I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Enigma.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/enigma).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/enigma")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/8bx90lw6/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Enigma's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1c1t20ji) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1c1t20ji/artifacts) is logged and versioned.


## How to use


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


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


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/enigma")

model = AutoModelWithLMHead.from_pretrained("huggingartists/enigma")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: enigma


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/enigma, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/miyagi
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

MiyaGi

@miyagi


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from MiyaGi.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/miyagi).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/miyagi")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1ai9l9x0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on MiyaGi's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1jowduev) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1jowduev/artifacts) is logged and versioned.


## How to use


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


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


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/miyagi")

model = AutoModelWithLMHead.from_pretrained("huggingartists/miyagi")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: miyagi


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/miyagi, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/doja-cat
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Doja Cat

@doja-cat


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Doja Cat.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/doja-cat).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/doja-cat")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1qxclk1g/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Doja Cat's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2lqvdntl) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2lqvdntl/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/doja-cat')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/doja-cat")

model = AutoModelWithLMHead.from_pretrained("huggingartists/doja-cat")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: doja-cat


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/doja-cat, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/ddt
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

DDT

@ddt


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from DDT.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/ddt).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/ddt")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2t9xnx5c/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on DDT's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/33zphjtk) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/33zphjtk/artifacts) is logged and versioned.


## How to use


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


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


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/ddt")

model = AutoModelWithLMHead.from_pretrained("huggingartists/ddt")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: ddt


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/ddt, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/5opka
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

5opka

@5opka


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from 5opka.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/5opka).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/5opka")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1o2s4fw8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on 5opka's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3vitposx) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3vitposx/artifacts) is logged and versioned.


## How to use


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


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


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/5opka")

model = AutoModelWithLMHead.from_pretrained("huggingartists/5opka")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: 5opka


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/5opka, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/gorillaz
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Gorillaz

@gorillaz


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Gorillaz.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/gorillaz).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/gorillaz")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3tuzza9u/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Gorillaz's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/12uilegj) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/12uilegj/artifacts) is logged and versioned.


## How to use


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


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


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/gorillaz")

model = AutoModelWithLMHead.from_pretrained("huggingartists/gorillaz")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: gorillaz


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/gorillaz, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/zemfira
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Земфира (Zemfira)

@zemfira


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Земфира (Zemfira).


Dataset is available [here](https://huggingface.co/datasets/huggingartists/zemfira).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/zemfira")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3hj4sma8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Земфира (Zemfira)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1v74giz2) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1v74giz2/artifacts) is logged and versioned.


## How to use


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


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


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/zemfira")

model = AutoModelWithLMHead.from_pretrained("huggingartists/zemfira")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: zemfira


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/zemfira, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/obladaet
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

OBLADAET

@obladaet


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from OBLADAET.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/obladaet).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/obladaet")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1mtsuuwr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on OBLADAET's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1s9epb35) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1s9epb35/artifacts) is logged and versioned.


## How to use


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


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


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/obladaet")

model = AutoModelWithLMHead.from_pretrained("huggingartists/obladaet")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: obladaet


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/obladaet, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/loud-luxury
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Loud Luxury

@loud-luxury


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Loud Luxury.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/loud-luxury).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/loud-luxury")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2a6kq74a/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Loud Luxury's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2l3op3mf) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2l3op3mf/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/loud-luxury')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/loud-luxury")

model = AutoModelWithLMHead.from_pretrained("huggingartists/loud-luxury")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: loud-luxury


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/loud-luxury, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/the-sugarcubes
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

The Sugarcubes

@the-sugarcubes


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from The Sugarcubes.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/the-sugarcubes).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/the-sugarcubes")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1zrlgv5f/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on The Sugarcubes's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/24shllae) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/24shllae/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/the-sugarcubes')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/the-sugarcubes")

model = AutoModelWithLMHead.from_pretrained("huggingartists/the-sugarcubes")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: the-sugarcubes


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/the-sugarcubes, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/florence-the-machine
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Florence + The Machine

@florence-the-machine


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Florence + The Machine.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/florence-the-machine).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/florence-the-machine")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/icjt5evm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Florence + The Machine's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1zfb9y24) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1zfb9y24/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/florence-the-machine')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/florence-the-machine")

model = AutoModelWithLMHead.from_pretrained("huggingartists/florence-the-machine")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: florence-the-machine


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/florence-the-machine, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/kishlak
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Кишлак (Kishlak)

@kishlak


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Кишлак (Kishlak).


Dataset is available [here](https://huggingface.co/datasets/huggingartists/kishlak).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/kishlak")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2654f8ic/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Кишлак (Kishlak)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/12gu37uv) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/12gu37uv/artifacts) is logged and versioned.


## How to use


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


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


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/kishlak")

model = AutoModelWithLMHead.from_pretrained("huggingartists/kishlak")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: kishlak


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/kishlak, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/chester-bennington
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Chester Bennington

@chester-bennington


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Chester Bennington.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/chester-bennington).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/chester-bennington")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3pq3bd6d/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Chester Bennington's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1sxpshrc) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1sxpshrc/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/chester-bennington')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/chester-bennington")

model = AutoModelWithLMHead.from_pretrained("huggingartists/chester-bennington")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: chester-bennington


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/chester-bennington, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/bones
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

BONES

@bones


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from BONES.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/bones).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/bones")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/26h7sojw/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on BONES's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1yr1mvc2) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1yr1mvc2/artifacts) is logged and versioned.


