.