Data
Quantity

Tweets downloaded
3174

Retweets
1310

Short tweets
584

Tweets kept
1280


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/1wl9er29/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


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


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


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


## Intended uses & limitations


### How to use


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


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


### Limitations and bias


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


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


## About


*Built by Boris Dayma*


</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: rickandmorty


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/noamchompers/1623096801492/predictions.png
tags:


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


🤖 AI BOT 🤖

it is antisemitic to call me ‘noam chumpers’

@noamchompers


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


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


## How does it work?


The model uses the following pipeline.


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


## Training data


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


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


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3vudk4dq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


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


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


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1o35nfpi/artifacts) is logged and versioned.


## How to use


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


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


## Limitations and bias


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


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


## About


*Built by Boris Dayma*


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: noamchompers


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/kennethlpearce/1616723425755/predictions.png
tags:


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


Kenny Pearce 🤖 AI Bot

@kennethlpearce bot


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


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


## How does it work?


The model uses the following pipeline.


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


## Training data


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


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


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ht0bj53j/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


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


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


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/j5gwbymt/artifacts) is logged and versioned.


## How to use


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


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


## Limitations and bias


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


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


## About


*Built by Boris Dayma*


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: kennethlpearce


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/ronnienumber7/1627061284718/predictions.png
tags:


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


🤖 AI BOT 🤖

Ronnie (the original Ronnie)

@ronnienumber7


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


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


## How does it work?


The model uses the following pipeline.


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


## Training data


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


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


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/z46j8b3c/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ronnienumber7's tweets.


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


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1n9lz23f/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/ronnienumber7')
generator("My dream is", num_return_sequences=5)
```


## Limitations and bias


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


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


## About


*Built by Boris Dayma*


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: ronnienumber7


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: http://www.huggingtweets.com/bouncemanautumn/1644093304436/predictions.png
tags:


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


🤖 AI BOT 🤖

autumn wants to hold ty’s hand

@bouncemanautumn


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


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


## How does it work?


The model uses the following pipeline.


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


## Training data


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


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


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/16mq5may/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


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


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


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


## How to use


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


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


## Limitations and bias


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


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


## About


*Built by Boris Dayma*


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: bouncemanautumn


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/charmin-claireredacted/1627080262136/predictions.png
tags:


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


🤖 AI CYBORG 🤖

Charmin & Claire

@charmin-claireredacted


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


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


## How does it work?


The model uses the following pipeline.


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


## Training data


The model was trained on tweets from Charmin & Claire.


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


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/rtv7eufi/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


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


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


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1rw1se40/artifacts) is logged and versioned.


## How to use


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


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


## Limitations and bias


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


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


## About


*Built by Boris Dayma*


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: charmin-claireredacted


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/pico8degalaleo/1616718900414/predictions.png
tags:


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


Dragon quest enjoyer 🤖 AI Bot

@pico8degalaleo bot


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


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


## How does it work?


The model uses the following pipeline.


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


## Training data


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


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


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/21u2c87d/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @pico8degalaleo's tweets.


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


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/29rz4faz/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/pico8degalaleo')
generator("My dream is", num_return_sequences=5)
```


## Limitations and bias


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


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


## About


*Built by Boris Dayma*


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: pico8degalaleo


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/hbloodedheroine/1617913229876/predictions.png
tags:


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


✦HotBloodedHeroine✦ @ PSO2 🤖 AI Bot

@hbloodedheroine bot


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


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


## How does it work?


The model uses the following pipeline.


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


## Training data


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


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


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/4tvl495z/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


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


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


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/gx30ahu9/artifacts) is logged and versioned.


## How to use


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


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


## Limitations and bias


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


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


## About


*Built by Boris Dayma*


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: hbloodedheroine


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/harishkgarg/1625312937710/predictions.png
tags:


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


🤖 AI BOT 🤖

Harish Garg

@harishkgarg


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


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


## How does it work?


The model uses the following pipeline.


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


## Training data


The model was trained on tweets from Harish Garg.


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


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2tl8dz55/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


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


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


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3853sjv0/artifacts) is logged and versioned.


## How to use


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


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


## Limitations and bias


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


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


## About


*Built by Boris Dayma*


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: harishkgarg


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/musicalmushr00m/1625122113002/predictions.png
tags:


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


🤖 AI BOT 🤖

mushr00m

@musicalmushr00m


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


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


## How does it work?


The model uses the following pipeline.


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


## Training data


The model was trained on tweets from mushr00m.


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


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/26j7t29j/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @musicalmushr00m's tweets.


