Marcin K 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 101 |
| Short tweets | 280 |
| Tweets kept | 2868 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2k7qbe8o/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 @krzyzanowskim's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/j8q05077) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/j8q05077/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/krzyzanowskim')
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: krzyzanowskim


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/dalailama/1615997106867/predictions.png
tags:


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


Dalai Lama 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1664 |
| Retweets | 0 |
| Short tweets | 1 |
| Tweets kept | 1663 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/p9scy18q/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 @dalailama's tweets.


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


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


model author: huggingtweets


task: text-generation


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


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


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


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


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


<section class='prose'>


Tarun 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
2492

Retweets
1206

Short tweets
136

Tweets kept
1150


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/1n10dp9h/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 @reachtarunhere's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/1bgxx4pj) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/1bgxx4pj/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/reachtarunhere')
generator("My dream is", num_return_sequences=5)


### Limitations and bias


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


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


## About


*Built by Boris Dayma*


</section>


For more details, visit the project repository.


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


model name: reachtarunhere


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/tamaybes/1616934032971/predictions.png
tags:


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


Tamay Besiroglu 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 181 |
| Retweets | 27 |
| Short tweets | 7 |
| Tweets kept | 147 |


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


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1mid24k0) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1mid24k0/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/tamaybes')
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: tamaybes


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/simpingboisinc/1636736705466/predictions.png
tags:


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


🤖 AI BOT 🤖

🎄elf-reen ₍•̀ 🐽•́ ₎ 🎗

@simpingboisinc


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 🎄elf-reen ₍•̀ 🐽•́ ₎ 🎗.


| Data | 🎄elf-reen ₍•̀ 🐽•́ ₎ 🎗 |
| --- | --- |
| Tweets downloaded | 3248 |
| Retweets | 220 |
| Short tweets | 438 |
| Tweets kept | 2590 |


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


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/15dy228a) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/15dy228a/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/simpingboisinc')
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: simpingboisinc


model author: huggingtweets


task: text-generation


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


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


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


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


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


<section class='prose'>


Sadhguru 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3222

Retweets
26

Short tweets
116

Tweets kept
3080


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


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/148bozc7) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/148bozc7/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/sadhgurujv')
generator("My dream is", num_return_sequences=5)


### Limitations and bias


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


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


## About


*Built by Boris Dayma*


</section>


For more details, visit the project repository.


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


model name: sadhgurujv


model author: huggingtweets


task: text-generation


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


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


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


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


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


<section class='prose'>


Delicious Tacos 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3214

Retweets
817

Short tweets
578

Tweets kept
1819


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/1573t9o6/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 @delicious_tacos's tweets.


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


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/39svipdd/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/delicious_tacos')
generator("My dream is", num_return_sequences=5)


### Limitations and bias


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


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


## About


*Built by Boris Dayma*


</section>


For more details, visit the project repository.


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


model name: delicious_tacos


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/davidgasquez/1600679713505/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'>


David Gasquez 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3065

Retweets
674

Short tweets
66

Tweets kept
2325


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/26tasjrn/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 @davidgasquez's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/3ez0xoyl) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/3ez0xoyl/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/davidgasquez')
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: davidgasquez


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 🤖

President Biden & Joe Biden

@joebiden-potus


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 President Biden & Joe Biden.


| Data | President Biden | Joe Biden |
| --- | --- | --- |
| Tweets downloaded | 872 | 3250 |
| Retweets | 32 | 384 |
| Short tweets | 3 | 38 |
| Tweets kept | 837 | 2828 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1c3s9vhj/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 @joebiden-potus's tweets.


