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 Kicchin (Most Powerful VTweeter).


| Data | Kicchin (Most Powerful VTweeter) |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 644 |
| Short tweets | 1223 |
| Tweets kept | 1380 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/25jce149/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 @kicchinnezumi's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3jg50eab) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3jg50eab/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/kicchinnezumi')
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: kicchinnezumi


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/dataandme/1602747308355/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'>


Mara Averick 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3209

Retweets
603

Short tweets
129

Tweets kept
2477


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/13sm7zyv/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 @dataandme's tweets.


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


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


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/postgohst/1632521452929/predictions.png
tags:


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


🤖 AI BOT 🤖

Connoise: FALSE GOD SYSTEMS @ONLINE🖤

@postgohst


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 Connoise: FALSE GOD SYSTEMS @ONLINE🖤.


| Data | Connoise: FALSE GOD SYSTEMS @ONLINE🖤 |
| --- | --- |
| Tweets downloaded | 3191 |
| Retweets | 319 |
| Short tweets | 387 |
| Tweets kept | 2485 |


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


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3dybkr8z) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3dybkr8z/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/postgohst')
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: postgohst


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/juanrallo/1611244251085/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'>


Juan Ramón Rallo 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3217

Retweets
84

Short tweets
167

Tweets kept
2966


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3v3r82g5/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 @juanrallo's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/307vpmrj) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/307vpmrj/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/juanrallo')
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: juanrallo


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/lucasgold06/1616728207965/predictions.png
tags:


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


Lucas 🧡 🤖 AI Bot

@lucasgold06 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 [@lucasgold06's tweets](https://twitter.com/lucasgold06).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 155 |
| Short tweets | 1079 |
| Tweets kept | 2011 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2bj8dyjt/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 @lucasgold06's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/fo8tlyjy) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/fo8tlyjy/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/lucasgold06')
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: lucasgold06


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/dariasuzu/1614219545681/predictions.png
tags:


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


🌸Daria🌸 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3183 |
| Retweets | 1813 |
| Short tweets | 417 |
| Tweets kept | 953 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/13f2yzvk/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 @dariasuzu's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/m73s18m3) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/m73s18m3/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/dariasuzu')
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: dariasuzu


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/lilbthebasedgod/1631657718769/predictions.png
tags:


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


🤖 AI BOT 🤖

Lil B THE BASEDGOD

@lilbthebasedgod


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 Lil B THE BASEDGOD.


| Data | Lil B THE BASEDGOD |
| --- | --- |
| Tweets downloaded | 3074 |
| Retweets | 2496 |
| Short tweets | 0 |
| Tweets kept | 578 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/19n5hf1u/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 @lilbthebasedgod's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2dlir6fx) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2dlir6fx/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/lilbthebasedgod')
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: lilbthebasedgod


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/richardknotel/1617846579962/predictions.png
tags:


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


Uniformology 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3223 |
| Retweets | 213 |
| Short tweets | 666 |
| Tweets kept | 2344 |


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


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3p4xpw1y) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3p4xpw1y/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/richardknotel')
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: richardknotel


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/chamath/1618092686446/predictions.png
tags:


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


Chamath Palihapitiya 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3197 |
| Retweets | 134 |
| Short tweets | 728 |
| Tweets kept | 2335 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/y5wkphb1/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 @chamath's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/yygp8qao) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/yygp8qao/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/chamath')
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: chamath


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 🤖

Jimmy

@jimlbsp


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 Jimmy.


| Data | Jimmy |
| --- | --- |
| Tweets downloaded | 3239 |
| Retweets | 348 |
| Short tweets | 383 |
| Tweets kept | 2508 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/213jvoqo/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 @jimlbsp's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2rtm7jla) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2rtm7jla/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/jimlbsp')
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: jimlbsp


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/fkuhlmeier/1603890209601/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'>


Florian Onur 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
168

Retweets
96

Short tweets
12

Tweets kept
60


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/yb58yalp/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 @fkuhlmeier's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/9cqebx43) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/9cqebx43/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/fkuhlmeier')
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: fkuhlmeier


