<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/e7e76c378cb43b4b1ff03947d5c0481a.400x400x1.jpg')"> </div> </div> <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">Lady Gaga</div> <a href="https://genius.com/artists/lady-gaga"> <div style="text-align: center; font-size: 14px;">@lady-gaga</div> </a> </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 Lady Gaga.
Dataset is available here. And can be used with:
from datasets import load_dataset
dataset = load_dataset("huggingartists/lady-gaga")
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 Lady Gaga'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',
model='huggingartists/lady-gaga')
generator("I am", num_return_sequences=5)
Or with Transformers library:
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/lady-gaga")
model = AutoModelWithLMHead.from_pretrained("huggingartists/lady-gaga")
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.