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


model name: pix_uwu


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/tosh14k1/1617758143605/predictions.png
tags:


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


fag noumena 🤖 AI Bot

@tosh14k1 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 [@tosh14k1's tweets](https://twitter.com/tosh14k1).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 810 |
| Retweets | 317 |
| Short tweets | 106 |
| Tweets kept | 387 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/xkmnrbkr/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 @tosh14k1's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1cx36vga) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1cx36vga/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/tosh14k1')
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: tosh14k1


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: http://www.huggingtweets.com/ctrlcreep/1637573720314/predictions.png
tags:


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


🤖 AI BOT 🤖

infineot

@ctrlcreep


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 infineot.


| Data | infineot |
| --- | --- |
| Tweets downloaded | 3241 |
| Retweets | 171 |
| Short tweets | 51 |
| Tweets kept | 3019 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/26459hr9/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 @ctrlcreep's tweets.


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


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


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: http://www.huggingtweets.com/petersengraph/1642785736519/predictions.png
tags:


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


🤖 AI BOT 🤖

The Petersen Graph

@petersengraph


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 The Petersen Graph.


| Data | The Petersen Graph |
| --- | --- |
| Tweets downloaded | 813 |
| Retweets | 72 |
| Short tweets | 82 |
| Tweets kept | 659 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2kq4r4x4/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 @petersengraph's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1b1liwq3) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1b1liwq3/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/petersengraph')
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: petersengraph


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/childermass4/1616900186649/predictions.png
tags:


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


R. K. (Productive for 9/14 straight days) 🤖 AI Bot

@childermass4 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 [@childermass4's tweets](https://twitter.com/childermass4).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 87 |
| Short tweets | 545 |
| Tweets kept | 2615 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3szpvshy/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 @childermass4's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2x2ddbef) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2x2ddbef/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/childermass4')
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: childermass4


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/philosoraptor/1616695417900/predictions.png
tags:


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


Real organic pattern 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3196 |
| Retweets | 700 |
| Short tweets | 278 |
| Tweets kept | 2218 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3k8xlpzy/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 @philosoraptor's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/5wwiewx7) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/5wwiewx7/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/philosoraptor')
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: philosoraptor


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 CYBORG 🤖

千坂恭二 :『哲学問答2020・ウィルス塹壕戦』 & Go Ando / PREDUCTS / THE GUILD & takano@MAMORI0

@chisaka_kyoji-goando-iototaku


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 千坂恭二 :『哲学問答2020・ウィルス塹壕戦』 & Go Ando / PREDUCTS / THE GUILD & takano@MAMORI0.


| Data | 千坂恭二 :『哲学問答2020・ウィルス塹壕戦』 | Go Ando / PREDUCTS / THE GUILD | takano@MAMORI0 |
| --- | --- | --- | --- |
| Tweets downloaded | 3246 | 3246 | 3233 |
| Retweets | 957 | 90 | 1144 |
| Short tweets | 455 | 1680 | 634 |
| Tweets kept | 1834 | 1476 | 1455 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/i7bv0620/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 @chisaka_kyoji-goando-iototaku's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2yl0izon) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2yl0izon/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/chisaka_kyoji-goando-iototaku')
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: chisaka_kyoji-goando-iototaku


model author: huggingtweets


tags: en, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/cunfamiliaris/1616770251594/predictions.png
tags:


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


Cane / he|him / Enby-femboi / Cute doggo 🔞🌽 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 135 |
| Short tweets | 287 |
| Tweets kept | 2822 |


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


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/b0a9baoe) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/b0a9baoe/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/cunfamiliaris')
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: cunfamiliaris


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/gods_txt/1623749962893/predictions.png
tags:


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


🤖 AI BOT 🤖

GPT-2 Religion AI

@gods_txt


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 GPT-2 Religion AI.


| Data | GPT-2 Religion AI |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 66 |
| Short tweets | 9 |
| Tweets kept | 3174 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/l1h0u8uh/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 @gods_txt's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2i75xs06) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2i75xs06/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/gods_txt')
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: gods_txt


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/protoneutype/1616628248591/predictions.png
tags:


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


TYPE of PROTO 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3223 |
| Retweets | 719 |
| Short tweets | 129 |
| Tweets kept | 2375 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2xz1sz9l/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 @protoneutype's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/7z0k8qgo) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/7z0k8qgo/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/protoneutype')
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: protoneutype


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 🤖

T. R. Darling

@quietpinetrees


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 T. R. Darling.


| Data | T. R. Darling |
| --- | --- |
| Tweets downloaded | 2230 |
| Retweets | 257 |
| Short tweets | 13 |
| Tweets kept | 1960 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3670guxr/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 @quietpinetrees's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ij8ltd0) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ij8ltd0/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/quietpinetrees')
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: quietpinetrees


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/opossumzavod/1617768474582/predictions.png
tags:


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


Leonid Bruhzhnev 🚩⚔👑 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3184 |
| Retweets | 1034 |
| Short tweets | 242 |
| Tweets kept | 1908 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/23nwwphy/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 @opossumzavod's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1dqk2qsz) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1dqk2qsz/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/opossumzavod')
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: opossumzavod


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/brittany_broski/1605761309372/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'>


brittany 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
1838

Retweets
451

Short tweets
392

Tweets kept
995


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2hz6b9b8/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 @brittany_broski's tweets.


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


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/35sgntcf/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/brittany_broski')
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: brittany_broski


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/raels_lamia/1627143259544/predictions.png
tags:


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


🤖 AI BOT 🤖

🐍 Krissssssy

@raels_lamia


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 🐍 Krissssssy.


| Data | 🐍 Krissssssy |
| --- | --- |
| Tweets downloaded | 3237 |
| Retweets | 1035 |
| Short tweets | 509 |
| Tweets kept | 1693 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3d0m12dk/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 @raels_lamia's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2b67w9oh) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2b67w9oh/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/raels_lamia')
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: raels_lamia


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: http://www.huggingtweets.com/arryadia_brk/1643119471683/predictions.png
tags:


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


🤖 AI BOT 🤖

الرياضية - عاجل

@arryadia_brk


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 الرياضية - عاجل.


| Data | الرياضية - عاجل |
| --- | --- |
| Tweets downloaded | 1548 |
| Retweets | 11 |
| Short tweets | 33 |
| Tweets kept | 1504 |


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


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2e36ahiu) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2e36ahiu/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/arryadia_brk')
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: arryadia_brk


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/caseygripps/1616642646137/predictions.png
tags:


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


casey 🖤 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3228 |
| Retweets | 347 |
| Short tweets | 375 |
| Tweets kept | 2506 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2a8u37lc/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 @caseygripps's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/22x53mg6) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/22x53mg6/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/caseygripps')
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: caseygripps


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/claire_v0ltaire-praisegodbarbon/1635306972772/predictions.png
tags:


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


🤖 AI CYBORG 🤖

Boston Psychology PhD & Claire

@claire_v0ltaire-praisegodbarbon


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


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


## How does it work?


The model uses the following pipeline.


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


## Training data


The model was trained on tweets from Boston Psychology PhD & Claire.


| Data | Boston Psychology PhD | Claire |
| --- | --- | --- |
| Tweets downloaded | 3211 | 3237 |
| Retweets | 798 | 494 |
| Short tweets | 272 | 566 |
| Tweets kept | 2141 | 2177 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/qvxkc6zt/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @claire_v0ltaire-praisegodbarbon's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ssxlnhr9) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ssxlnhr9/artifacts) is logged and versioned.