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


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/lxo37ttz/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/musicalmushr00m')
generator("My dream is", num_return_sequences=5)
```


## Limitations and bias


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


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


## About


*Built by Boris Dayma*


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: musicalmushr00m


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/anotherpattern/1616768869837/predictions.png
tags:


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


Another 🤖 AI Bot

@anotherpattern bot


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


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


## How does it work?


The model uses the following pipeline.


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


## Training data


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


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


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3lb52jwv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


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


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


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/33p25f4f/artifacts) is logged and versioned.


## How to use


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


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


## Limitations and bias


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


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


## About


*Built by Boris Dayma*


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: anotherpattern


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/gerardsans/1634670781074/predictions.png
tags:


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


🤖 AI BOT 🤖

ᐸGerardSans/ᐳ🤣🇬🇧

@gerardsans


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 ᐸGerardSans/ᐳ🤣🇬🇧.


| Data | ᐸGerardSans/ᐳ🤣🇬🇧 |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 648 |
| Short tweets | 586 |
| Tweets kept | 2016 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/115pr1rh/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 @gerardsans's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/10heg4by) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/10heg4by/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/gerardsans')
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: gerardsans


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/swedense/1603209768542/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'>


Sweden.se 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3243

Retweets
438

Short tweets
686

Tweets kept
2119


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/gn7q9sno/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 @swedense's tweets.


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


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


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/ben_r_hoffman/1618455389168/predictions.png
tags:


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


Anarcho-Moses 🐍 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 107 |
| Short tweets | 264 |
| Tweets kept | 2876 |


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


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2nf4hyti) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2nf4hyti/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/ben_r_hoffman')
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: ben_r_hoffman


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/thesravaka/1615863749292/predictions.png
tags:


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


qwest monk 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 3 |
| Short tweets | 451 |
| Tweets kept | 2796 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2y1u2jdy/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 @thesravaka's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3i046klx) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3i046klx/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/thesravaka')
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: thesravaka


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"


dawniee🌸☁🌈 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3225 |
| Retweets | 488 |
| Short tweets | 441 |
| Tweets kept | 2296 |


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


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1vytig6y) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1vytig6y/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/dawnieedreams')
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: dawnieedreams


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/stephenking/1613325153265/predictions.png
tags:


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


Stephen King 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3182 |
| Retweets | 721 |
| Short tweets | 189 |
| Tweets kept | 2272 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/26bv6b2f/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 @stephenking's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3nxbm83r) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3nxbm83r/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/stephenking')
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: stephenking


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"


🤖 AI BOT 🤖

Harrison Glaser

@nyjetstfmedia


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 Harrison Glaser.


| Data | Harrison Glaser |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 370 |
| Short tweets | 795 |
| Tweets kept | 2085 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/19vyig3e/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 @nyjetstfmedia's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/31aujunb) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/31aujunb/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/nyjetstfmedia')
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: nyjetstfmedia


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/cakesniffe1/1616776928773/predictions.png
tags:


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


(Suspiciously unoutra)Ged 🤖 AI Bot

@cakesniffe1 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 [@cakesniffe1's tweets](https://twitter.com/cakesniffe1).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3235 |
| Retweets | 414 |
| Short tweets | 201 |
| Tweets kept | 2620 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/kqahg35h/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 @cakesniffe1's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2b48lj3s) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2b48lj3s/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/cakesniffe1')
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: cakesniffe1


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/devtesla/1614137580281/predictions.png
tags:


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


an actual dog 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3164 |
| Retweets | 1246 |
| Short tweets | 222 |
| Tweets kept | 1696 |


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


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/8vrkz503) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/8vrkz503/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/devtesla')
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: devtesla


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/femawalmart/1609143235673/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'>


liam 🎅🗡🔜 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3077

Retweets
497

Short tweets
620

Tweets kept
1960


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2q9q9o6r/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 @femawalmart's tweets.


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


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


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"


<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'>


vishal 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
812

Retweets
37

Short tweets
84

Tweets kept
691


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


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/3c829t92) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/3c829t92/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/vishxl')
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: vishxl


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/avrillavigne/1601265702542/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'>


Avril Lavigne 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3112

Retweets
766

Short tweets
306

Tweets kept
2040


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/f4t9xd39/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 @avrillavigne's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/2rkl8zq3) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/2rkl8zq3/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/avrillavigne')
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: avrillavigne


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/regaleyes/1618781940476/predictions.png
tags:


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


Mark Stewart 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 2628 |
| Short tweets | 17 |
| Tweets kept | 600 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1weh2h4y/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 @regaleyes's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1yt7oko6) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1yt7oko6/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/regaleyes')
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: regaleyes


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'>


chrissy teigen 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3211

Retweets
187

Short tweets
458

Tweets kept
2566


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/3rq9c0fg/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 @chrissyteigen's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/bsu3mmey) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/bsu3mmey/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/chrissyteigen')
