In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Boris Dayma*


For more details, visit the project repository.


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


model name: kapusaicin


model author: huggingtweets


task: text-generation


tags: pytorch, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/praisegodbarbon/1635047234116/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


🤖 AI BOT 🤖

Boston Psychology PhD

@praisegodbarbon


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.


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 Boston Psychology PhD.


| Data | Boston Psychology PhD |
| --- | --- |
| Tweets downloaded | 3212 |
| Retweets | 810 |
| Short tweets | 265 |
| Tweets kept | 2137 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/h4r5tyq8/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 @praisegodbarbon's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1o2225sd) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1o2225sd/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/praisegodbarbon')
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*


For more details, visit the project repository.


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


model name: praisegodbarbon


model author: huggingtweets


task: text-generation


tags: pytorch, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/harbogomps/1635628393154/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


🤖 AI BOT 🤖

🧛 Harbo Chomps 🧛

@harbogomps


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.


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 🧛 Harbo Chomps 🧛.


| Data | 🧛 Harbo Chomps 🧛 |
| --- | --- |
| Tweets downloaded | 515 |
| Retweets | 189 |
| Short tweets | 92 |
| Tweets kept | 234 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ao36t1el/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 @harbogomps's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3b5rtb6c) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3b5rtb6c/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/harbogomps')
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*


For more details, visit the project repository.


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


model name: harbogomps


model author: huggingtweets


task: text-generation


tags: pytorch, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/_nisagiss-dril-prezoh/1629931624717/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


🤖 AI CYBORG 🤖

Nisa 🇲🇽 & wint & prezoh

@_nisagiss-dril-prezoh


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.


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 Nisa 🇲🇽 & wint & prezoh.


| Data | Nisa 🇲🇽 | wint | prezoh |
| --- | --- | --- | --- |
| Tweets downloaded | 2987 | 3226 | 3250 |
| Retweets | 2556 | 479 | 37 |
| Short tweets | 155 | 312 | 940 |
| Tweets kept | 276 | 2435 | 2273 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3is5qgb7/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 @_nisagiss-dril-prezoh's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/8gs7ve4p) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/8gs7ve4p/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/_nisagiss-dril-prezoh')
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*


For more details, visit the project repository.


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


model name: _nisagiss-dril-prezoh


model author: huggingtweets


task: text-generation


tags: pytorch, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/glowdonk/1620242160895/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


GlowDonk 🤖 AI Bot

@glowdonk 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.


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 [@glowdonk's tweets](https://twitter.com/glowdonk).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3228 |
| Retweets | 190 |
| Short tweets | 761 |
| Tweets kept | 2277 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/sajyw4x6/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 @glowdonk's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/27srcmsx) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/27srcmsx/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/glowdonk')
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*


For more details, visit the project repository.


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


model name: glowdonk


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/jruizalt/1617777087795/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


Glavny Konstruktor 2.0 🛰🚀🏴🚩🏴☠🖖🏽 🤖 AI Bot

@jruizalt 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.


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 [@jruizalt's tweets](https://twitter.com/jruizalt).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3215 |
| Retweets | 1813 |
| Short tweets | 128 |
| Tweets kept | 1274 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/20z7lx0j/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 @jruizalt's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/39t1p2y2) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/39t1p2y2/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/jruizalt')
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*


For more details, visit the project repository.


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


model name: jruizalt


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/clarjon1/1617835463867/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


Clarjon1 🤖 AI Bot

@clarjon1 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.


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 [@clarjon1's tweets](https://twitter.com/clarjon1).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1366 |
| Retweets | 194 |
| Short tweets | 82 |
| Tweets kept | 1090 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/dkthawo1/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 @clarjon1's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1n12v8x8) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1n12v8x8/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/clarjon1')
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*


For more details, visit the project repository.


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


model name: clarjon1


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/legendarysoren/1614146572127/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


Nicøle Røsalyn Lørelai 🤖 AI Bot

@legendarysoren 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.


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 [@legendarysoren's tweets](https://twitter.com/legendarysoren).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3053 |
| Retweets | 1519 |
| Short tweets | 260 |
| Tweets kept | 1274 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ta1669u/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 @legendarysoren's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2p5at964) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2p5at964/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/legendarysoren')
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*


For more details, visit the project repository.


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


model name: legendarysoren


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/malleus_malefix/1614139090065/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


Malahidael 🤖 AI Bot

@malleus_malefix 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.


