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text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1377989668189405192/II6ZfJPK_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Atlassian</div>
<div style="text-align: center; font-size: 14px;">@atlassian</div>
</div>
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 Atlassian.
| Data | Atlassian |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 824 |
| Short tweets | 58 |
| Tweets kept | 2367 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2i1f4hr0/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 @atlassian's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/olb55vh0) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/olb55vh0/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/atlassian')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/atlassian/1623889197185/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/atlassian
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Atlassian
@atlassian
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Atlassian.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @atlassian's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1351987572747202560/v_vDGtnX_400x400.png')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">AtomicNicos | @d_overcon co-organiser 🤖 AI Bot </div>
<div style="font-size: 15px">@atomicnicos bot</div>
</div>
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 [@atomicnicos's tweets](https://twitter.com/atomicnicos).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 221 |
| Short tweets | 452 |
| Tweets kept | 2576 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3mnuo591/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 @atomicnicos's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/n30kmifp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/n30kmifp/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/atomicnicos')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/atomicnicos/1616352732946/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/atomicnicos
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AtomicNicos | @d\_overcon co-organiser AI Bot
@atomicnicos bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @atomicnicos's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @atomicnicos's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1238289351861137408/k-sNIYOh_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">site specific carnivorous occurrence 🤖 AI Bot </div>
<div style="font-size: 15px">@atomicthumbs bot</div>
</div>
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 [@atomicthumbs's tweets](https://twitter.com/atomicthumbs).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3203 |
| Retweets | 1686 |
| Short tweets | 287 |
| Tweets kept | 1230 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2oe2pbns/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 @atomicthumbs's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3jgkhbgt) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3jgkhbgt/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/atomicthumbs')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/atomicthumbs/1614107568559/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/atomicthumbs
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
site specific carnivorous occurrence AI Bot
@atomicthumbs bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @atomicthumbs's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @atomicthumbs's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1363925362699337731/cAnWI7wL_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Atreyu Joker 🤖 AI Bot </div>
<div style="font-size: 15px">@atreyupilled bot</div>
</div>
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 [@atreyupilled's tweets](https://twitter.com/atreyupilled).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3233 |
| Retweets | 232 |
| Short tweets | 500 |
| Tweets kept | 2501 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1nd8rl11/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 @atreyupilled's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1gt351ez) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1gt351ez/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/atreyupilled')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/atreyupilled/1614099692275/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/atreyupilled
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Atreyu Joker AI Bot
@atreyupilled bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @atreyupilled's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @atreyupilled's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1323206624765448197/eqBniY_E_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">AtticScientist</div>
<div style="text-align: center; font-size: 14px;">@atticscientist</div>
</div>
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 AtticScientist.
| Data | AtticScientist |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 1 |
| Short tweets | 8 |
| Tweets kept | 3241 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3pvpbxir/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 @atticscientist's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ktckeg7n) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ktckeg7n/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/atticscientist')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/atticscientist/1625552752637/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/atticscientist
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
AtticScientist
@atticscientist
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from AtticScientist.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @atticscientist's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1137529671690706949/nKjy1T77_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Himboclese,,,, of ACAB 🤖 AI Bot </div>
<div style="font-size: 15px">@august77lng bot</div>
</div>
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 [@august77lng's tweets](https://twitter.com/august77lng).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3151 |
| Retweets | 2357 |
| Short tweets | 114 |
| Tweets kept | 680 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ly74f65/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 @august77lng's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ck3jotc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ck3jotc/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/august77lng')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/august77lng/1614115355848/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/august77lng
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Himboclese,,,, of ACAB AI Bot
@august77lng bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @august77lng's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @august77lng's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1400755065644802052/ZaFgV9cj_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Haruka</div>
<div style="text-align: center; font-size: 14px;">@aumgensokyo</div>
</div>
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 Haruka.
