---
language: en
thumbnail: https://www.huggingtweets.com/detseretninu-dumbricardo-illuminusnumb/1629841756956/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

🤖 AI CYBORG 🤖

sad rico & follow me only if you're sad & ...

@detseretninu-dumbricardo-illuminusnumb

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on tweets from sad rico & follow me only if you're sad & ....

| Data | sad rico | follow me only if you're sad | ... |
| --- | --- | --- | --- |
| Tweets downloaded | 768 | 3233 | 677 |
| Retweets | 0 | 167 | 1 |
| Short tweets | 102 | 755 | 285 |
| Tweets kept | 666 | 2311 | 391 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/l42hthlz/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 @detseretninu-dumbricardo-illuminusnumb's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/c1hyp8lf) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/c1hyp8lf/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='huggingtweets/detseretninu-dumbricardo-illuminusnumb')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/joelstc/1616682513195/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

joël🦥 🤖 AI Bot

@joelstc bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@joelstc's tweets](https://twitter.com/joelstc).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1196 |
| Retweets | 213 |
| Short tweets | 117 |
| Tweets kept | 866 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2kiypyjs/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 @joelstc's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/x9nyrpnv) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/x9nyrpnv/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='huggingtweets/joelstc')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/richardbspencer/1612629366200/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">

Richard 🌞 Spencer 🤖 AI Bot

@richardbspencer bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@richardbspencer's tweets](https://twitter.com/richardbspencer).

Data
Quantity

Tweets downloaded
3106

Retweets
923

Short tweets
155

Tweets kept
2028

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/36ugp0to/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 @richardbspencer's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/7ti5f0ny) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/7ti5f0ny/artifacts) is logged and versioned.

## Intended uses & limitations

### How to use

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

from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/richardbspencer')
generator("My dream is", num_return_sequences=5)

### 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: http://res.cloudinary.com/huggingtweets/image/upload/v1599953574/sagefuncom.jpg
tags:
- huggingtweets
widget:
- text: "My dream is"
---

<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">

SFC 🤖 AI Bot

@sagefuncom bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@sagefuncom's tweets](https://twitter.com/sagefuncom).

Data
Quantity

Tweets downloaded
505

Retweets
27

Short tweets
15

Tweets kept
463

[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/2ght0hes/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 @sagefuncom's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/2ug8tsat) for full transparency and reproducibility.

At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/2ug8tsat/artifacts) is logged and versioned.

## Intended uses & limitations

### How to use

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

from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/sagefuncom')
generator("My dream is", num_return_sequences=5)

### 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/elmo_oxygen/1617790228158/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

Elmo 🤖 AI Bot

@elmo_oxygen bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@elmo_oxygen's tweets](https://twitter.com/elmo_oxygen).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3240 |
| Retweets | 69 |
| Short tweets | 390 |
| Tweets kept | 2781 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ltnqbnk5/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 @elmo_oxygen's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1gigw5nn) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1gigw5nn/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='huggingtweets/elmo_oxygen')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/lets4r/1618512728298/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

Romain Rastel 💙 🤖 AI Bot

@lets4r bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@lets4r's tweets](https://twitter.com/lets4r).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 951 |
| Short tweets | 208 |
| Tweets kept | 2088 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3vvyl04g/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 @lets4r's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ra681rz) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ra681rz/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='huggingtweets/lets4r')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/rwphan/1616747587434/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

Rich 潘伟山 🤖 AI Bot

@rwphan bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@rwphan's tweets](https://twitter.com/rwphan).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3233 |
| Retweets | 1437 |
| Short tweets | 108 |
| Tweets kept | 1688 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/18hagr22/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 @rwphan's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/23juio2i) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/23juio2i/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='huggingtweets/rwphan')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---