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 [@malleus_malefix's tweets](https://twitter.com/malleus_malefix).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 609 |
| Retweets | 294 |
| Short tweets | 124 |
| Tweets kept | 191 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/a1boc9ew/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 @malleus_malefix's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2o1a1por) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2o1a1por/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/malleus_malefix')
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*


For more details, visit the project repository.


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


model name: malleus_malefix


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/cnstnce_/1608361711919/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">


<style>
@media (prefers-color-scheme: dark) {
  .prose { color: #E2E8F0 !important; }
  .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>


<section class='prose'>


aalis na ko sa acc na to bye 🤖 AI Bot

@cnstnce_ 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.


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 [@cnstnce_'s tweets](https://twitter.com/cnstnce_).


Data
Quantity

Tweets downloaded
3056

Retweets
503

Short tweets
748

Tweets kept
1805


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/zzbcppb9/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 @cnstnce_'s tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/32cd3gw3) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/32cd3gw3/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/cnstnce_')
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*


</section>


For more details, visit the project repository.


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


model name: cnstnce_


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/visualizevalue/1601837796274/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">


<style>
@media (prefers-color-scheme: dark) {
  .prose { color: #E2E8F0 !important; }
  .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>


<section class='prose'>


Visualize Value 🤖 AI Bot

@visualizevalue 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.


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 [@visualizevalue's tweets](https://twitter.com/visualizevalue).


Data
Quantity

Tweets downloaded
1000

Retweets
132

Short tweets
331

Tweets kept
537


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/f2olvyds/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 @visualizevalue's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/1rm01ie6) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/1rm01ie6/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/visualizevalue')
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*


</section>


For more details, visit the project repository.


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


<!--- random size file                              -->


model name: visualizevalue


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:


- huggingtweets
widget:
- text: "My dream is"


Inca 🤖 AI Bot

@lainca_ 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.


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 [@lainca_'s tweets](https://twitter.com/lainca_).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 146 |
| Short tweets | 350 |
| Tweets kept | 2749 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2q8tlw4p/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 @lainca_'s tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/20lp49wa) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/20lp49wa/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/lainca_')
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*


For more details, visit the project repository.


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


model name: lainca_


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/claire_v0ltaire/1635393819410/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


🤖 AI BOT 🤖

Claire

@claire_v0ltaire


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.


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 Claire.


| Data | Claire |
| --- | --- |
| Tweets downloaded | 3236 |
| Retweets | 491 |
| Short tweets | 574 |
| Tweets kept | 2171 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/5yprh52r/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 @claire_v0ltaire's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/33jg2b88) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/33jg2b88/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/claire_v0ltaire')
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*


For more details, visit the project repository.


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


model name: claire_v0ltaire


model author: huggingtweets


task: text-generation


tags: pytorch, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:


- huggingtweets
widget:
- text: "My dream is"


🤖 AI BOT 🤖

Elon Musk

@elonmusk


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.


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 Elon Musk.


| Data | Elon Musk |
| --- | --- |
| Tweets downloaded | 1022 |
| Retweets | 43 |
| Short tweets | 323 |
| Tweets kept | 656 |


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


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ier347a) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ier347a/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/elonmusk')
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*


For more details, visit the project repository.


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


model name: elonmusk


model author: huggingtweets


task: text-generation


tags: pytorch, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/cloarecjulien/1609505793943/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">


<style>
@media (prefers-color-scheme: dark) {
  .prose { color: #E2E8F0 !important; }
  .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>


<section class='prose'>


Julien Cloarec 🤖 AI Bot

@cloarecjulien 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.


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 [@cloarecjulien's tweets](https://twitter.com/cloarecjulien).


Data
Quantity

Tweets downloaded
969

Retweets
543

Short tweets
163

Tweets kept
263


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/20w6mank/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 @cloarecjulien's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/n9t26gk2) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/n9t26gk2/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/cloarecjulien')
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*


</section>


For more details, visit the project repository.


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


model name: cloarecjulien


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, 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">


<style>
@media (prefers-color-scheme: dark) {
  .prose { color: #E2E8F0 !important; }
  .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>


<section class='prose'>


Jason Rosenfeld 🤖 AI Bot

@jrosenfeld13 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.


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 [@jrosenfeld13's tweets](https://twitter.com/jrosenfeld13).


Data
Quantity

Tweets downloaded
108

Retweets
13

Short tweets
7

Tweets kept
88


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/zll21jin/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 @jrosenfeld13's tweets.


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


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/emtjeqkx/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/jrosenfeld13')
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*


</section>


For more details, visit the project repository.