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/dallaswentdown-jwgrieve-shanselman/1622469689056/predictions.png
tags:


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


🤖 AI CYBORG 🤖

Jack Grieve & Scott Hanselman & Marc Miller

@dallaswentdown-jwgrieve-shanselman


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 Jack Grieve & Scott Hanselman & Marc Miller.


| Data | Jack Grieve | Scott Hanselman | Marc Miller |
| --- | --- | --- | --- |
| Tweets downloaded | 3241 | 3248 | 204 |
| Retweets | 408 | 649 | 11 |
| Short tweets | 325 | 953 | 16 |
| Tweets kept | 2508 | 1646 | 177 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1szwn06m/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 @dallaswentdown-jwgrieve-shanselman's tweets.


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


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/umdhmmbr/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/dallaswentdown-jwgrieve-shanselman')
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: dallaswentdown-jwgrieve-shanselman


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/jen_122/1628851017758/predictions.png
tags:


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


🤖 AI BOT 🤖

Jen 🌸

@jen_122


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 Jen 🌸.


| Data | Jen 🌸 |
| --- | --- |
| Tweets downloaded | 3206 |
| Retweets | 1337 |
| Short tweets | 137 |
| Tweets kept | 1732 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ikjihay/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 @jen_122's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1by45dby) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1by45dby/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/jen_122')
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: jen_122


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/alexip/1602315863564/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'>


Alexis Perrier 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3199

Retweets
2059

Short tweets
49

Tweets kept
1091


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/157sg90v/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 @alexip's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/1wz9te3l) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/1wz9te3l/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/alexip')
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: alexip


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/timcast/1627059798876/predictions.png
tags:


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


🤖 AI BOT 🤖

Tim Pool

@timcast


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 Tim Pool.


| Data | Tim Pool |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 204 |
| Short tweets | 324 |
| Tweets kept | 2719 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3m867fab/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 @timcast's tweets.


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


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


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/wormonnastring/1628103109378/predictions.png
tags:


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


🤖 AI BOT 🤖

rhys cake

@wormonnastring


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 rhys cake.


| Data | rhys cake |
| --- | --- |
| Tweets downloaded | 3139 |
| Retweets | 841 |
| Short tweets | 260 |
| Tweets kept | 2038 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1f327axi/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 @wormonnastring's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3gmcf7lk) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3gmcf7lk/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/wormonnastring')
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: wormonnastring


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo_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'>


Michelangelo Bucci 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
1559

Retweets
248

Short tweets
52

Tweets kept
1259


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/198uon6g/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 @micbucci's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/f6humoq2) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/f6humoq2/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/micbucci')
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: micbucci


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/alice333ai-alicecweam/1625611976936/predictions.png
tags:


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


🤖 AI CYBORG 🤖

Alice Cream 🐇🍓 Vtuber & 👁⃤ lison

@alice333ai-alicecweam


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 Alice Cream 🐇🍓 Vtuber & 👁⃤ lison.


| Data | Alice Cream 🐇🍓 Vtuber | 👁⃤ lison |
| --- | --- | --- |
| Tweets downloaded | 3244 | 3216 |
| Retweets | 359 | 1062 |
| Short tweets | 463 | 200 |
| Tweets kept | 2422 | 1954 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/4cfpc23c/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 @alice333ai-alicecweam's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2r62epp4) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2r62epp4/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/alice333ai-alicecweam')
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: alice333ai-alicecweam


model author: huggingtweets


task: text-generation


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


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


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


bee.girl / rachel 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3185 |
| Retweets | 476 |
| Short tweets | 666 |
| Tweets kept | 2043 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1cj9094m/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 @thattrans_girl's tweets.