Stephen Curry 🤖 AI Bot

@stephencurry30 bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@stephencurry30's tweets](https://twitter.com/stephencurry30).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3191 |
| Retweets | 359 |
| Short tweets | 703 |
| Tweets kept | 2129 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3rk7yd66/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 @stephencurry30's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/209zunsz) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/209zunsz/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='huggingtweets/stephencurry30')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/johnowhitaker/1628678191103/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

🤖 AI BOT 🤖

Jonathan Whitaker

@johnowhitaker

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on tweets from Jonathan Whitaker.

| Data | Jonathan Whitaker |
| --- | --- |
| Tweets downloaded | 508 |
| Retweets | 45 |
| Short tweets | 13 |
| Tweets kept | 450 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2iuk80nc/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 @johnowhitaker's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2xsei074) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2xsei074/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='huggingtweets/johnowhitaker')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/anoushnajarian/1604082481494/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">

Anoush #JusticeforBreonnaTaylor 🤖 AI Bot

@anoushnajarian bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@anoushnajarian's tweets](https://twitter.com/anoushnajarian).

Data
Quantity

Tweets downloaded
3208

Retweets
2926

Short tweets
119

Tweets kept
163

[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/32edy0zq/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 @anoushnajarian's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/47coh41e) for full transparency and reproducibility.

At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/47coh41e/artifacts) is logged and versioned.

## Intended uses & limitations

### How to use

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

from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/anoushnajarian')
generator("My dream is", num_return_sequences=5)

### 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/buildwithcycy/1603391285588/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">

Cynthia Habonimana 🤖 AI Bot

@buildwithcycy bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@buildwithcycy's tweets](https://twitter.com/buildwithcycy).

Data
Quantity

Tweets downloaded
680

Retweets
181

Short tweets
80

Tweets kept
419

[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/woos7f0i/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 @buildwithcycy's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/39i5bi0o) for full transparency and reproducibility.

At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/39i5bi0o/artifacts) is logged and versioned.

## Intended uses & limitations

### How to use

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

from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/buildwithcycy')
generator("My dream is", num_return_sequences=5)

### 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/fullbitchschol1/1616889911749/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

Fullbitchscholar 🤖 AI Bot

@fullbitchschol1 bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@fullbitchschol1's tweets](https://twitter.com/fullbitchschol1).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3248 |
| Retweets | 20 |
| Short tweets | 224 |
| Tweets kept | 3004 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1em7u8my/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 @fullbitchschol1's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2u9ua2kl) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2u9ua2kl/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='huggingtweets/fullbitchschol1')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/charlottefare/1617808412743/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

Charlotte Fare 🤖 AI Bot

@charlottefare bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@charlottefare's tweets](https://twitter.com/charlottefare).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3163 |
| Retweets | 1889 |
| Short tweets | 141 |
| Tweets kept | 1133 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/cilpmqxj/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 @charlottefare's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1qxbfgd4) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1qxbfgd4/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='huggingtweets/charlottefare')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/chalklings/1617810524934/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

Niel ⚙ 🤖 AI Bot

@chalklings bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@chalklings's tweets](https://twitter.com/chalklings).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3073 |
| Retweets | 1146 |
| Short tweets | 250 |
| Tweets kept | 1677 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2sqd8rq6/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 @chalklings's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/s0popiel) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/s0popiel/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='huggingtweets/chalklings')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/enilox-madacol-ricardocalleja/1620512214792/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

🤖 AI CYBORG 🤖

Ricardo Calleja & Marco D'Agostini & Eliecer Aldana

@enilox-madacol-ricardocalleja

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on tweets from Ricardo Calleja & Marco D'Agostini & Eliecer Aldana.

| Data | Ricardo Calleja | Marco D'Agostini | Eliecer Aldana |
| --- | --- | --- | --- |
| Tweets downloaded | 396 | 3209 | 884 |
| Retweets | 213 | 1970 | 622 |
| Short tweets | 32 | 244 | 45 |
| Tweets kept | 151 | 995 | 217 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1keiiwwy/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 @enilox-madacol-ricardocalleja's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1hem46kg) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1hem46kg/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='huggingtweets/enilox-madacol-ricardocalleja')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/cheascake/1617656786247/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