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


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/tcstvtkt/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/joebiden-potus')
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: joebiden-potus


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/camrin_blaze/1619573161434/predictions.png
tags:


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


rin 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 258 |
| Retweets | 27 |
| Short tweets | 22 |
| Tweets kept | 209 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/34155lyy/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 @camrin_blaze's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/291c9e66) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/291c9e66/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/camrin_blaze')
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: camrin_blaze


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/andreskwon/1600798823307/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'>


Andrés Dae Keun Kwon 권대건 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3150

Retweets
2468

Short tweets
163

Tweets kept
519


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


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/2en2cxq7) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/2en2cxq7/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/andreskwon')
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: andreskwon


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/sapphirelally/1616767752799/predictions.png
tags:


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


Sapphire Lally 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3186 |
| Retweets | 600 |
| Short tweets | 143 |
| Tweets kept | 2443 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wntmqj00/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 @sapphirelally's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ge3zqrv) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ge3zqrv/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/sapphirelally')
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: sapphirelally


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/lado_boi/1617800384792/predictions.png
tags:


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


Lado Soyboy 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2986 |
| Retweets | 1786 |
| Short tweets | 168 |
| Tweets kept | 1032 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1nyzzg3z/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 @lado_boi's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/17gefac5) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/17gefac5/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/lado_boi')
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: lado_boi


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/adhib/1617472294749/predictions.png
tags:


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


Adam Hibbert 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 89 |
| Short tweets | 509 |
| Tweets kept | 2649 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3vmd854v/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 @adhib's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/okvjl3od) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/okvjl3od/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/adhib')
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: adhib


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/thyacinth/1616691723294/predictions.png
tags:


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


Xin Hui 芯慧 is my first name 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3173 |
| Retweets | 714 |
| Short tweets | 245 |
| Tweets kept | 2214 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/17paiax7/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 @thyacinth's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1wkde8bc) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1wkde8bc/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/thyacinth')
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: thyacinth


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/hairchewer/1617766469015/predictions.png
tags:


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


maggie 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3223 |
| Retweets | 258 |
| Short tweets | 484 |
| Tweets kept | 2481 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ar310nf/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 @hairchewer's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2fojeuw3) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2fojeuw3/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/hairchewer')
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: hairchewer


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/kwiihours/1616725434123/predictions.png
tags:


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


Kwii 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 109 |
| Short tweets | 1051 |
| Tweets kept | 2087 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2i5s41be/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 @kwiihours's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1aetz628) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1aetz628/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/kwiihours')
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: kwiihours


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"


DaBaby 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2970 |
| Retweets | 2078 |
| Short tweets | 300 |
| Tweets kept | 592 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2pxo4nhg/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 @dababydababy's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3byz0p03) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3byz0p03/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/dababydababy')
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: dababydababy


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/weworewhat/1607531750256/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'>


Danielle Bernstein 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3202

Retweets
45

Short tweets
389

Tweets kept
2768


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3p1bdluv/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 @weworewhat's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/32xwr6fx) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/32xwr6fx/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/weworewhat')
generator("My dream is", num_return_sequences=5)


### Limitations and bias


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


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


## About


*Built by Boris Dayma*


</section>


For more details, visit the project repository.


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


model name: weworewhat


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: http://www.huggingtweets.com/lavanyaai/1600320144154/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'>


Lavanya 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3187

Retweets
1482

Short tweets
220

Tweets kept
1485


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/1s4lpnmf/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 @lavanyaai's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/6zcv33k4) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/6zcv33k4/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/lavanyaai')
generator("My dream is", num_return_sequences=5)


### Limitations and bias


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


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


## About


*Built by Boris Dayma*


</section>


For more details, visit the project repository.


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


model name: lavanyaai


model author: huggingtweets


task: text-generation


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


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


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


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


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


<section class='prose'>


NVIDIA 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3222

Retweets
1876

Short tweets
18

Tweets kept
1328


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/35e4bboc/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 @nvidia's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/2t7z7a45) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/2t7z7a45/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/nvidia')
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: nvidia


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/kr00ney-nerdwallet-producthunt/1623668927813/predictions.png
tags:


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


🤖 AI CYBORG 🤖

Product Hunt 😸 & Kate Rooney & NerdWallet

@kr00ney-nerdwallet-producthunt


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 Product Hunt 😸 & Kate Rooney & NerdWallet.


| Data | Product Hunt 😸 | Kate Rooney | NerdWallet |
| --- | --- | --- | --- |
| Tweets downloaded | 3250 | 2622 | 3234 |
| Retweets | 92 | 896 | 713 |
| Short tweets | 234 | 125 | 22 |
| Tweets kept | 2924 | 1601 | 2499 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/vrmrzm0o/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 @kr00ney-nerdwallet-producthunt's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/zdu3nltx) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/zdu3nltx/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/kr00ney-nerdwallet-producthunt')
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: kr00ney-nerdwallet-producthunt