| Data | Haruka |
| --- | --- |
| Tweets downloaded | 3227 |
| Retweets | 632 |
| Short tweets | 494 |
| Tweets kept | 2101 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/lpttj7aa/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 @aumgensokyo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/mxwc1h4k) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/mxwc1h4k/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/aumgensokyo')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/aumgensokyo/1624891912864/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/aumgensokyo
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Haruka
@aumgensokyo
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Haruka.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @aumgensokyo's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1298871483310190593/Bev51BER_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Austen Allred 🤖 AI Bot </div>
<div style="font-size: 15px">@austen bot</div>
</div>
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 [@austen's tweets](https://twitter.com/austen).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 112 |
| Short tweets | 465 |
| Tweets kept | 2672 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wyv57q5p/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 @austen's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2jltqo1x) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2jltqo1x/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/austen')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/austen/1617725908050/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/austen
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Austen Allred AI Bot
@austen bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @austen's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @austen's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1356365325663666177/fgKMKMGD_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">April 🤖 AI Bot </div>
<div style="font-size: 15px">@autogynefiles bot</div>
</div>
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 [@autogynefiles's tweets](https://twitter.com/autogynefiles).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3202 |
| Retweets | 901 |
| Short tweets | 360 |
| Tweets kept | 1941 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2jw5booy/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 @autogynefiles's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1b1l87sd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1b1l87sd/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/autogynefiles')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/autogynefiles
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
April AI Bot
@autogynefiles bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @autogynefiles's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @autogynefiles's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1357641829710241792/93qv43Iv_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">mika 🤖 AI Bot </div>
<div style="font-size: 15px">@autophagian bot</div>
</div>
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 [@autophagian's tweets](https://twitter.com/autophagian).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3237 |
| Retweets | 586 |
| Short tweets | 380 |
| Tweets kept | 2271 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/16w10v81/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 @autophagian's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3dalb9ir) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3dalb9ir/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/autophagian')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/autophagian/1616669932241/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/autophagian
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
mika AI Bot
@autophagian bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @autophagian's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @autophagian's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1192531689060200448/S9KoiehJ_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1294927107605356544/CVXTlp9y_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1468895545007775746/NIWzzmye_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Speedcafe.com & Formula One World & Autosport</div>
<div style="text-align: center; font-size: 14px;">@autosport-formulaoneworld-speedcafe</div>
</div>
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 Speedcafe.com & Formula One World & Autosport.
| Data | Speedcafe.com | Formula One World | Autosport |
| --- | --- | --- | --- |
| Tweets downloaded | 3250 | 3247 | 3250 |
| Retweets | 0 | 2778 | 52 |
| Short tweets | 3 | 178 | 15 |
| Tweets kept | 3247 | 291 | 3183 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/kcn72bl0/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 @autosport-formulaoneworld-speedcafe's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2fq703qs) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2fq703qs/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/autosport-formulaoneworld-speedcafe')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/autosport-formulaoneworld-speedcafe/1642217065882/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/autosport-formulaoneworld-speedcafe
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
URL & Formula One World & Autosport
@autosport-formulaoneworld-speedcafe
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from URL & Formula One World & Autosport.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @autosport-formulaoneworld-speedcafe's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1046984086026371073/zN5I5g0G_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">deep dish pizza eater 🤖 AI Bot </div>
<div style="font-size: 15px">@avantredguard bot</div>
</div>
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 [@avantredguard's tweets](https://twitter.com/avantredguard).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3173 |
| Retweets | 851 |
| Short tweets | 386 |
| Tweets kept | 1936 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ybevoux/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 @avantredguard's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/10kbxe0u) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/10kbxe0u/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/avantredguard')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/avantredguard/1614096845665/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/avantredguard
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
deep dish pizza eater AI Bot
@avantredguard bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @avantredguard's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @avantredguard's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1368753714568327168/oh6BSjqX_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">AverageVermontSmasher</div>
<div style="text-align: center; font-size: 14px;">@averagesmasher</div>
</div>
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 AverageVermontSmasher.
| Data | AverageVermontSmasher |
| --- | --- |
| Tweets downloaded | 41 |
| Retweets | 0 |
| Short tweets | 2 |
| Tweets kept | 39 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/auyr340s/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 @averagesmasher's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2qnfjchi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2qnfjchi/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/averagesmasher')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/averagesmasher/1625924846625/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/averagesmasher
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
AverageVermontSmasher
@averagesmasher
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from AverageVermontSmasher.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @averagesmasher's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/847818629840228354/VXyQHfn0_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1427457256958930948/J2FGNejT_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1354274870264266753/9D_FgIsC_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">wint & Chet & ac</div>
<div style="text-align: center; font-size: 14px;">@avgmeat-dril-methwaffles</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from wint & Chet & ac.