Eel Enthusiast 🤖 AI Bot

@cheascake bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@cheascake's tweets](https://twitter.com/cheascake).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3248 |
| Retweets | 216 |
| Short tweets | 732 |
| Tweets kept | 2300 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1pgthrar/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 @cheascake's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ndb8e5s3) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ndb8e5s3/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='huggingtweets/cheascake')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/strappedtrap/1614193505363/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

Have Belt Will Battle 🤖 AI Bot

@strappedtrap bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@strappedtrap's tweets](https://twitter.com/strappedtrap).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3154 |
| Retweets | 1275 |
| Short tweets | 293 |
| Tweets kept | 1586 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/31gv6sdf/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 @strappedtrap's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/vmj8j69e) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/vmj8j69e/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='huggingtweets/strappedtrap')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/grapefried/1626857673378/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

🤖 AI BOT 🤖

ju1ce💎

@grapefried

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on tweets from ju1ce💎.

| Data | ju1ce💎 |
| --- | --- |
| Tweets downloaded | 2034 |
| Retweets | 504 |
| Short tweets | 403 |
| Tweets kept | 1127 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1actx5cl/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 @grapefried's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1a1nwhd0) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1a1nwhd0/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='huggingtweets/grapefried')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/wiifactsplus/1614218679072/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

Wii Facts Plus 🤖 AI Bot

@wiifactsplus bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@wiifactsplus's tweets](https://twitter.com/wiifactsplus).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 153 |
| Retweets | 5 |
| Short tweets | 0 |
| Tweets kept | 148 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/19wweb3v/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 @wiifactsplus's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/28sh6nv9) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/28sh6nv9/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='huggingtweets/wiifactsplus')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/raholaoficial/1600766375243/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">

Pilar Rahola 🤖 AI Bot

@raholaoficial bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@raholaoficial's tweets](https://twitter.com/raholaoficial).

Data
Quantity

Tweets downloaded
3209

Retweets
2179

Short tweets
255

Tweets kept
775

[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/1bb9slz6/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 @raholaoficial's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/2je4n0p6) for full transparency and reproducibility.

At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/2je4n0p6/artifacts) is logged and versioned.

## Intended uses & limitations

### How to use

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

from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/raholaoficial')
generator("My dream is", num_return_sequences=5)

### 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/theofficetv/1631662381899/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

🤖 AI BOT 🤖

The Office on Peacock

@theofficetv

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on tweets from The Office on Peacock.

| Data | The Office on Peacock |
| --- | --- |
| Tweets downloaded | 3215 |
| Retweets | 459 |
| Short tweets | 592 |
| Tweets kept | 2164 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3dwxnzp9/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 @theofficetv's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1mnr0e28) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1mnr0e28/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='huggingtweets/theofficetv')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/_deep_winter_/1615614034490/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

erin?? 🤖 AI Bot

@_deep_winter_ bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@_deep_winter_'s tweets](https://twitter.com/_deep_winter_).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3231 |
| Retweets | 533 |
| Short tweets | 324 |
| Tweets kept | 2374 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/17lau6du/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 @_deep_winter_'s tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1gj4ousy) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1gj4ousy/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='huggingtweets/_deep_winter_')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/eddyburback/1614125872436/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

Eddy Burback 🤖 AI Bot

@eddyburback bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@eddyburback's tweets](https://twitter.com/eddyburback).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3236 |
| Retweets | 87 |
| Short tweets | 565 |
| Tweets kept | 2584 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/18u1qnb6/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 @eddyburback's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/5s7mt5k1) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/5s7mt5k1/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='huggingtweets/eddyburback')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/javiballester4/1616630748333/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