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: chrissyteigen


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/sadalsvvd/1616693754262/predictions.png
tags:


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


Sadal of Suud 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 381 |
| Short tweets | 218 |
| Tweets kept | 2648 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1emue7gj/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 @sadalsvvd's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1dui8eu5) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1dui8eu5/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/sadalsvvd')
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: sadalsvvd


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/circlekpolarpop/1628301626888/predictions.png
tags:


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


🤖 AI BOT 🤖

mannan

@circlekpolarpop


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 mannan.


| Data | mannan |
| --- | --- |
| Tweets downloaded | 3130 |
| Retweets | 1000 |
| Short tweets | 433 |
| Tweets kept | 1697 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/uvyj480y/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 @circlekpolarpop's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/gxud7br2) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/gxud7br2/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/circlekpolarpop')
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: circlekpolarpop


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/most_lamentable/1618953821275/predictions.png
tags:


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


ACAB Rocky 🖤 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3155 |
| Retweets | 2752 |
| Short tweets | 63 |
| Tweets kept | 340 |


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


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3i44t2y3) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3i44t2y3/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/most_lamentable')
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: most_lamentable


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/paola_rojas/1617459678125/predictions.png
tags:


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


Paola Rojas, CEO 🚀 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 563 |
| Short tweets | 423 |
| Tweets kept | 2258 |


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


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3f2aq14b) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3f2aq14b/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/paola_rojas')
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: paola_rojas


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/dog_feelings/1622239063857/predictions.png
tags:


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


🤖 AI BOT 🤖

Thoughts of Dog®

@dog_feelings


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 Thoughts of Dog®.


| Data | Thoughts of Dog® |
| --- | --- |
| Tweets downloaded | 1147 |
| Retweets | 13 |
| Short tweets | 17 |
| Tweets kept | 1117 |


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


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


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/19eaauii/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/dog_feelings')
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: dog_feelings


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/pokimanelol/1618687549011/predictions.png
tags:


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


pokimane 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 129 |
| Short tweets | 751 |
| Tweets kept | 2369 |


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


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2oa7wpqj) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2oa7wpqj/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/pokimanelol')
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: pokimanelol


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/dani_remade/1614192638303/predictions.png
tags:


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


Dani 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2270 |
| Retweets | 1509 |
| Short tweets | 108 |
| Tweets kept | 653 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/c0gm6a77/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 @dani_remade's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/18pilez8) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/18pilez8/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/dani_remade')
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: dani_remade


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/persimfan/1614096598302/predictions.png
tags:


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


bad vibes only 😇🧘🏻 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3089 |
| Retweets | 762 |
| Short tweets | 530 |
| Tweets kept | 1797 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/vjosb2cs/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 @persimfan's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2aaa509f) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2aaa509f/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/persimfan')
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: persimfan


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"


<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'>


François Chollet 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3231

Retweets
682

Short tweets
82

Tweets kept
2467


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/2rv5any2/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 @fchollet's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/3ajhtw99) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/3ajhtw99/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/fchollet')
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: fchollet


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/eigenrobot/1617781549970/predictions.png
tags:


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


eigenrobot 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 184 |
| Short tweets | 709 |
| Tweets kept | 2352 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1mw2r5kr/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 @eigenrobot's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1p1mtyuv) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1p1mtyuv/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/eigenrobot')
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: eigenrobot


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/gpt2drilpapa/1611164765660/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'>


AI Wint Pontifex 🤖 AI Bot

@gpt2drilpapa 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 [@gpt2drilpapa's tweets](https://twitter.com/gpt2drilpapa).


Data
Quantity

Tweets downloaded
218

Retweets
22

Short tweets
3

Tweets kept
193


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1yjlghvn/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 @gpt2drilpapa's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/m5q357m9) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/m5q357m9/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/gpt2drilpapa')
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: gpt2drilpapa


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: http://www.huggingtweets.com/scottmorrisonmp/1643715083152/predictions.png
tags:


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


🤖 AI BOT 🤖

Scott Morrison

@scottmorrisonmp


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 Scott Morrison.


| Data | Scott Morrison |
| --- | --- |
| Tweets downloaded | 3243 |
| Retweets | 610 |
| Short tweets | 34 |
| Tweets kept | 2599 |


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


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/19568gcc) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/19568gcc/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/scottmorrisonmp')
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: scottmorrisonmp


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: http://www.huggingtweets.com/rokroka25/1639531375291/predictions.png
tags:


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


🤖 AI BOT 🤖

Roka

@rokroka25


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 Roka.


| Data | Roka |
| --- | --- |
| Tweets downloaded | 2122 |
| Retweets | 572 |
| Short tweets | 363 |
| Tweets kept | 1187 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ubryx8ss/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 @rokroka25's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1qioq2np) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1qioq2np/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/rokroka25')
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: rokroka25


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/facebook/1609715229852/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'>


Facebook 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3227

Retweets
165

Short tweets
22

Tweets kept
3040


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/11ylirf6/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 @facebook's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/13i0hel1) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/13i0hel1/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/facebook')
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: facebook


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/phrasee/1606157185581/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'>


Phrasee® 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3216

Retweets
1086

Short tweets
87

Tweets kept
2043


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ldhpa4r/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 @phrasee's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/xwsari96) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/xwsari96/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/phrasee')
generator("My dream is", num_return_sequences=5)


### Limitations and bias