| Data | wint | Chet | ac |
| --- | --- | --- | --- |
| Tweets downloaded | 3189 | 2471 | 3167 |
| Retweets | 468 | 748 | 209 |
| Short tweets | 310 | 299 | 816 |
| Tweets kept | 2411 | 1424 | 2142 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1gv4gxjf/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 @avgmeat-dril-methwaffles's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3dg2j508) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3dg2j508/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/avgmeat-dril-methwaffles')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/avgmeat-dril-methwaffles/1631963152302/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/avgmeat-dril-methwaffles
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
wint & Chet & ac
@avgmeat-dril-methwaffles
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from wint & Chet & ac.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @avgmeat-dril-methwaffles's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/847818629840228354/VXyQHfn0_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1255544995190321152/2ZTJAJ7K_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1354274870264266753/9D_FgIsC_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">wint & megan & ac</div>
<div style="text-align: center; font-size: 14px;">@avgmeat-dril-slitthroatz</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from wint & megan & ac.
| Data | wint | megan | ac |
| --- | --- | --- | --- |
| Tweets downloaded | 3186 | 3175 | 3167 |
| Retweets | 466 | 190 | 209 |
| Short tweets | 310 | 920 | 816 |
| Tweets kept | 2410 | 2065 | 2142 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1p8qo7jh/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 @avgmeat-dril-slitthroatz's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/29pi8slb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/29pi8slb/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/avgmeat-dril-slitthroatz')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/avgmeat-dril-slitthroatz
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
wint & megan & ac
@avgmeat-dril-slitthroatz
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from wint & megan & ac.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @avgmeat-dril-slitthroatz's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/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'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1072899793054121990/QPK0Qv46_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Avril Lavigne 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@avrillavigne bot</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on [@avrillavigne's tweets](https://twitter.com/avrillavigne).
<table style='border-width:0'>
<thead style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
<th style='border-width:0'>Data</th>
<th style='border-width:0'>Quantity</th>
</tr>
</thead>
<tbody style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Tweets downloaded</td>
<td style='border-width:0'>3112</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Retweets</td>
<td style='border-width:0'>766</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Short tweets</td>
<td style='border-width:0'>306</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2040</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/f4t9xd39/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @avrillavigne's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/2rkl8zq3) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/2rkl8zq3/artifacts) is logged and versioned.
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for text generation:
<pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline
generator = pipeline(<span style="color:#FF9800">'text-generation'</span>,
model=<span style="color:#FF9800">'huggingtweets/avrillavigne'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### 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>
[](https://twitter.com/intent/follow?screen_name=borisdayma)
<section class='prose'>
For more details, visit the project repository.
</section>
[](https://github.com/borisdayma/huggingtweets)
<!--- random size file -->
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/avrillavigne/1601265702542/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/avrillavigne
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<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'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Avril Lavigne AI Bot </div>
<div style="font-size: 15px; color: #657786">@avrillavigne bot</div>
</div>
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
## How does it work?
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
## Training data
The model was trained on @avrillavigne's tweets.
<table style='border-width:0'>
<thead style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
<th style='border-width:0'>Data</th>
<th style='border-width:0'>Quantity</th>
</tr>
</thead>
<tbody style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Tweets downloaded</td>
<td style='border-width:0'>3112</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Retweets</td>
<td style='border-width:0'>766</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Short tweets</td>
<td style='border-width:0'>306</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2040</td>
</tr>
</tbody>
</table>
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
## Training procedure
The model is based on a pre-trained GPT-2 which is fine-tuned on @avrillavigne's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for text generation:
<pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline
generator = pipeline(<span style="color:#FF9800">'text-generation'</span>,
model=<span style="color:#FF9800">'huggingtweets/avrillavigne'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### Limitations and bias
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
</section>
![Follow](URL
<section class='prose'>
For more details, visit the project repository.