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


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


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/tenthkrige/1616941353204/predictions.png
tags:


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


Erg kit, then 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 725 |
| Retweets | 253 |
| Short tweets | 35 |
| Tweets kept | 437 |


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


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/25p19wdk) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/25p19wdk/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/tenthkrige')
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: tenthkrige


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: http://www.huggingtweets.com/darth/1641324110436/predictions.png
tags:


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


🤖 AI BOT 🤖

darth™

@darth


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 darth™.


| Data | darth™ |
| --- | --- |
| Tweets downloaded | 3189 |
| Retweets | 1278 |
| Short tweets | 677 |
| Tweets kept | 1234 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3t5g6hcx/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 @darth's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/c56rnej9) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/c56rnej9/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/darth')
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: darth


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/nyanberryy/1617147047295/predictions.png
tags:


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


sanrio mentall illness 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3171 |
| Retweets | 2093 |
| Short tweets | 430 |
| Tweets kept | 648 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2n3cxdi4/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 @nyanberryy's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2m61vei9) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2m61vei9/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/nyanberryy')
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: nyanberryy


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/dabit3/1607128642974/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'>


Nader Dabit 🤖 AI Bot

@dabit3 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 [@dabit3's tweets](https://twitter.com/dabit3).


Data
Quantity

Tweets downloaded
3205

Retweets
822

Short tweets
449

Tweets kept
1934


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/19qlkkql/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 @dabit3's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ai4t9ptt) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ai4t9ptt/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/dabit3')
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: dabit3


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/araffin2/1602238408015/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'>


Antonin Raffin 🤖 AI Bot

@araffin2 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 [@araffin2's tweets](https://twitter.com/araffin2).


Data
Quantity

Tweets downloaded
446

Retweets
173

Short tweets
5

Tweets kept
268


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/3qawuhc2/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 @araffin2's tweets.


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


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/aczxeidd/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/araffin2')
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: araffin2


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/gohere4porn-onlinepete/1625638031693/predictions.png
tags:


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


🤖 AI CYBORG 🤖

im pete online & Grateful King

@gohere4porn-onlinepete


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 im pete online & Grateful King.


| Data | im pete online | Grateful King |
| --- | --- | --- |
| Tweets downloaded | 3190 | 2141 |
| Retweets | 94 | 557 |
| Short tweets | 1003 | 217 |
| Tweets kept | 2093 | 1367 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1w0274vc/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 @gohere4porn-onlinepete's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2rvkp85n) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2rvkp85n/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/gohere4porn-onlinepete')
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: gohere4porn-onlinepete


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/chaneldrug_/1617788025995/predictions.png
tags:


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


Skinny La Drug 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2160 |
| Retweets | 376 |
| Short tweets | 668 |
| Tweets kept | 1116 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/24ua01ny/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 @chaneldrug_'s tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/26vdld9h) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/26vdld9h/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/chaneldrug_')
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: chaneldrug_


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/baidu_inc/1609715579938/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'>


Baidu Inc. 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
1364

Retweets
254

Short tweets
7

Tweets kept
1103


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/7tblrg78/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 @baidu_inc's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/23h9pyfk) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/23h9pyfk/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/baidu_inc')
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: baidu_inc


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: http://res.cloudinary.com/huggingtweets/image/upload/v1599933225/saitej786.jpg
tags:


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


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


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


<section class='prose'>


Sai Tej 🤖 AI Bot

@saitej786 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 [@saitej786's tweets](https://twitter.com/saitej786).


Data
Quantity

Tweets downloaded
2651

Retweets
2119

Short tweets
400

Tweets kept
132


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/emckckhg/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 @saitej786's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/3v32avnh) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/3v32avnh/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/saitej786')
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: saitej786


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: http://res.cloudinary.com/huggingtweets/image/upload/v1599871089/l2k.jpg
tags:


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


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


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


<section class='prose'>


Lukas Biewald 🤖 AI Bot

@l2k 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 [@l2k's tweets](https://twitter.com/l2k).


