bruce-springsteen /
AlekseyKorshuk's picture
language: en
- huggingartists/bruce-springsteen
- huggingartists
- lyrics
- lm-head
- causal-lm
- text: "I am"
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style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;;)">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Bruce Springsteen</div>
<a href="">
<div style="text-align: center; font-size: 14px;">@bruce-springsteen</div>
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.
## About
*Built by Aleksey Korshuk*
For more details, visit the project repository.
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