</section>
![GitHub stars](URL
|
[
"## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on @avrillavigne's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3112</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>766</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>306</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2040</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @avrillavigne's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## Intended uses & limitations",
"### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/avrillavigne'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on @avrillavigne's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3112</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>766</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>306</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2040</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @avrillavigne's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## Intended uses & limitations",
"### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/avrillavigne'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1336485377461219329/VqCT-VZ0_400x400.png')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">A Wondering of Eye 🤖 AI Bot </div>
<div style="font-size: 15px">@awanderingi bot</div>
</div>
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 [@awanderingi's tweets](https://twitter.com/awanderingi).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1641 |
| Retweets | 202 |
| Short tweets | 166 |
| Tweets kept | 1273 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3pnmx279/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 @awanderingi's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/19ne4lje) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/19ne4lje/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/awanderingi')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/awanderingi/1616615246454/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/awanderingi
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
A Wondering of Eye AI Bot
@awanderingi bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @awanderingi's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @awanderingi's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1374488661673185281/l071mOH1_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Chris 🤖 AI Bot </div>
<div style="font-size: 15px">@awaythrow8 bot</div>
</div>
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 [@awaythrow8's tweets](https://twitter.com/awaythrow8).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3060 |
| Retweets | 230 |
| Short tweets | 469 |
| Tweets kept | 2361 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/18vas1gq/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 @awaythrow8's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ju36rrz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ju36rrz/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/awaythrow8')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/awaythrow8/1617904264202/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/awaythrow8
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Chris AI Bot
@awaythrow8 bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @awaythrow8's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @awaythrow8's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1402029332516773888/oJJ69stf_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Axel! ♠️</div>
<div style="text-align: center; font-size: 14px;">@axel_hugsky</div>
</div>
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 Axel! ♠️.
| Data | Axel! ♠️ |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 529 |
| Short tweets | 1491 |
| Tweets kept | 1224 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ox7p0bd/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 @axel_hugsky's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/rrwwxdal) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/rrwwxdal/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/axel_hugsky')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/axel_hugsky/1624473007421/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/axel_hugsky
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Axel! ️
@axel\_hugsky
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Axel! ️.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @axel\_hugsky's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1362332889204015104/Rk5Iloej_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">lillie! 🤖 AI Bot </div>
<div style="font-size: 15px">@axialcatwalk bot</div>
</div>
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 [@axialcatwalk's tweets](https://twitter.com/axialcatwalk).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1153 |
| Retweets | 244 |
| Short tweets | 197 |
| Tweets kept | 712 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/thgvv1za/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 @axialcatwalk's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3mvj3lqy) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3mvj3lqy/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/axialcatwalk')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/axialcatwalk/1614103612518/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/axialcatwalk
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
lillie! AI Bot
@axialcatwalk bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @axialcatwalk's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @axialcatwalk's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1216804050991108097/kOy4RwPD_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Axiom of Rock House</div>
<div style="text-align: center; font-size: 14px;">@axiaofficial</div>
</div>
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 Axiom of Rock House.
| Data | Axiom of Rock House |
| --- | --- |
| Tweets downloaded | 1455 |
| Retweets | 432 |
| Short tweets | 146 |
| Tweets kept | 877 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1gs7ydag/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 @axiaofficial's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/wa3qdf22) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/wa3qdf22/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/axiaofficial')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/axiaofficial/1627065097228/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/axiaofficial
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Axiom of Rock House
@axiaofficial
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Axiom of Rock House.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @axiaofficial's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1334136134234849280/XgE0O39a_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1480842681182220288/ywam5sXK_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1480168235417083905/Kp8uyXIy_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">池澤あやか / いけあや & ちょまど🎀💻エンジニア兼漫画家 & 職業「戸倉彩」👩💻とくあや</div>
<div style="text-align: center; font-size: 14px;">@ayatokura-chomado-ikeay</div>
</div>
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 池澤あやか / いけあや & ちょまど🎀💻エンジニア兼漫画家 & 職業「戸倉彩」👩💻とくあや.
| Data | 池澤あやか / いけあや | ちょまど🎀💻エンジニア兼漫画家 | 職業「戸倉彩」👩💻とくあや |
| --- | --- | --- | --- |
| Tweets downloaded | 3250 | 3245 | 3249 |
| Retweets | 224 | 717 | 1266 |
| Short tweets | 2813 | 867 | 1036 |
| Tweets kept | 213 | 1661 | 947 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2rhguk5h/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 @ayatokura-chomado-ikeay's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/34bxjwb8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/34bxjwb8/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/ayatokura-chomado-ikeay')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/ayatokura-chomado-ikeay/1642462957980/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/ayatokura-chomado-ikeay
| null |
[
"huggingtweets",
"en",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#huggingtweets #en #region-us
|
AI CYBORG
池澤あやか / いけあや & ちょまどエンジニア兼漫画家 & 職業「戸倉彩」とくあや
@ayatokura-chomado-ikeay
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from 池澤あやか / いけあや & ちょまどエンジニア兼漫画家 & 職業「戸倉彩」とくあや.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @ayatokura-chomado-ikeay's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#huggingtweets #en #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1385798100430868486/QusggrjY_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Azul Crescent</div>
<div style="text-align: center; font-size: 14px;">@azulcrescent</div>
</div>
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 Azul Crescent.