Data
Quantity

Tweets downloaded
2580

Retweets
598

Short tweets
88

Tweets kept
1894


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/17e2cw73/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 @l2k's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/10mi5zis) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/10mi5zis/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/l2k')
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: l2k


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/wonkhe/1617269501722/predictions.png
tags:


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


Wonkhe 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 82 |
| Short tweets | 0 |
| Tweets kept | 3168 |


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


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/23efmlg0) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/23efmlg0/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/wonkhe')
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: wonkhe


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/rmaxico/1617847228596/predictions.png
tags:


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


Roger Maxico 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3222 |
| Retweets | 132 |
| Short tweets | 296 |
| Tweets kept | 2794 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wnr95gcr/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 @rmaxico's tweets.


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


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


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/tgdeergirl/1614164124021/predictions.png
tags:


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


Deer 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3185 |
| Retweets | 1684 |
| Short tweets | 340 |
| Tweets kept | 1161 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/p50b07q5/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 @tgdeergirl's tweets.


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


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


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/evilvillain1231/1617765945431/predictions.png
tags:


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


cheyenne 🤖 AI Bot

@evilvillain1231 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 [@evilvillain1231's tweets](https://twitter.com/evilvillain1231).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3234 |
| Retweets | 439 |
| Short tweets | 469 |
| Tweets kept | 2326 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/fxxttga6/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 @evilvillain1231's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2zyo772l) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2zyo772l/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/evilvillain1231')
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: evilvillain1231


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/lmgriffjohnson/1601270660746/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'>


Griffin Johnson 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3197

Retweets
251

Short tweets
690

Tweets kept
2256


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


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/3syq9mme) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/3syq9mme/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/lmgriffjohnson')
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: lmgriffjohnson


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: http://www.huggingtweets.com/chickenhalf/1642665052826/predictions.png
tags:


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


🤖 AI BOT 🤖

chicken sandwich

@chickenhalf


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 chicken sandwich.


| Data | chicken sandwich |
| --- | --- |
| Tweets downloaded | 3202 |
| Retweets | 126 |
| Short tweets | 427 |
| Tweets kept | 2649 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3r0cwhle/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 @chickenhalf's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1zvaxh71) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1zvaxh71/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/chickenhalf')
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: chickenhalf


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/cf__bundy/1625285188781/predictions.png
tags:


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


🤖 AI BOT 🤖

ty

@cf__bundy


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 ty.


| Data | ty |
| --- | --- |
| Tweets downloaded | 1009 |
| Retweets | 117 |
| Short tweets | 200 |
| Tweets kept | 692 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2li311zj/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 @cf__bundy's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1hxi4q6u) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1hxi4q6u/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/cf__bundy')
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: cf__bundy


model author: huggingtweets


task: text-generation


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


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


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


🤖 AI CYBORG 🤖

BettyBoopQoS & Ragamuffin1970 & Cuckoldress Scarlet

@cuckoldresss-qobetty-ragamuffin197


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 BettyBoopQoS & Ragamuffin1970 & Cuckoldress Scarlet.


| Data | BettyBoopQoS | Ragamuffin1970 | Cuckoldress Scarlet |
| --- | --- | --- | --- |
| Tweets downloaded | 129 | 3247 | 1005 |
| Retweets | 2 | 11 | 252 |
| Short tweets | 10 | 584 | 70 |
| Tweets kept | 117 | 2652 | 683 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/zfpi2vmm/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 @cuckoldresss-qobetty-ragamuffin197's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/172rz2sh) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/172rz2sh/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/cuckoldresss-qobetty-ragamuffin197')
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: cuckoldresss-qobetty-ragamuffin197


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/nintendoamerica/1601313308462/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'>


Nintendo of America 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3217

Retweets
819

Short tweets
22

Tweets kept
2376


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/12wbwplr/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 @nintendoamerica's tweets.


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


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


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/philosophy_mark/1616697551802/predictions.png
tags:


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


Mark Schroeder 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 171 |
| Short tweets | 347 |
| Tweets kept | 2732 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/21aq4o19/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 @philosophy_mark's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1erhw6qr) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1erhw6qr/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/philosophy_mark')
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: philosophy_mark


model author: huggingtweets


task: text-generation