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/liararoux/1614107104869/predictions.png
tags:


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


Liara Roux 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3179 |
| Retweets | 861 |
| Short tweets | 401 |
| Tweets kept | 1917 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1p2npmua/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 @liararoux's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/z4wqd6hv) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/z4wqd6hv/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/liararoux')
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: liararoux


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/sematarygravemn/1630171139756/predictions.png
tags:


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


🤖 AI BOT 🤖

SEMATARY GRAVE MAN ✟ ✟ ✟

@sematarygravemn


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 SEMATARY GRAVE MAN ✟ ✟ ✟.


| Data | SEMATARY GRAVE MAN ✟ ✟ ✟ |
| --- | --- |
| Tweets downloaded | 585 |
| Retweets | 75 |
| Short tweets | 116 |
| Tweets kept | 394 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3jy7xpe9/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 @sematarygravemn's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2svkr1dq) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2svkr1dq/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/sematarygravemn')
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: sematarygravemn


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/jk_rowling/1617719835072/predictions.png
tags:


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


J.K. Rowling 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3186 |
| Retweets | 719 |
| Short tweets | 190 |
| Tweets kept | 2277 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/17fpax3c/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 @jk_rowling's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2362x8t2) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2362x8t2/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/jk_rowling')
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: jk_rowling


model author: huggingtweets


task: text-generation


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


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


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


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


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


<section class='prose'>


James Woods 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3248

Retweets
171

Short tweets
405

Tweets kept
2672


[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/2gdf0h5l/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 @realjameswoods's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/23h52qc6) for full transparency and reproducibility.


At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/23h52qc6/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/realjameswoods')
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: realjameswoods


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/purplepupper/1624499307689/predictions.png
tags:


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


🤖 AI BOT 🤖

Lottie (charlotte.wirtanen@SecondLife)

@purplepupper


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 Lottie (charlotte.wirtanen@SecondLife).


| Data | Lottie (charlotte.wirtanen@SecondLife) |
| --- | --- |
| Tweets downloaded | 2075 |
| Retweets | 1267 |
| Short tweets | 141 |
| Tweets kept | 667 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1kqbqk9g/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 @purplepupper's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2z1n8g4b) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2z1n8g4b/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/purplepupper')
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: purplepupper


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/amelamelcia/1637950041914/predictions.png
tags:


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


🤖 AI BOT 🤖

Amelka

@amelamelcia


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 Amelka.


| Data | Amelka |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 101 |
| Short tweets | 550 |
| Tweets kept | 2593 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/tomda94s/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 @amelamelcia's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1hxvf49x) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1hxvf49x/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/amelamelcia')
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: amelamelcia


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/stefrappeneau/1609353045656/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'>


Stephane Rappeneau 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3208

Retweets
297

Short tweets
86

Tweets kept
2825


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/qa7ycwy3/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 @stefrappeneau's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/b1exumr4) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/b1exumr4/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/stefrappeneau')
generator("My dream is", num_return_sequences=5)


### Limitations and bias


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


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


## About


*Built by Boris Dayma*


</section>


For more details, visit the project repository.


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


model name: stefrappeneau


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/coinburnm/1631499945178/predictions.png
tags:


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


🤖 AI BOT 🤖

Coinburn

@coinburnm


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 Coinburn.


| Data | Coinburn |
| --- | --- |
| Tweets downloaded | 837 |
| Retweets | 72 |
| Short tweets | 141 |
| Tweets kept | 624 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/38wldrmx/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 @coinburnm's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2z4rh9o1) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2z4rh9o1/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/coinburnm')
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: coinburnm


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: http://www.huggingtweets.com/hostagekiller/1641809138009/predictions.png
tags:


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


🤖 AI BOT 🤖

HUSSY2K.