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


model name: jrosenfeld13


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/yujiri3/1616651697285/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


Yujiri (my likes don't always work) 🤖 AI Bot

@yujiri3 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.


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 [@yujiri3's tweets](https://twitter.com/yujiri3).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3182 |
| Retweets | 773 |
| Short tweets | 318 |
| Tweets kept | 2091 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/353ok7f3/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 @yujiri3's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/sqw7eetr) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/sqw7eetr/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/yujiri3')
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*


For more details, visit the project repository.


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


model name: yujiri3


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:


- huggingtweets
widget:
- text: "My dream is"


florence bacus 💜 🤖 AI Bot

@morallawwithin 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.


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 [@morallawwithin's tweets](https://twitter.com/morallawwithin).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3227 |
| Retweets | 666 |
| Short tweets | 491 |
| Tweets kept | 2070 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3hnxbkm1/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 @morallawwithin's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ue5m0yh) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ue5m0yh/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/morallawwithin')
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*


For more details, visit the project repository.


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


model name: morallawwithin


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: http://www.huggingtweets.com/dril-fart-horse_ebooks/1640224513212/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


🤖 AI CYBORG 🤖

wint & jon (PERSECUTED) & Horse ebooks

@dril-fart-horse_ebooks


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.


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 wint & jon (PERSECUTED) & Horse ebooks.


| Data | wint | jon (PERSECUTED) | Horse ebooks |
| --- | --- | --- | --- |
| Tweets downloaded | 3226 | 3217 | 3200 |
| Retweets | 477 | 571 | 0 |
| Short tweets | 306 | 558 | 421 |
| Tweets kept | 2443 | 2088 | 2779 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wf0ppmhi/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 @dril-fart-horse_ebooks's tweets.


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


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/unmddioo/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/dril-fart-horse_ebooks')
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*


For more details, visit the project repository.


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


model name: dril-fart-horse_ebooks


model author: huggingtweets


task: text-generation


tags: pytorch, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/endlessoffal/1617845283715/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


Great Value Abu Ghraib 🤖 AI Bot

@endlessoffal 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.


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 [@endlessoffal's tweets](https://twitter.com/endlessoffal).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 677 |
| Retweets | 354 |
| Short tweets | 104 |
| Tweets kept | 219 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3a9boxro/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 @endlessoffal's tweets.


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


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3mfqyzwf/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/endlessoffal')
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*


For more details, visit the project repository.


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


model name: endlessoffal


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/ravenn_diagram/1617708907194/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


Raven 🌸 🤖 AI Bot

@ravenn_diagram 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.


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 [@ravenn_diagram's tweets](https://twitter.com/ravenn_diagram).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 773 |
| Retweets | 19 |
| Short tweets | 82 |
| Tweets kept | 672 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/10g3alwn/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 @ravenn_diagram's tweets.


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


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2mjnirnn/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/ravenn_diagram')
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*


For more details, visit the project repository.


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


model name: ravenn_diagram


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/abdi_smokes/1618179038747/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


Mr. Dj💿 🤖 AI Bot

@abdi_smokes 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.


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 [@abdi_smokes's tweets](https://twitter.com/abdi_smokes).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3185 |
| Retweets | 172 |
| Short tweets | 790 |
| Tweets kept | 2223 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/33jkx1b1/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 @abdi_smokes's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/33g19os5) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/33g19os5/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/abdi_smokes')
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*


For more details, visit the project repository.


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


model name: abdi_smokes


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: http://res.cloudinary.com/huggingtweets/image/upload/v1599921389/joeddav.jpg
tags:


- huggingtweets
widget:
- text: "My dream is"


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


<style>
@media (prefers-color-scheme: dark) {
  .prose { color: #E2E8F0 !important; }
  .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>


<section class='prose'>


Joe Davison 🤖 AI Bot

@joeddav 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.


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 [@joeddav's tweets](https://twitter.com/joeddav).


Data
Quantity

Tweets downloaded
1557

Retweets
632

Short tweets
67

Tweets kept
858


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/2jn1y5u5/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 @joeddav's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/2bbyx49y) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/2bbyx49y/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/joeddav')
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*


</section>


For more details, visit the project repository.


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


model name: joeddav


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, 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">


<style>
@media (prefers-color-scheme: dark) {
  .prose { color: #E2E8F0 !important; }
  .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>


<section class='prose'>


Brandon Reeves 🤖 AI Bot

@brandonreeves08 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.


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 [@brandonreeves08's tweets](https://twitter.com/brandonreeves08).