Javi Ballester 🤖 AI Bot

@javiballester4 bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@javiballester4's tweets](https://twitter.com/javiballester4).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 369 |
| Retweets | 4 |
| Short tweets | 108 |
| Tweets kept | 257 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/33xklndf/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 @javiballester4's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/s6kbzp61) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/s6kbzp61/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='huggingtweets/javiballester4')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/imcummingonline/1617770513198/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

Ace 🤖 AI Bot

@imcummingonline bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@imcummingonline's tweets](https://twitter.com/imcummingonline).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 914 |
| Retweets | 88 |
| Short tweets | 218 |
| Tweets kept | 608 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2yh36yxx/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 @imcummingonline's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3nnnr0u8) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3nnnr0u8/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='huggingtweets/imcummingonline')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/slainkinsman/1617812785653/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

Sඞlfish Dying Relative 🤖 AI Bot

@slainkinsman bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@slainkinsman's tweets](https://twitter.com/slainkinsman).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3205 |
| Retweets | 2771 |
| Short tweets | 27 |
| Tweets kept | 407 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/z5f80l0r/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 @slainkinsman's tweets.

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

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/qforafva/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='huggingtweets/slainkinsman')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/nerv_emma/1617760555385/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

nerv ✝⚙🏳⚧ 🤖 AI Bot

@nerv_emma bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@nerv_emma's tweets](https://twitter.com/nerv_emma).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3228 |
| Retweets | 348 |
| Short tweets | 1089 |
| Tweets kept | 1791 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2hux32zr/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 @nerv_emma's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1x8aiphn) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1x8aiphn/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='huggingtweets/nerv_emma')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/pastellexists/1624493429168/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

🤖 AI BOT 🤖

pastell

@pastellexists

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on tweets from pastell.

| Data | pastell |
| --- | --- |
| Tweets downloaded | 3210 |
| Retweets | 732 |
| Short tweets | 91 |
| Tweets kept | 2387 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/5lqxaa5l/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 @pastellexists's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2y0xb5js) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2y0xb5js/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='huggingtweets/pastellexists')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo_share.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---

<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">

Rick and Morty 🤖 AI Bot

@rickandmorty bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://bit.ly/2TGXMZf).

## Training data

The model was trained on [@rickandmorty's tweets](https://twitter.com/rickandmorty).

Data
Quantity

Tweets downloaded
3174

Retweets
1310

Short tweets
584

Tweets kept
1280

[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/1wl9er29/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 @rickandmorty's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/3ioo8pyf) for full transparency and reproducibility.

At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/3ioo8pyf/artifacts) is logged and versioned.

## Intended uses & limitations

### How to use

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

from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/rickandmorty')
generator("My dream is", num_return_sequences=5)

### 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/noamchompers/1623096801492/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

🤖 AI BOT 🤖

it is antisemitic to call me ‘noam chumpers’

@noamchompers

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on tweets from it is antisemitic to call me ‘noam chumpers’.

| Data | it is antisemitic to call me ‘noam chumpers’ |
| --- | --- |
| Tweets downloaded | 3242 |
| Retweets | 450 |
| Short tweets | 371 |
| Tweets kept | 2421 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3vudk4dq/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 @noamchompers's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1o35nfpi) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1o35nfpi/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='huggingtweets/noamchompers')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/kennethlpearce/1616723425755/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

Kenny Pearce 🤖 AI Bot

@kennethlpearce bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@kennethlpearce's tweets](https://twitter.com/kennethlpearce).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 362 |
| Retweets | 32 |
| Short tweets | 7 |
| Tweets kept | 323 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ht0bj53j/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 @kennethlpearce's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/j5gwbymt) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/j5gwbymt/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='huggingtweets/kennethlpearce')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/ronnienumber7/1627061284718/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

🤖 AI BOT 🤖

Ronnie (the original Ronnie)

@ronnienumber7

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on tweets from Ronnie (the original Ronnie).