| Data | Azul Crescent |
| --- | --- |
| Tweets downloaded | 1039 |
| Retweets | 9 |
| Short tweets | 92 |
| Tweets kept | 938 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3hna2b57/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 @azulcrescent's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/23ccs0az) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/23ccs0az/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/azulcrescent')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/azulcrescent/1621537893204/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/azulcrescent
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Azul Crescent
@azulcrescent
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Azul Crescent.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @azulcrescent's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/637912443281272832/dAsVKNNU_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Azzam Ameen</div>
<div style="text-align: center; font-size: 14px;">@azzamameen</div>
</div>
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 Azzam Ameen.
| Data | Azzam Ameen |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 2828 |
| Short tweets | 18 |
| Tweets kept | 401 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/4gs8vmx7/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 @azzamameen's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1yezh3jh) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1yezh3jh/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/azzamameen')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/azzamameen/1622350298746/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/azzamameen
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Azzam Ameen
@azzamameen
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Azzam Ameen.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @azzamameen's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1327803375144955904/CPYJCjKe_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Anupam Gupta 🤖 AI Bot </div>
<div style="font-size: 15px">@b50 bot</div>
</div>
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 [@b50's tweets](https://twitter.com/b50).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3243 |
| Retweets | 438 |
| Short tweets | 283 |
| Tweets kept | 2522 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3sfdctj9/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 @b50's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3jrmsda8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3jrmsda8/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/b50')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/b50/1617343613656/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/b50
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Anupam Gupta AI Bot
@b50 bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @b50's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @b50's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1313658315767910400/bCaV9qVB_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">BadBunny 💢</div>
<div style="text-align: center; font-size: 14px;">@badbunnytwitch</div>
</div>
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 BadBunny 💢.
| Data | BadBunny 💢 |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 177 |
| Short tweets | 1018 |
| Tweets kept | 2051 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/jrtmk7ym/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 @badbunnytwitch's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/r2t5349l) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/r2t5349l/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/badbunnytwitch')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/badbunnytwitch/1643762099951/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/badbunnytwitch
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
BadBunny
@badbunnytwitch
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from BadBunny .
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @badbunnytwitch's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1351393562764709889/ZS3zxvTT_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">c a l (b r b)</div>
<div style="text-align: center; font-size: 14px;">@badsleepwelll</div>
</div>
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 c a l (b r b).
| Data | c a l (b r b) |
| --- | --- |
| Tweets downloaded | 3216 |
| Retweets | 692 |
| Short tweets | 533 |
| Tweets kept | 1991 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3cl0ip5y/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 @badsleepwelll's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/cz16cnsk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/cz16cnsk/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/badsleepwelll')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/badsleepwelll/1621366332895/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/badsleepwelll
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
c a l (b r b)
@badsleepwelll
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from c a l (b r b).
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @badsleepwelll's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/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'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/938437222948265985/NhKG-UQd_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Baidu Inc. 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@baidu_inc bot</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on [@baidu_inc's tweets](https://twitter.com/baidu_inc).
<table style='border-width:0'>
<thead style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
<th style='border-width:0'>Data</th>
<th style='border-width:0'>Quantity</th>
</tr>
</thead>
<tbody style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Tweets downloaded</td>
<td style='border-width:0'>1364</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Retweets</td>
<td style='border-width:0'>254</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Short tweets</td>
<td style='border-width:0'>7</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1103</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/7tblrg78/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @baidu_inc's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/23h9pyfk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/23h9pyfk/artifacts) is logged and versioned.
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for text generation:
<pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline
generator = pipeline(<span style="color:#FF9800">'text-generation'</span>,
model=<span style="color:#FF9800">'huggingtweets/baidu_inc'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### 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>
[](https://twitter.com/intent/follow?screen_name=borisdayma)
<section class='prose'>
For more details, visit the project repository.