## How to use


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


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingtweets/claire_v0ltaire-praisegodbarbon')
generator("My dream is", num_return_sequences=5)
```


## Limitations and bias


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


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


## About


*Built by Boris Dayma*


For more details, visit the project repository.


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


model name: claire_v0ltaire-praisegodbarbon


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"


Chaz Firestone 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3242 |
| Retweets | 512 |
| Short tweets | 407 |
| Tweets kept | 2323 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2pncq1l1/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 @chazfirestone's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2z06ysmr) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2z06ysmr/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/chazfirestone')
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: chazfirestone


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/henninglobin/1603610850998/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'>


Henning Lobin 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
928

Retweets
147

Short tweets
15

Tweets kept
766


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/admmx96c/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 @henninglobin's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/f3vjyvag) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/f3vjyvag/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/henninglobin')
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: henninglobin


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/nestor_d/1616784666647/predictions.png
tags:


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


Ṅ̮̖̦̬̬̬̼̓͊͂̾̂͆é͆ṡ͍̼̱̜̦̋̀t̡̯̭̝̮̍͑̐̽o̺͎͐ͫ̅̉͒̑̚r̋ͮ͗ 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3239 |
| Retweets | 232 |
| Short tweets | 340 |
| Tweets kept | 2667 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1iu58bxy/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 @nestor_d's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/kyb41zp8) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/kyb41zp8/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/nestor_d')
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: nestor_d


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/_me_you_coward/1616717688936/predictions.png
tags:


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


Phlogiston International 😇🌷🇵🇸 ☭🏴 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3214 |
| Retweets | 358 |
| Short tweets | 629 |
| Tweets kept | 2227 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/362p183s/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 @_me_you_coward's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/clfmnc4u) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/clfmnc4u/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/_me_you_coward')
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: _me_you_coward


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/febreezyxd/1614137805621/predictions.png
tags:


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


FebreezyXD 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2579 |
| Retweets | 281 |
| Short tweets | 633 |
| Tweets kept | 1665 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1uhj4h75/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 @febreezyxd's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/37c0iqc2) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/37c0iqc2/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/febreezyxd')
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: febreezyxd


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"


Abu Pepe 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3214 |
| Retweets | 829 |
| Short tweets | 692 |
| Tweets kept | 1693 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2g5r3yyi/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 @abupepeofficial's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/avhc3qdq) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/avhc3qdq/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/abupepeofficial')
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: abupepeofficial


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/skinny_pickens/1626411183607/predictions.png
tags:


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


🤖 AI BOT 🤖

skinny pickens

@skinny_pickens


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 skinny pickens.


| Data | skinny pickens |
| --- | --- |
| Tweets downloaded | 2817 |
| Retweets | 1329 |
| Short tweets | 154 |
| Tweets kept | 1334 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2guwsx1g/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 @skinny_pickens's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/109349ze) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/109349ze/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/skinny_pickens')
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: skinny_pickens


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"


seman🐄 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3184 |
| Retweets | 970 |
| Short tweets | 370 |
| Tweets kept | 1844 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/y4rnqx7f/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 @porter_esq's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/eqz1nxjx) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/eqz1nxjx/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/porter_esq')
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: porter_esq


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/vermontsmash/1615171689234/predictions.png
tags:


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


Vermont Smash Ultimate 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 161 |
| Retweets | 22 |
| Short tweets | 13 |
| Tweets kept | 126 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2jy80fph/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 @vermontsmash's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/14r2owgg) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/14r2owgg/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/vermontsmash')
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: vermontsmash


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/doityboy/1621603103969/predictions.png
tags:


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


🤖 AI BOT 🤖

Doityboy

@doityboy


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 Doityboy.


| Data | Doityboy |
| --- | --- |
| Tweets downloaded | 3180 |
| Retweets | 551 |
| Short tweets | 660 |
| Tweets kept | 1969 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/17aeg3tr/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 @doityboy's tweets.


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


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


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/hamletbatista/1600859203128/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'>


Hamlet Batista 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3222

Retweets
1431

Short tweets
620

Tweets kept
1171


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/3t0swbn3/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 @hamletbatista's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/ypcx69ns) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/ypcx69ns/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/hamletbatista')