@hostagekiller


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 HUSSY2K..


| Data | HUSSY2K. |
| --- | --- |
| Tweets downloaded | 3186 |
| Retweets | 819 |
| Short tweets | 395 |
| Tweets kept | 1972 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/u2hpg02v/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 @hostagekiller's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3tx11pqs) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3tx11pqs/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/hostagekiller')
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: hostagekiller


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"


Night the aeromorph 🤖 AI Bot

@drumbunkerdrag1 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 [@drumbunkerdrag1's tweets](https://twitter.com/drumbunkerdrag1).


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 148 |
| Retweets | 8 |
| Short tweets | 27 |
| Tweets kept | 113 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1sfm2bdn/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 @drumbunkerdrag1's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2trykl4g) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2trykl4g/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/drumbunkerdrag1')
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: drumbunkerdrag1


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: http://www.huggingtweets.com/krankergeist1/1639115420252/predictions.png
tags:


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


🤖 AI BOT 🤖

Kranker Geist

@krankergeist1


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 Kranker Geist.


| Data | Kranker Geist |
| --- | --- |
| Tweets downloaded | 755 |
| Retweets | 75 |
| Short tweets | 336 |
| Tweets kept | 344 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/zpv7kt6r/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 @krankergeist1's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1jipf5v9) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1jipf5v9/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/krankergeist1')
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: krankergeist1


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/p69ns/1620455795681/predictions.png
tags:


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


🤖 AI BOT 🤖

Puneet 🌌

@p69ns


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 Puneet 🌌.


| Data | Puneet 🌌 |
| --- | --- |
| Tweets downloaded | 223 |
| Retweets | 44 |
| Short tweets | 51 |
| Tweets kept | 128 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/7i9ntszz/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 @p69ns's tweets.


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


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2wsrhmmr/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/p69ns')
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: p69ns


model author: huggingtweets


task: text-generation


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


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


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


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


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


<section class='prose'>


Barnes & Noble 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3156

Retweets
821

Short tweets
124

Tweets kept
2211


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2tso130j/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 @bnbuzz's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2b6k9q0j) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2b6k9q0j/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/bnbuzz')
generator("My dream is", num_return_sequences=5)


### Limitations and bias


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


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


## About


*Built by Boris Dayma*


</section>


For more details, visit the project repository.


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


model name: bnbuzz


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/hideki_naganuma/1619655487401/predictions.png
tags:


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


HIDEKI NAGANUMA｜CEO OF FUNKY FRESH BEATS 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3227 |
| Retweets | 1186 |
| Short tweets | 208 |
| Tweets kept | 1833 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1766c7eg/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 @hideki_naganuma's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/13aynk8e) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/13aynk8e/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/hideki_naganuma')
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: hideki_naganuma


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: https://www.huggingtweets.com/tylerthecreator/1608310160581/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'>


Tyler, The Creator 🤖 AI Bot

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


Data
Quantity

Tweets downloaded
3213

Retweets
452

Short tweets
633

Tweets kept
2128


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1u6g35xr/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 @tylerthecreator's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2m8auax4) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2m8auax4/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/tylerthecreator')
generator("My dream is", num_return_sequences=5)


### Limitations and bias


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


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


## About


*Built by Boris Dayma*


</section>


For more details, visit the project repository.


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


model name: tylerthecreator


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"


Sera♪ 🤖 AI Bot

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


| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 266 |
| Short tweets | 727 |
| Tweets kept | 2251 |


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


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/b5wbv6sy) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/b5wbv6sy/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/seraxiz')
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: seraxiz


model author: huggingtweets


task: text-generation


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


language: en
thumbnail: http://www.huggingtweets.com/rapevictlm/1639365417456/predictions.png
tags:


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


🤖 AI BOT 🤖

📡 CRIMEA RIVER

@rapevictlm


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 📡 CRIMEA RIVER.


| Data | 📡 CRIMEA RIVER |
| --- | --- |
| Tweets downloaded | 3212 |
| Retweets | 678 |
| Short tweets | 1100 |
| Tweets kept | 1434 |


[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/116qxl17/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 @rapevictlm's tweets.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3pbvmby8) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3pbvmby8/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/rapevictlm')
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: rapevictlm


model author: huggingtweets


task: text-generation


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