</section>
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/baidu_inc/1609715579938/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/baidu_inc
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<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'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Baidu Inc. AI Bot </div>
<div style="font-size: 15px; color: #657786">@baidu_inc bot</div>
</div>
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
## How does it work?
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
## Training data
The model was trained on @baidu_inc's tweets.
<table style='border-width:0'>
<thead style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
<th style='border-width:0'>Data</th>
<th style='border-width:0'>Quantity</th>
</tr>
</thead>
<tbody style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Tweets downloaded</td>
<td style='border-width:0'>1364</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Retweets</td>
<td style='border-width:0'>254</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Short tweets</td>
<td style='border-width:0'>7</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1103</td>
</tr>
</tbody>
</table>
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
## Training procedure
The model is based on a pre-trained GPT-2 which is fine-tuned on @baidu_inc's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for text generation:
<pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline
generator = pipeline(<span style="color:#FF9800">'text-generation'</span>,
model=<span style="color:#FF9800">'huggingtweets/baidu_inc'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### Limitations and bias
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
</section>
![Follow](URL
<section class='prose'>
For more details, visit the project repository.
</section>
![GitHub stars](URL
|
[
"## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on @baidu_inc's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>1364</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>254</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>7</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1103</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @baidu_inc's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## Intended uses & limitations",
"### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/baidu_inc'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on @baidu_inc's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>1364</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>254</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>7</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1103</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @baidu_inc's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## Intended uses & limitations",
"### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/baidu_inc'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1406974882919813128/LOUb2m4R_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Balaji</div>
<div style="text-align: center; font-size: 14px;">@balajis</div>
</div>
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 Balaji.
| Data | Balaji |
| --- | --- |
| Tweets downloaded | 3243 |
| Retweets | 849 |
| Short tweets | 54 |
| Tweets kept | 2340 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/bioobb8j/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 @balajis's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1iql7y69) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1iql7y69/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/balajis')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/balajis/1668967580599/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/balajis
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Balaji
@balajis
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Balaji.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @balajis's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1372581144873472004/jc7AWhim_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">maddie 🤖 AI Bot </div>
<div style="font-size: 15px">@balanchinarinaa bot</div>
</div>
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 [@balanchinarinaa's tweets](https://twitter.com/balanchinarinaa).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3187 |
| Retweets | 358 |
| Short tweets | 232 |
| Tweets kept | 2597 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2u50n6tw/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 @balanchinarinaa's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3klfmyxg) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3klfmyxg/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/balanchinarinaa')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/balanchinarinaa/1616741385918/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/balanchinarinaa
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
maddie AI Bot
@balanchinarinaa bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @balanchinarinaa's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @balanchinarinaa's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1379189650556911619/EiZklugS_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1441278016164818955/T-PDXXvg_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1438447321604313089/5_lZmeyb_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Wait Forgot & Balco - Special Boperative & Liyrex</div>
<div style="text-align: center; font-size: 14px;">@balcobops-liyrex_irl-waitforgot</div>
</div>
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 Wait Forgot & Balco - Special Boperative & Liyrex.
| Data | Wait Forgot | Balco - Special Boperative | Liyrex |
| --- | --- | --- | --- |
| Tweets downloaded | 3194 | 1171 | 3189 |
| Retweets | 1294 | 129 | 1587 |
| Short tweets | 285 | 122 | 279 |
| Tweets kept | 1615 | 920 | 1323 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/371suxoa/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 @balcobops-liyrex_irl-waitforgot's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/bj54dpp8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/bj54dpp8/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/balcobops-liyrex_irl-waitforgot')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/balcobops-liyrex_irl-waitforgot/1632888280266/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/balcobops-liyrex_irl-waitforgot
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Wait Forgot & Balco - Special Boperative & Liyrex
@balcobops-liyrex\_irl-waitforgot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Wait Forgot & Balco - Special Boperative & Liyrex.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @balcobops-liyrex\_irl-waitforgot's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1349590876931072001/kmtebQA4_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Banjodogg 🤖 AI Bot </div>
<div style="font-size: 15px">@banjocatt bot</div>
</div>
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 [@banjocatt's tweets](https://twitter.com/banjocatt).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1384 |
| Retweets | 27 |
| Short tweets | 150 |
| Tweets kept | 1207 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3u0ms4j5/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 @banjocatt's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2me74qbw) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2me74qbw/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/banjocatt')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/banjocatt
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Banjodogg AI Bot
@banjocatt bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @banjocatt's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @banjocatt's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/988775660163252226/XpgonN0X_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1329647526807543809/2SGvnHYV_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/988775660163252226/XpgonN0X_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Bill Gates & Barack Obama</div>
<div style="text-align: center; font-size: 14px;">@barackobama-billgates</div>
</div>
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 Bill Gates & Barack Obama.