## How to use


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


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


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/bones")

model = AutoModelWithLMHead.from_pretrained("huggingartists/bones")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: bones


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/bones, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/agata-christie
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Агата Кристи (Agata Christie)

@agata-christie


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Агата Кристи (Agata Christie).


Dataset is available [here](https://huggingface.co/datasets/huggingartists/agata-christie).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/agata-christie")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1dtf6ia5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Агата Кристи (Agata Christie)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/q27fvz1h) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/q27fvz1h/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/agata-christie')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/agata-christie")

model = AutoModelWithLMHead.from_pretrained("huggingartists/agata-christie")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: agata-christie


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/agata-christie, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/yung-plague
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Yung Plague

@yung-plague


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Yung Plague.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/yung-plague).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/yung-plague")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/9hz73kye/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Yung Plague's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/28boe4q8) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/28boe4q8/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/yung-plague')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/yung-plague")

model = AutoModelWithLMHead.from_pretrained("huggingartists/yung-plague")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: yung-plague


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/yung-plague, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/tony-raut-and-garry-topor
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Тони Раут (Tony Raut) & Гарри Топор (Garry Topor)

@tony-raut-and-garry-topor


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Тони Раут (Tony Raut) & Гарри Топор (Garry Topor).


Dataset is available [here](https://huggingface.co/datasets/huggingartists/tony-raut-and-garry-topor).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/tony-raut-and-garry-topor")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/xnzxet17/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Тони Раут (Tony Raut) & Гарри Топор (Garry Topor)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/tfby1rj2) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/tfby1rj2/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/tony-raut-and-garry-topor')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/tony-raut-and-garry-topor")

model = AutoModelWithLMHead.from_pretrained("huggingartists/tony-raut-and-garry-topor")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: tony-raut-and-garry-topor


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/tony-raut-and-garry-topor, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/boris-grebenshikov
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Борис Гребенщиков (Boris Grebenshikov)

@boris-grebenshikov


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Борис Гребенщиков (Boris Grebenshikov).


Dataset is available [here](https://huggingface.co/datasets/huggingartists/boris-grebenshikov).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/boris-grebenshikov")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3nb43gls/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Борис Гребенщиков (Boris Grebenshikov)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/34p8ye7k) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/34p8ye7k/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/boris-grebenshikov')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/boris-grebenshikov")

model = AutoModelWithLMHead.from_pretrained("huggingartists/boris-grebenshikov")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: boris-grebenshikov


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/boris-grebenshikov, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/rammstein
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Rammstein

@rammstein


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Rammstein.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/rammstein).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/rammstein")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2c3w0gbx/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Rammstein's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/19sol366) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/19sol366/artifacts) is logged and versioned.


## How to use


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


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


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/rammstein")

model = AutoModelWithLMHead.from_pretrained("huggingartists/rammstein")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: rammstein


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/rammstein, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/melanie-martinez
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Melanie Martinez

@melanie-martinez


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Melanie Martinez.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/melanie-martinez).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/melanie-martinez")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/lb3ks0y5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Melanie Martinez's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2rvs9wvc) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2rvs9wvc/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/melanie-martinez')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/melanie-martinez")

model = AutoModelWithLMHead.from_pretrained("huggingartists/melanie-martinez")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: melanie-martinez


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/melanie-martinez, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/5nizza
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

5’Nizza

@5nizza


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from 5’Nizza.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/5nizza).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/5nizza")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1zcp1grf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on 5’Nizza's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2zg6pzw7) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2zg6pzw7/artifacts) is logged and versioned.


## How to use


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


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


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/5nizza")

model = AutoModelWithLMHead.from_pretrained("huggingartists/5nizza")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: 5nizza


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/5nizza, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/kasta
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Каста (Kasta)

@kasta


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Каста (Kasta).


Dataset is available [here](https://huggingface.co/datasets/huggingartists/kasta).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/kasta")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3k79xvbx/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Каста (Kasta)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1rphmch0) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1rphmch0/artifacts) is logged and versioned.


## How to use


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


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


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/kasta")

model = AutoModelWithLMHead.from_pretrained("huggingartists/kasta")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: kasta


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/kasta, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/og-buda
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

OG Buda

@og-buda


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from OG Buda.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/og-buda).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/og-buda")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2ic775kv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on OG Buda's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1g4193mx) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1g4193mx/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/og-buda')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/og-buda")

model = AutoModelWithLMHead.from_pretrained("huggingartists/og-buda")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: og-buda


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/og-buda, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/bushido-zho
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

BUSHIDO ZHO

@bushido-zho


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from BUSHIDO ZHO.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/bushido-zho).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/bushido-zho")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/vtfjc0qi/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on BUSHIDO ZHO's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/iwclgqsj) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/iwclgqsj/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/bushido-zho')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/bushido-zho")

model = AutoModelWithLMHead.from_pretrained("huggingartists/bushido-zho")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: bushido-zho


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/bushido-zho, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/baklan
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

BAKLAN

@baklan


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from BAKLAN.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/baklan).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/baklan")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2k5w5yhe/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on BAKLAN's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/28fvfef4) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/28fvfef4/artifacts) is logged and versioned.


