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

🤖 HuggingArtists Model 🤖

Егор Летов (Egor Letov)

@egor-letov

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 Егор Летов (Egor Letov).

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/egor-letov")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/hya2wr54/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 Егор Летов (Egor Letov)'s lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2xmn5btx) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2xmn5btx/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/egor-letov')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/egor-letov")

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

## 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/aaron-watson
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

Aaron Watson

@aaron-watson

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 Aaron Watson.

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/aaron-watson")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/14ha1tnc/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 Aaron Watson's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/34e4zb2v) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/34e4zb2v/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/aaron-watson')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/aaron-watson")

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

## 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-velvet-underground
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

The Velvet Underground

@the-velvet-underground

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 Velvet Underground.

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/the-velvet-underground")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/lbkqy84q/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 Velvet Underground's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1e4s74q4) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1e4s74q4/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-velvet-underground')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

## 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/slava-marlow
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

SLAVA MARLOW

@slava-marlow

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 SLAVA MARLOW.

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/slava-marlow")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1fdcz1s5/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 SLAVA MARLOW's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/ro4q353s) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/ro4q353s/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/slava-marlow')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/slava-marlow")

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

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

🤖 HuggingArtists Model 🤖

pyrokinesis

@pyrokinesis

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 pyrokinesis.

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

```python
from datasets import load_dataset

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

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1s8696f3/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 pyrokinesis's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/22hm2utc) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/22hm2utc/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/pyrokinesis')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

## 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/red-hot-chili-peppers
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

Red Hot Chili Peppers

@red-hot-chili-peppers

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 Red Hot Chili Peppers.

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/red-hot-chili-peppers")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2spp06qm/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 Red Hot Chili Peppers's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/opiwx19q) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/opiwx19q/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/red-hot-chili-peppers')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/red-hot-chili-peppers")

model = AutoModelWithLMHead.from_pretrained("huggingartists/red-hot-chili-peppers")
```

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

🤖 HuggingArtists Model 🤖

Enya

@enya

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 Enya.

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

```python
from datasets import load_dataset

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

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/16cuy8yb/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 Enya's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/il8ldqo8) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/il8ldqo8/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/enya')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

## 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/tanzy-minus
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

Танцы Минус (Tanzy Minus)

@tanzy-minus

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 Танцы Минус (Tanzy Minus).

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/tanzy-minus")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/14vmwaxq/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 Танцы Минус (Tanzy Minus)'s lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/ru5wxieh) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/ru5wxieh/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/tanzy-minus')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/tanzy-minus")

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

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

🤖 HuggingArtists Model 🤖

Coldplay

@coldplay

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 Coldplay.

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

```python
from datasets import load_dataset

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

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/399heq03/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 Coldplay's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3o6fr9bq) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3o6fr9bq/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/coldplay')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

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

🤖 HuggingArtists Model 🤖

Kehlani

@kehlani

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 Kehlani.

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

```python
from datasets import load_dataset

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

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3t2b2m5y/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 Kehlani's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/35pweb11) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/35pweb11/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/kehlani')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

## 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/lazy-jay
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

Lazy Jay

@lazy-jay

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 Lazy Jay.

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/lazy-jay")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/tlb735a4/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 Lazy Jay's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/36z52xfj) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/36z52xfj/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/lazy-jay')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/lazy-jay")

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

## 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/bring-me-the-horizon
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

Bring Me The Horizon

@bring-me-the-horizon

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 Bring Me The Horizon.

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/bring-me-the-horizon")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1e9181i6/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 Bring Me The Horizon's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3p7pncir) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3p7pncir/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/bring-me-the-horizon')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/bring-me-the-horizon")

model = AutoModelWithLMHead.from_pretrained("huggingartists/bring-me-the-horizon")
```

## 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-notorious-big
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

The Notorious B.I.G.

@the-notorious-big

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 Notorious B.I.G..

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/the-notorious-big")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/wkvasju4/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 Notorious B.I.G.'s lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1coezuy2) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1coezuy2/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-notorious-big')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

## 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/lovv66
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

LOVV66

@lovv66

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 LOVV66.

Dataset is available [here](https://huggingface.co/datasets/huggingartists/lovv66).
And can be used with:

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/lovv66")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1t6a2fxs/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 LOVV66's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1de08pf6) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1de08pf6/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/lovv66')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/lovv66")

model = AutoModelWithLMHead.from_pretrained("huggingartists/lovv66")
```

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

🤖 HuggingArtists Model 🤖

HyunA (현아)

@hyuna

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 HyunA (현아).

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

```python
from datasets import load_dataset

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

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3uo94mxd/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 HyunA (현아)'s lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1o8t0mq0) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1o8t0mq0/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/hyuna')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

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

🤖 HuggingArtists Model 🤖

Tool

@tool

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 Tool.