| Data | Ronnie (the original Ronnie) |
| --- | --- |
| Tweets downloaded | 559 |
| Retweets | 11 |
| Short tweets | 35 |
| Tweets kept | 513 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/z46j8b3c/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 @ronnienumber7's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1n9lz23f) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1n9lz23f/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='huggingtweets/ronnienumber7')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: http://www.huggingtweets.com/bouncemanautumn/1644093304436/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

🤖 AI BOT 🤖

autumn wants to hold ty’s hand

@bouncemanautumn

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on tweets from autumn wants to hold ty’s hand.

| Data | autumn wants to hold ty’s hand |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 195 |
| Short tweets | 434 |
| Tweets kept | 2616 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/16mq5may/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 @bouncemanautumn's tweets.

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

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3vlqrfex/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='huggingtweets/bouncemanautumn')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/charmin-claireredacted/1627080262136/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

🤖 AI CYBORG 🤖

Charmin & Claire

@charmin-claireredacted

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on tweets from Charmin & Claire.

| Data | Charmin | Claire |
| --- | --- | --- |
| Tweets downloaded | 3250 | 3241 |
| Retweets | 22 | 523 |
| Short tweets | 129 | 627 |
| Tweets kept | 3099 | 2091 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/rtv7eufi/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 @charmin-claireredacted's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1rw1se40) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1rw1se40/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='huggingtweets/charmin-claireredacted')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/pico8degalaleo/1616718900414/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

Dragon quest enjoyer 🤖 AI Bot

@pico8degalaleo bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@pico8degalaleo's tweets](https://twitter.com/pico8degalaleo).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 717 |
| Retweets | 313 |
| Short tweets | 53 |
| Tweets kept | 351 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/21u2c87d/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 @pico8degalaleo's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/29rz4faz) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/29rz4faz/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='huggingtweets/pico8degalaleo')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/hbloodedheroine/1617913229876/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

✦HotBloodedHeroine✦ @ PSO2 🤖 AI Bot

@hbloodedheroine bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@hbloodedheroine's tweets](https://twitter.com/hbloodedheroine).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3238 |
| Retweets | 406 |
| Short tweets | 1019 |
| Tweets kept | 1813 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/4tvl495z/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 @hbloodedheroine's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/gx30ahu9) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/gx30ahu9/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='huggingtweets/hbloodedheroine')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/harishkgarg/1625312937710/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

🤖 AI BOT 🤖

Harish Garg

@harishkgarg

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on tweets from Harish Garg.

| Data | Harish Garg |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 328 |
| Short tweets | 366 |
| Tweets kept | 2552 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2tl8dz55/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 @harishkgarg's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3853sjv0) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3853sjv0/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='huggingtweets/harishkgarg')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/musicalmushr00m/1625122113002/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

🤖 AI BOT 🤖

mushr00m

@musicalmushr00m

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on tweets from mushr00m.

| Data | mushr00m |
| --- | --- |
| Tweets downloaded | 161 |
| Retweets | 50 |
| Short tweets | 33 |
| Tweets kept | 78 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/26j7t29j/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 @musicalmushr00m's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/lxo37ttz) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/lxo37ttz/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='huggingtweets/musicalmushr00m')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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


---
language: en
thumbnail: https://www.huggingtweets.com/anotherpattern/1616768869837/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---

Another 🤖 AI Bot

@anotherpattern bot

I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).

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

## How does it work?

The model uses the following pipeline.

![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).

## Training data

The model was trained on [@anotherpattern's tweets](https://twitter.com/anotherpattern).

| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1137 |
| Retweets | 2 |
| Short tweets | 147 |
| Tweets kept | 988 |

[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3lb52jwv/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 @anotherpattern's tweets.

Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/33p25f4f) for full transparency and reproducibility.

At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/33p25f4f/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='huggingtweets/anotherpattern')
generator("My dream is", num_return_sequences=5)
```

## 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 Boris Dayma*

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

For more details, visit the project repository.

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