## How to use


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


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


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/baklan")

model = AutoModelWithLMHead.from_pretrained("huggingartists/baklan")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: baklan


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/baklan, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/sum-41
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Sum 41

@sum-41


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Sum 41.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/sum-41).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/sum-41")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/18ckwm0s/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Sum 41's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1rj7iazy) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1rj7iazy/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/sum-41')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/sum-41")

model = AutoModelWithLMHead.from_pretrained("huggingartists/sum-41")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: sum-41


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/sum-41, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/till-lindemann
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Till Lindemann

@till-lindemann


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Till Lindemann.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/till-lindemann).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/till-lindemann")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2xh6fyqt/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Till Lindemann's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/32ohf092) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/32ohf092/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/till-lindemann')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/till-lindemann")

model = AutoModelWithLMHead.from_pretrained("huggingartists/till-lindemann")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: till-lindemann


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/till-lindemann, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/the-beatles
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

The Beatles

@the-beatles


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from The Beatles.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/the-beatles).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/the-beatles")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1twc71z8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on The Beatles's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3vbolrfq) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3vbolrfq/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/the-beatles')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/the-beatles")

model = AutoModelWithLMHead.from_pretrained("huggingartists/the-beatles")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: the-beatles


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/the-beatles, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/taylor-swift
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Taylor Swift

@taylor-swift


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Taylor Swift.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/taylor-swift).
And can be used with:


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/orx9pgcj/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Taylor Swift's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/13d0g1o4) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/13d0g1o4/artifacts) is logged and versioned.


## How to use


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


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


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/taylor-swift")

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


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: taylor-swift


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/taylor-swift, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/lil-nas-x
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Lil Nas X

@lil-nas-x


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Lil Nas X.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/lil-nas-x).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/lil-nas-x")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/n5s2tj7p/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Lil Nas X's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/334lnf7p) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/334lnf7p/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/lil-nas-x')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/lil-nas-x")

model = AutoModelWithLMHead.from_pretrained("huggingartists/lil-nas-x")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: lil-nas-x


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/lil-nas-x, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/vladimir-vysotsky
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Владимир Высоцкий (Vladimir Vysotsky)

@vladimir-vysotsky


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Владимир Высоцкий (Vladimir Vysotsky).


Dataset is available [here](https://huggingface.co/datasets/huggingartists/vladimir-vysotsky).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/vladimir-vysotsky")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1w1qc649/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Владимир Высоцкий (Vladimir Vysotsky)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1inrl5qe) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1inrl5qe/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/vladimir-vysotsky')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/vladimir-vysotsky")

model = AutoModelWithLMHead.from_pretrained("huggingartists/vladimir-vysotsky")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: vladimir-vysotsky


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/vladimir-vysotsky, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/death-grips
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Death Grips

@death-grips


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Death Grips.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/death-grips).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/death-grips")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1z4yxb78/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Death Grips's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/18v4x337) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/18v4x337/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/death-grips')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/death-grips")

model = AutoModelWithLMHead.from_pretrained("huggingartists/death-grips")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: death-grips


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/death-grips, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/ariana-grande
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Ariana Grande

@ariana-grande


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Ariana Grande.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/ariana-grande).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/ariana-grande")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2nfg7v7i/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Ariana Grande's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3u3sn1bx) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3u3sn1bx/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/ariana-grande')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/ariana-grande")

model = AutoModelWithLMHead.from_pretrained("huggingartists/ariana-grande")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: ariana-grande


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/ariana-grande, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/lady-gaga
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Lady Gaga

@lady-gaga


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Lady Gaga.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/lady-gaga).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/lady-gaga")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/s6109nue/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Lady Gaga's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3g2fq2s0) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3g2fq2s0/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/lady-gaga')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
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](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: lady-gaga


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/lady-gaga, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/duran-duran
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Duran Duran

@duran-duran


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Duran Duran.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/duran-duran).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/duran-duran")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/dy133fuf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Duran Duran's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/386u7cc3) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/386u7cc3/artifacts) is logged and versioned.


## How to use


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


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


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/duran-duran")

model = AutoModelWithLMHead.from_pretrained("huggingartists/duran-duran")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: duran-duran


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/duran-duran, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/dj-artem-artemov
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

DJ Artem Artemov

@dj-artem-artemov


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from DJ Artem Artemov.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/dj-artem-artemov).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/dj-artem-artemov")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2yaf9hon/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on DJ Artem Artemov's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/crwya5am) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/crwya5am/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/dj-artem-artemov')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/dj-artem-artemov")

model = AutoModelWithLMHead.from_pretrained("huggingartists/dj-artem-artemov")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


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.