language: en
datasets:
- huggingartists/mnogoznaal
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: I am
I was made with huggingartists.
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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 Mnogoznaal.
Dataset is available here. And can be used with:
from datasets import load_dataset
dataset = load_dataset("huggingartists/mnogoznaal")
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 Mnogoznaal'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/mnogoznaal')
generator("I am", num_return_sequences=5)
Or with Transformers library:
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/mnogoznaal")
model = AutoModelWithLMHead.from_pretrained("huggingartists/mnogoznaal")
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.