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

```python
from datasets import load_dataset

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

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3ql2lz0c/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 Tool's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/vc1gg3ig) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/vc1gg3ig/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/tool')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

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

🤖 HuggingArtists Model 🤖

Джизус (Dzhizus)

@dzhizus

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 Джизус (Dzhizus).

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

```python
from datasets import load_dataset

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

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/35paacn1/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 Джизус (Dzhizus)'s lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1ug3yebo) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1ug3yebo/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/dzhizus')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

## 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/ed-sheeran
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

Ed Sheeran

@ed-sheeran

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 Ed Sheeran.

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/ed-sheeran")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3nju68bo/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 Ed Sheeran's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3hu7zc76) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3hu7zc76/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/ed-sheeran')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/ed-sheeran")

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

## 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/mating-ritual
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

Mating Ritual

@mating-ritual

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 Mating Ritual.

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/mating-ritual")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3cljintu/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 Mating Ritual's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/dv1g3x3b) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/dv1g3x3b/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/mating-ritual')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/mating-ritual")

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

## 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/led-zeppelin
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

Led Zeppelin

@led-zeppelin

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 Led Zeppelin.

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/led-zeppelin")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/170kpeuy/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 Led Zeppelin's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/39r70n5b) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/39r70n5b/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/led-zeppelin')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/led-zeppelin")

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

## 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/bryan-adams
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

Bryan Adams

@bryan-adams

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 Bryan Adams.

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/bryan-adams")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/22ksbpsz/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 Bryan Adams's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3b0c22fu) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3b0c22fu/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/bryan-adams')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/bryan-adams")

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

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

🤖 HuggingArtists Model 🤖

ROCKET

@rocket

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 ROCKET.

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

```python
from datasets import load_dataset

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

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3ceqmb05/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 ROCKET's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/37kckftd) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/37kckftd/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/rocket')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

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

🤖 HuggingArtists Model 🤖

Logic

@logic

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 Logic.

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

```python
from datasets import load_dataset

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

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2rp89nd3/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 Logic's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/25a9752b) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/25a9752b/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/logic')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

## 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/alan-walker
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

Alan Walker

@alan-walker

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 Alan Walker.

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/alan-walker")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3oyxxcos/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 Alan Walker's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/huoxll6m) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/huoxll6m/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/alan-walker')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/alan-walker")

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

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

🤖 HuggingArtists Model 🤖

Queen

@queen

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 Queen.

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

```python
from datasets import load_dataset

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

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1v5o4ijc/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 Queen's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2apdiv6y) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2apdiv6y/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/queen')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

## 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/kurt-cobain
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

Kurt Cobain

@kurt-cobain

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 Kurt Cobain.

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/kurt-cobain")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/tjfuj6tr/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 Kurt Cobain's lyrics.

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

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

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/kurt-cobain")

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

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

🤖 HuggingArtists Model 🤖

Eminem

@eminem

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 Eminem.

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

```python
from datasets import load_dataset

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

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/wqeebjm6/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 Eminem's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1r91z1u3) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1r91z1u3/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/eminem')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

## 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-69-eyes
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

The 69 Eyes

@the-69-eyes

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 69 Eyes.

Dataset is available [here](https://huggingface.co/datasets/huggingartists/the-69-eyes).
And can be used with:

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/the-69-eyes")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/26sibipb/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 69 Eyes's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1mjcdm16) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1mjcdm16/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-69-eyes')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/the-69-eyes")

model = AutoModelWithLMHead.from_pretrained("huggingartists/the-69-eyes")
```

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

🤖 HuggingArtists Model 🤖

Gunna

@gunna

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 Gunna.

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

```python
from datasets import load_dataset

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

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

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3c1xymw6) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3c1xymw6/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/gunna')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

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

🤖 HuggingArtists Model 🤖

Madonna

@madonna

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 Madonna.

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

```python
from datasets import load_dataset

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

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1zgkgxrp/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 Madonna's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1nejy753) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1nejy753/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/madonna')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

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

🤖 HuggingArtists Model 🤖

Tiamat

@tiamat

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 Tiamat.

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

```python
from datasets import load_dataset

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

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1tqzwb4a/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 Tiamat's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/ttkys3mq) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/ttkys3mq/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/tiamat')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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

## 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/system-of-a-down
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---

🤖 HuggingArtists Model 🤖

System of a Down

@system-of-a-down

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 System of a Down.

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

```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/system-of-a-down")
```

[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3m1sikv8/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 System of a Down's lyrics.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/wf3qe4yi) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/wf3qe4yi/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/system-of-a-down')
generator("I am", num_return_sequences=5)
```

Or with Transformers library:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/system-of-a-down")

model = AutoModelWithLMHead.from_pretrained("huggingartists/system-of-a-down")
```

## 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)