Taylor Swift

🚀 🤖 HuggingArtists Model - Taylor Swift 🤖

This model is designed to generate lyrics in the style of Taylor Swift. It's created with the huggingartists tool, enabling users to generate text based on the artist's unique lyrical style.

I was made with huggingartists.

Create your own bot based on your favorite artist with 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/taylor-swift')
generator("I am", num_return_sequences=5)

Or with Transformers library:

from transformers import AutoTokenizer, AutoModelWithLMHead
  
tokenizer = AutoTokenizer.from_pretrained("huggingartists/taylor-swift")

model = AutoModelWithLMHead.from_pretrained("huggingartists/taylor-swift")

✨ Features

• Artist-Specific Generation: Generate lyrics in the style of Taylor Swift.
• Easy to Use: Can be used with standard Transformers pipelines and libraries.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

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

Advanced Usage

from transformers import AutoTokenizer, AutoModelWithLMHead
  
tokenizer = AutoTokenizer.from_pretrained("huggingartists/taylor-swift")

model = AutoModelWithLMHead.from_pretrained("huggingartists/taylor-swift")

📚 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 Taylor Swift.

Dataset is available here. And can be used with:

from datasets import load_dataset

dataset = load_dataset("huggingartists/taylor-swift")

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 Taylor Swift'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.

🔧 Technical Details

The model is fine - tuned from the pre - trained GPT - 2 model on Taylor Swift's lyrics. The training process is tracked using Weights & Biases (W&B) to ensure transparency and reproducibility of the results.

⚠️ Important Note

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.

💡 Usage Tip

For more details, visit the project repository.

📄 About

Built by Aleksey Korshuk