Data
Quantity

Tweets downloaded
2233

Retweets
813

Short tweets
87

Tweets kept
1333


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/25xf6z75/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 @brandonreeves08's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/3cboi872) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/3cboi872/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/brandonreeves08')
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*


</section>


For more details, visit the project repository.


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


model name: brandonreeves08


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: http://www.huggingtweets.com/rotandgrow/1642563669658/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


🤖 AI BOT 🤖

☈ꪊϟ♰ሃ

@rotandgrow


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.


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 ☈ꪊϟ♰ሃ.


| Data | ☈ꪊϟ♰ሃ |
| --- | --- |
| Tweets downloaded | 3204 |
| Retweets | 261 |
| Short tweets | 742 |
| Tweets kept | 2201 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ja286qy/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 @rotandgrow's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2i16aoe5) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2i16aoe5/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/rotandgrow')
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*


For more details, visit the project repository.


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


model name: rotandgrow


model author: huggingtweets


task: text-generation


tags: pytorch, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/samkyle0/1614137112254/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


swaggy k 🤖 AI Bot

@samkyle0 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.


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 [@samkyle0's tweets](https://twitter.com/samkyle0).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2904 |
| Retweets | 2588 |
| Short tweets | 43 |
| Tweets kept | 273 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1a3ezx6n/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 @samkyle0's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/25f3wqjt) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/25f3wqjt/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/samkyle0')
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*


For more details, visit the project repository.


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


model name: samkyle0


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: http://www.huggingtweets.com/computerdefeat2/1640674811300/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


🤖 AI BOT 🤖

Merry Krismas🎄

@computerdefeat2


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.


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 Merry Krismas🎄.


| Data | Merry Krismas🎄 |
| --- | --- |
| Tweets downloaded | 3238 |
| Retweets | 827 |
| Short tweets | 671 |
| Tweets kept | 1740 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ut558an/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 @computerdefeat2's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/36nzt8vn) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/36nzt8vn/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/computerdefeat2')
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*


For more details, visit the project repository.


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


model name: computerdefeat2


model author: huggingtweets


task: text-generation


tags: pytorch, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/rterdogan/1614527111840/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


Recep Tayyip Erdoğan 🤖 AI Bot

@rterdogan 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.


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 [@rterdogan's tweets](https://twitter.com/rterdogan).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 540 |
| Short tweets | 221 |
| Tweets kept | 2488 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3i7hhtpy/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 @rterdogan's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1p07wu1v) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1p07wu1v/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/rterdogan')
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*


For more details, visit the project repository.


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


model name: rterdogan


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/flatironschool/1603341000640/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">


<style>
@media (prefers-color-scheme: dark) {
  .prose { color: #E2E8F0 !important; }
  .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>


<section class='prose'>


Flatiron School (at 🏡) 🤖 AI Bot

@flatironschool 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.


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 [@flatironschool's tweets](https://twitter.com/flatironschool).


Data
Quantity

Tweets downloaded
3202

Retweets
1068

Short tweets
582

Tweets kept
1552


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/179qzrny/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 @flatironschool's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/174rjbb8) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/174rjbb8/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/flatironschool')
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*


</section>


For more details, visit the project repository.


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


<!--- random size file                                 -->


model name: flatironschool


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:


- huggingtweets
widget:
- text: "My dream is"


Donald Hoffman 🤖 AI Bot

@donalddhoffman 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.


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 [@donalddhoffman's tweets](https://twitter.com/donalddhoffman).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 236 |
| Retweets | 11 |
| Short tweets | 45 |
| Tweets kept | 180 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1wzfrcs4/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 @donalddhoffman's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2zo2lld7) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2zo2lld7/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/donalddhoffman')
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*


For more details, visit the project repository.


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


model name: donalddhoffman


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/rct_ai/1627813451024/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


🤖 AI BOT 🤖

rct

@rct_ai


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.


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 rct.


| Data | rct |
| --- | --- |
| Tweets downloaded | 341 |
| Retweets | 12 |
| Short tweets | 3 |
| Tweets kept | 326 |


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


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/28z1r3gy) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/28z1r3gy/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/rct_ai')
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*


For more details, visit the project repository.


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


model name: rct_ai


model author: huggingtweets


task: text-generation


tags: pytorch, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/robber0540/1623620847015/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


🤖 AI BOT 🤖

Combat Ballerina

@robber0540


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.


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 Combat Ballerina.


| Data | Combat Ballerina |
| --- | --- |
| Tweets downloaded | 668 |
| Retweets | 65 |
| Short tweets | 303 |
| Tweets kept | 300 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2se37abl/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 @robber0540's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3i4yohh5) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3i4yohh5/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/robber0540')
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*


For more details, visit the project repository.


