---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
language: en
datasets:
- huggingartists/kanye-west
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

Kanye West

@kanye-west

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 Kanye West.

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/kanye-west")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1nriijf4/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 Kanye West's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2qxzz0h9) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2qxzz0h9/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/kanye-west')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/kanye-west")

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

## 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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
language: en
datasets:
- huggingartists/linkin-park
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

Linkin Park

@linkin-park

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 Linkin Park.

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/linkin-park")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3mtr0u4z/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 Linkin Park's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/fxn4brd6) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/fxn4brd6/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/linkin-park')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/linkin-park")

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

## 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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
language: en
datasets:
- huggingartists/billy-talent
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

Billy Talent

@billy-talent

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 Billy Talent.

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/billy-talent")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/37amfbe8/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 Billy Talent's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/pyw6tj9v) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/pyw6tj9v/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/billy-talent')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/billy-talent")

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

## 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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
language: en
datasets:
- huggingartists/25-17
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

25/17

@25-17

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 25/17.

Dataset is available [here](https://huggingface.co/datasets/huggingartists/25-17).
And can be used with:

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/25-17")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1iuytbjp/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 25/17's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/knv4l4gw) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/knv4l4gw/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/25-17')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/25-17")

model = AutoModelWithLMHead.from_pretrained("huggingartists/25-17")
```

## 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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
language: en
datasets:
- huggingartists/bob-dylan
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

Bob Dylan

@bob-dylan

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 Bob Dylan.

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/bob-dylan")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/12pi332s/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 Bob Dylan's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/34f7xa4s) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/34f7xa4s/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/bob-dylan')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/bob-dylan")

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

## 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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
language: en
datasets:
- huggingartists/imagine-dragons
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

Imagine Dragons

@imagine-dragons

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 Imagine Dragons.

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/imagine-dragons")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/dln6ixis/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 Imagine Dragons's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3cj3c8z1) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3cj3c8z1/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/imagine-dragons')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/imagine-dragons")

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

## 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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
language: en
datasets:
- huggingartists/shadowraze
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

shadowraze

@shadowraze

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 shadowraze.

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

```python
from datasets import load_dataset

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

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/pkbkflsq/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 shadowraze's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/tiu2mjo1) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/tiu2mjo1/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/shadowraze')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

## 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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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


---
language: en
datasets:
- huggingartists/bladee
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

Bladee

@bladee

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 Bladee.

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

```python
from datasets import load_dataset

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

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/326nmhkf/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 Bladee's lyrics.

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

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/28bmutxl/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/bladee')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

## 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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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---
language: en
datasets:
- huggingartists/mashina-vremeni
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

Машина Времени (Mashina Vremeni)

@mashina-vremeni

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 Машина Времени (Mashina Vremeni).

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/mashina-vremeni")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3r1yxrx7/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 Машина Времени (Mashina Vremeni)'s lyrics.

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

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1cgaltpc/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/mashina-vremeni')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/mashina-vremeni")

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

## 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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

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For more details, visit the project repository.

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


---
language: en
datasets:
- huggingartists/sqwore
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

Sqwore

@sqwore

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 Sqwore.

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

```python
from datasets import load_dataset

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

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3gzd5crq/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 Sqwore's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/vzeft23g) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/vzeft23g/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/sqwore')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

## 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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

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For more details, visit the project repository.

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


---
language: en
datasets:
- huggingartists/muse
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

Muse

@muse

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 Muse.

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

```python
from datasets import load_dataset

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

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3w58rwod/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 Muse's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3j03atcr) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3j03atcr/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/muse')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

## 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*

[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)

[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)

[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)

For more details, visit the project repository.

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