| Data | Bill Gates | Barack Obama |
| --- | --- | --- |
| Tweets downloaded | 3207 | 3214 |
| Retweets | 186 | 353 |
| Short tweets | 6 | 25 |
| Tweets kept | 3015 | 2836 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/7wwgduen/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 @barackobama-billgates's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1afk1edu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1afk1edu/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/barackobama-billgates')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/barackobama-billgates/1620428621185/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/barackobama-billgates
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Bill Gates & Barack Obama
@barackobama-billgates
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Bill Gates & Barack Obama.
Data: Tweets downloaded, Bill Gates: 3207, Barack Obama: 3214
Data: Retweets, Bill Gates: 186, Barack Obama: 353
Data: Short tweets, Bill Gates: 6, Barack Obama: 25
Data: Tweets kept, Bill Gates: 3015, Barack Obama: 2836
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @barackobama-billgates's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1383184766959120385/MM9DHPWC_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1329647526807543809/2SGvnHYV_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Barack Obama</div>
<div style="text-align: center; font-size: 14px;">@barackobama-elonmusk</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Elon Musk & Barack Obama.
| Data | Elon Musk | Barack Obama |
| --- | --- | --- |
| Tweets downloaded | 1021 | 3234 |
| Retweets | 52 | 353 |
| Short tweets | 268 | 25 |
| Tweets kept | 701 | 2856 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/osj70w7o/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 @barackobama-elonmusk's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/21huy5w1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/21huy5w1/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/barackobama-elonmusk')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/barackobama-elonmusk
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Elon Musk & Barack Obama
@barackobama-elonmusk
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Elon Musk & Barack Obama.
Data: Tweets downloaded, Elon Musk: 1021, Barack Obama: 3234
Data: Retweets, Elon Musk: 52, Barack Obama: 353
Data: Short tweets, Elon Musk: 268, Barack Obama: 25
Data: Tweets kept, Elon Musk: 701, Barack Obama: 2856
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @barackobama-elonmusk's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1263334523187671040/_hCX0Xmb_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1396877840763719684/88N2DjSH_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1329647526807543809/2SGvnHYV_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">University of OnFoeNem 🇭🇹 & Karlous & Barack Obama</div>
<div style="text-align: center; font-size: 14px;">@barackobama-karlousm-uofofn</div>
</div>
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 University of OnFoeNem 🇭🇹 & Karlous & Barack Obama.
| Data | University of OnFoeNem 🇭🇹 | Karlous | Barack Obama |
| --- | --- | --- | --- |
| Tweets downloaded | 3153 | 3207 | 3249 |
| Retweets | 457 | 1736 | 352 |
| Short tweets | 576 | 175 | 25 |
| Tweets kept | 2120 | 1296 | 2872 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ghcjele/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 @barackobama-karlousm-uofofn's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/24cklty4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/24cklty4/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/barackobama-karlousm-uofofn')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/barackobama-karlousm-uofofn/1625032946203/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/barackobama-karlousm-uofofn
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
University of OnFoeNem 🇭🇹 & Karlous & Barack Obama
@barackobama-karlousm-uofofn
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from University of OnFoeNem 🇭🇹 & Karlous & Barack Obama.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @barackobama-karlousm-uofofn's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1329647526807543809/2SGvnHYV_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Barack Obama</div>
<div style="text-align: center; font-size: 14px;">@barackobama</div>
</div>
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 Barack Obama.
