Metallica

🚀 HuggingArtists Model - Metallica

This is a model based on Metallica's lyrics, created with huggingartists. It allows you to generate text similar to Metallica's style. You can also create your own bot based on your favorite artist using 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/metallica')
generator("I am", num_return_sequences=5)

Or with Transformers library:

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

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

✨ Features

• Generate text based on Metallica's lyrics.
• Developed with the help of huggingartists.

📦 Installation

This model can be used directly through the Transformers library. You can install it via pip:

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 Metallica. Dataset is available here. And can be used with:

from datasets import load_dataset

dataset = load_dataset("huggingartists/metallica")

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 Metallica'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.

🔧 Technical Details

The model uses a pre-trained GPT - 2 architecture and fine - tunes it on Metallica's lyrics dataset. The training process is tracked by Weights & Biases (W&B) to ensure transparency and reproducibility.

📄 License

No license information is provided in the original document.

About

Built by Aleksey Korshuk

For more details, visit the project repository.