bruce-springsteen /
AlekseyKorshuk's picture
language: en
  - huggingartists/bruce-springsteen
  - huggingartists
  - lyrics
  - lm-head
  - causal-lm
  - text: I am
🤖 HuggingArtists Model 🤖
Bruce Springsteen

I was made with huggingartists.

Create your own bot based on your favorite artist with the demo!

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 Bruce Springsteen.

Dataset is available here. And can be used with:

from datasets import load_dataset

dataset = load_dataset("huggingartists/bruce-springsteen")

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 Bruce Springsteen'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.

How to use

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

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

Or with Transformers library:

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

model = AutoModelWithLMHead.from_pretrained("huggingartists/bruce-springsteen")

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.


Built by Aleksey Korshuk




For more details, visit the project repository.

GitHub stars