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


model name: robber0540


model author: huggingtweets


task: text-generation


tags: pytorch, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.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">


<style>
@media (prefers-color-scheme: dark) {
  .prose { color: #E2E8F0 !important; }
  .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>


<section class='prose'>


Edward Witten 🤖 AI Bot

@witten271 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.


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 [@witten271's tweets](https://twitter.com/witten271).


Data
Quantity

Tweets downloaded
1337

Retweets
761

Short tweets
32

Tweets kept
544


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/i5o4s13a/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 @witten271's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/35w4smrg) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/35w4smrg/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/witten271')
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*


</section>


For more details, visit the project repository.


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


model name: witten271


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/wolfejosh/1616623620980/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


Josh Wolfe 🤖 AI Bot

@wolfejosh 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.


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 [@wolfejosh's tweets](https://twitter.com/wolfejosh).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 838 |
| Short tweets | 374 |
| Tweets kept | 2033 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/121x3hw4/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 @wolfejosh's tweets.


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


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2poyfrja/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/wolfejosh')
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*


For more details, visit the project repository.


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


model name: wolfejosh


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/karchitecture/1613440346289/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


Christopher Parsons 🤖 AI Bot

@karchitecture 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.


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 [@karchitecture's tweets](https://twitter.com/karchitecture).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3209 |
| Retweets | 1496 |
| Short tweets | 37 |
| Tweets kept | 1676 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2t8ybhy5/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 @karchitecture's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/cosz0u1v) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/cosz0u1v/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/karchitecture')
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*


For more details, visit the project repository.


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


model name: karchitecture


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/urst0ff/1616685735707/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


death metal minivan 🤖 AI Bot

@urst0ff 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.


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 [@urst0ff's tweets](https://twitter.com/urst0ff).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1955 |
| Retweets | 75 |
| Short tweets | 151 |
| Tweets kept | 1729 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1u8zc1mk/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 @urst0ff's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/yg1cb4yl) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/yg1cb4yl/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/urst0ff')
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*


For more details, visit the project repository.


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


model name: urst0ff


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/yarbsalocin/1616647825805/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


Nicolas Bray 🤖 AI Bot

@yarbsalocin 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.


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 [@yarbsalocin's tweets](https://twitter.com/yarbsalocin).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3243 |
| Retweets | 135 |
| Short tweets | 190 |
| Tweets kept | 2918 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ps5uhut/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 @yarbsalocin's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/tsu7r7s9) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/tsu7r7s9/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/yarbsalocin')
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*


For more details, visit the project repository.


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


model name: yarbsalocin


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/ugh_lily/1614210833411/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


lillillil 🤖 AI Bot

@ugh_lily 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.


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 [@ugh_lily's tweets](https://twitter.com/ugh_lily).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3177 |
| Retweets | 673 |
| Short tweets | 418 |
| Tweets kept | 2086 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/12myglx7/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 @ugh_lily's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1dzbo8k1) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1dzbo8k1/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/ugh_lily')
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*


For more details, visit the project repository.


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


model name: ugh_lily


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/washed_u/1616771910952/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


honcho⛰🌵🦎 🤖 AI Bot

@washed_u 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.


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 [@washed_u's tweets](https://twitter.com/washed_u).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1250 |
| Retweets | 102 |
| Short tweets | 124 |
| Tweets kept | 1024 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/qt0xkbfn/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 @washed_u's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3sm2zrqc) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3sm2zrqc/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/washed_u')
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*


For more details, visit the project repository.


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


model name: washed_u


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:


- huggingtweets
widget:
- text: "My dream is"


The SCP Foundation 🤖 AI Bot

@scpwiki 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.


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 [@scpwiki's tweets](https://twitter.com/scpwiki).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3219 |
| Retweets | 385 |
| Short tweets | 302 |
| Tweets kept | 2532 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2kz7gdc3/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 @scpwiki's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/17pdq2uc) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/17pdq2uc/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/scpwiki')
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*


For more details, visit the project repository.


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


model name: scpwiki


model author: huggingtweets


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, transformers, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/nolemonnomelon/1616619611154/predictions.png
tags:


- huggingtweets
widget:
- text: "My dream is"


zest of lemon stove 🤖 AI Bot

@nolemonnomelon 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.


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 [@nolemonnomelon's tweets](https://twitter.com/nolemonnomelon).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3242 |
| Retweets | 103 |
| Short tweets | 342 |
| Tweets kept | 2797 |