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: hamletbatista


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/forshaper/1616646541286/predictions.png
tags:


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


Ray Doraisamy 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3241 |
| Retweets | 181 |
| Short tweets | 413 |
| Tweets kept | 2647 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/392kuq3o/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 @forshaper's tweets.


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


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


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/dansalvato/1612858230042/predictions.png
tags:


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


Dan Salvato 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3233 |
| Retweets | 197 |
| Short tweets | 233 |
| Tweets kept | 2803 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2jbu3vnq/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 @dansalvato's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1c66e4az) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1c66e4az/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/dansalvato')
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: dansalvato


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/starbannergames/1616902434636/predictions.png
tags:


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


Nanda //// Star Banner Games 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 990 |
| Retweets | 134 |
| Short tweets | 97 |
| Tweets kept | 759 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/39zshs8e/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 @starbannergames's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/292aokzw) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/292aokzw/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/starbannergames')
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: starbannergames


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/dorkyfolf/1617804114723/predictions.png
tags:


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


Thistle Bnuuy 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2881 |
| Retweets | 1665 |
| Short tweets | 255 |
| Tweets kept | 961 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2m0yq9vg/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 @dorkyfolf's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2wv3osjp) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2wv3osjp/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/dorkyfolf')
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: dorkyfolf


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/tommyhump/1617421683439/predictions.png
tags:


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


Tommy Humphreys 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 542 |
| Short tweets | 133 |
| Tweets kept | 2571 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3biusud1/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 @tommyhump's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1i3ro0vj) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1i3ro0vj/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/tommyhump')
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: tommyhump


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/vgr/1602716793427/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'>


Venkatesh Rao 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3201

Retweets
487

Short tweets
235

Tweets kept
2479


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/3ro13ju0/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 @vgr's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/18283z0w) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/18283z0w/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/vgr')
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: vgr


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: http://www.huggingtweets.com/goando/1643510064373/predictions.png
tags:


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


🤖 AI BOT 🤖

Go Ando / PREDUCTS / THE GUILD

@goando


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 Go Ando / PREDUCTS / THE GUILD.


| Data | Go Ando / PREDUCTS / THE GUILD |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 91 |
| Short tweets | 1680 |
| Tweets kept | 1476 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/37h8wmzh/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 @goando's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3qeev4eu) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3qeev4eu/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/goando')
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: goando


model author: huggingtweets


tags: en, huggingtweets


language: en
thumbnail: https://www.huggingtweets.com/ijustbluemyself/1625279746808/predictions.png
tags:


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


🤖 AI BOT 🤖

¯\_(ツ)_/¯

@ijustbluemyself


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 ¯\_(ツ)_/¯.


| Data | ¯\_(ツ)_/¯ |
| --- | --- |
| Tweets downloaded | 3224 |
| Retweets | 250 |
| Short tweets | 982 |
| Tweets kept | 1992 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/qgmk16ox/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 @ijustbluemyself's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2yq2ve7k) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2yq2ve7k/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/ijustbluemyself')
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: ijustbluemyself


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/disconcision/1616643733458/predictions.png
tags:


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


andrew blinn 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2262 |
| Retweets | 453 |
| Short tweets | 159 |
| Tweets kept | 1650 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/n4jrdsqh/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 @disconcision's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2f36wyoh) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2f36wyoh/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/disconcision')
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: disconcision


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/abelaer/1616682063676/predictions.png
tags:


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


Abel Jansma 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 231 |
| Retweets | 15 |
| Short tweets | 14 |
| Tweets kept | 202 |


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


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


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


model author: huggingtweets


task: text-generation


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


license: apache-2.0
tags:


- generated_from_trainer
model-index:
name: wav2vec2-xls-r-300m-romanian


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## This model achieves WER on common-voice ro test split of WER: 12.457178%


# wav2vec2-xls-r-300m-romanian


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an common voice ro and RSS dataset.
It achieves the following results on the evaluation set:


- eval_loss: 0.0836
- eval_wer: 0.0705
- eval_runtime: 160.4549
- eval_samples_per_second: 11.081
- eval_steps_per_second: 1.39
- epoch: 14.38
- step: 2703


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 15
- mixed_precision_training: Native AMP


### Framework versions


- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3


Used the following code for evaluation:


```
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "ro", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("Dumiiii/wav2vec2-xls-r-300m-romanian")
model = Wav2Vec2ForCTC.from_pretrained("Dumiiii/wav2vec2-xls-r-300m-romanian")
model.to("cuda")

chars_to_ignore_regex = '['+string.punctuation+']'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
  batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
  speech_array, sampling_rate = torchaudio.load(batch["path"])
  batch["speech"] = resampler(speech_array).squeeze().numpy()
  return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
  inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

  with torch.no_grad():
    logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
  return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```


Credits for evaluation: https://huggingface.co/anton-l


model name: wav2vec2-xls-r-300m-romanian


model author: Dumiiii


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, transformers, license:apache-2.0, generated_from_trainer, model-index


language: no
license: cc-by-4.0
pipeline_tag: fill-mask
tags:


- norwegian
- bert
thumbnail: https://raw.githubusercontent.com/ltgoslo/NorBERT/main/Norbert.png


## Quickstart


**Release 2.0** (February 7, 2022)


Trained on the very large corpus of Norwegian (C4 + NCC, about 15 billion word tokens).
Features a 50 000 words vocabulary and was trained using Whole Word Masking.


Download the model here:


* Cased Norwegian BERT Base 2.0 (NorBERT 2): [221.zip](http://vectors.nlpl.eu/repository/20/221.zip)


More about NorBERT training corpora, training procedure and evaluation benchmarks: http://norlm.nlpl.eu/


Associated code: https://github.com/ltgoslo/NorBERT


Check this paper for more details:


*Andrey Kutuzov, Jeremy Barnes, Erik Velldal, Lilja Øvrelid, Stephan Oepen. [Large-Scale Contextualised Language Modelling for Norwegian](https://aclanthology.org/2021.nodalida-main.4/), NoDaLiDa'21 (2021)*


NorBERT was trained as a part of NorLM, a joint initiative of the projects [EOSC-Nordic](https://www.eosc-nordic.eu/) (European Open Science Cloud),
coordinated by the [Language Technology Group](https://www.mn.uio.no/ifi/english/research/groups/ltg/) (LTG) at the University of Oslo.


The computations were performed on resources provided by UNINETT Sigma2 - the National Infrastructure for High Performance Computing and Data Storage in Norway.


model name: norbert2


model author: ltgoslo


task: fill-mask


tags: pytorch, tf, bert, fill-mask, no, transformers, license:cc-by-4.0, norwegian, autonlp_compatible, infinity_compatible


**Release 1.1** (February 13, 2021)


* Cased Norwegian BERT Base: [216.zip](http://vectors.nlpl.eu/repository/20/216.zip)


More about NorBERT training corpora and training procedure: http://norlm.nlpl.eu/


*Andrey Kutuzov, Jeremy Barnes, Erik Velldal, Lilja Øvrelid, Stephan Oepen. [Large-Scale Contextualised Language Modelling for Norwegian](https://arxiv.org/abs/2104.06546), NoDaLiDa'21 (2021)*


NorBERT was trained as a part of NorLM, a joint initiative of the projects [EOSC-Nordic](https://www.eosc-nordic.eu/) (European Open Science Cloud) and [SANT](https://www.mn.uio.no/ifi/english/research/projects/sant/index.html) (Sentiment Analysis for Norwegian),
coordinated by the [Language Technology Group](https://www.mn.uio.no/ifi/english/research/groups/ltg/) (LTG) at the University of Oslo.


model name: norbert


tags: pytorch, tf, jax, bert, fill-mask, no, arxiv:2104.06546, transformers, license:cc-by-4.0, norwegian, autonlp_compatible, infinity_compatible


skt/kogpt2-base-v2에 wellness 및 일상챗봇 데이터를 fine-tuning한 모델입니다.


model name: KoGPT2_finetuned_wellness


model author: jack-oh


task: text-generation


tags: pytorch, gpt2, text-generation, transformers


---hello


model name: investopedia-QnA


model author: ruishan-lin


task: question-answering


tags: pytorch, distilbert, question-answering, transformers, autonlp_compatible, infinity_compatible


This is a tiny-albert random model to be used for basic testing.


model name: tiny-albert


model author: hf-internal-testing


task: fill-mask


tags: pytorch, albert, fill-mask, transformers, autonlp_compatible, infinity_compatible


Please leave this README for testing purposes


model name: processor_with_lm


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, transformers


Small model used as a  token-classification to enable fast tests on that pipeline.