| Data | Barack Obama |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 319 |
| Short tweets | 17 |
| Tweets kept | 2914 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/tjijp9lf/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 @barackobama's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2jfeof85) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2jfeof85/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/barackobama')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/barackobama
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
AI BOT
Barack Obama
@barackobama
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Barack Obama.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @barackobama's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1362428223611670531/c08rJGpf_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">𓅅 zack 𓅅 🤖 AI Bot </div>
<div style="font-size: 15px">@barzoople bot</div>
</div>
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 [@barzoople's tweets](https://twitter.com/barzoople).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1787 |
| Retweets | 162 |
| Short tweets | 225 |
| Tweets kept | 1400 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/32bz3nyh/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 @barzoople's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ezszw7hb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ezszw7hb/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/barzoople')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/barzoople/1614101635102/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/barzoople
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
𓅅 zack 𓅅 AI Bot
@barzoople bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @barzoople's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @barzoople's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1333015562704785412/cYcTBrHF_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Basedgamerboi 🤖 AI Bot </div>
<div style="font-size: 15px">@basedgamerboi bot</div>
</div>
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 [@basedgamerboi's tweets](https://twitter.com/basedgamerboi).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3186 |
| Retweets | 1305 |
| Short tweets | 603 |
| Tweets kept | 1278 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/387726ql/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 @basedgamerboi's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/36dqi3qi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/36dqi3qi/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/basedgamerboi')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/basedgamerboi/1618147489025/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/basedgamerboi
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Basedgamerboi AI Bot
@basedgamerboi bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @basedgamerboi's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @basedgamerboi's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1372264552029679619/liCuKg_U_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Mel Andrews 🤖 AI Bot </div>
<div style="font-size: 15px">@bayesianboy bot</div>
</div>
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 [@bayesianboy's tweets](https://twitter.com/bayesianboy).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 216 |
| Short tweets | 589 |
| Tweets kept | 2439 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3shc5anf/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 @bayesianboy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3rrpzuts) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3rrpzuts/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/bayesianboy')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bayesianboy/1616641444174/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bayesianboy
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Mel Andrews AI Bot
@bayesianboy bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @bayesianboy's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bayesianboy's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1358510866371661830/rxzOoe9A_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1073647682487410688/2yrbD4RY_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1334065878917390338/V6Eh8ZJn_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Miss Gbadamosi ♠ & ♠Jenny Summers♠ & ♠️MsWhite♠️</div>
<div style="text-align: center; font-size: 14px;">@bbcqos-fitslut63-kellyg_official</div>
</div>
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 Miss Gbadamosi ♠ & ♠Jenny Summers♠ & ♠️MsWhite♠️.
| Data | Miss Gbadamosi ♠ | ♠Jenny Summers♠ | ♠️MsWhite♠️ |
| --- | --- | --- | --- |
| Tweets downloaded | 480 | 882 | 3063 |
| Retweets | 117 | 55 | 1391 |
| Short tweets | 154 | 483 | 230 |
| Tweets kept | 209 | 344 | 1442 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3rzzq99i/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 @bbcqos-fitslut63-kellyg_official's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/xd2e2hom) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/xd2e2hom/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/bbcqos-fitslut63-kellyg_official')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bbcqos-fitslut63-kellyg_official/1629198375751/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bbcqos-fitslut63-kellyg_official
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Miss Gbadamosi & Jenny Summers & ️MsWhite️
@bbcqos-fitslut63-kellyg\_official
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Miss Gbadamosi & Jenny Summers & ️MsWhite️.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bbcqos-fitslut63-kellyg\_official's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1073647682487410688/2yrbD4RY_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">♠Jenny Summers♠</div>
<div style="text-align: center; font-size: 14px;">@bbcqos</div>
</div>
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 ♠Jenny Summers♠.
| Data | ♠Jenny Summers♠ |
| --- | --- |
| Tweets downloaded | 882 |
| Retweets | 55 |
| Short tweets | 483 |
| Tweets kept | 344 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2uwts9v5/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 @bbcqos's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1shy0ous) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1shy0ous/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/bbcqos')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bbcqos/1629197549972/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bbcqos
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Jenny Summers
@bbcqos
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Jenny Summers.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bbcqos's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/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'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1310015308833452032/VWjqd52U_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Benjamin Dreyer 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@bcdreyer bot</div>
</div>
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 [@bcdreyer's tweets](https://twitter.com/bcdreyer).
<table style='border-width:0'>
<thead style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
<th style='border-width:0'>Data</th>
<th style='border-width:0'>Quantity</th>
</tr>
</thead>
<tbody style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Tweets downloaded</td>
<td style='border-width:0'>3213</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Retweets</td>
<td style='border-width:0'>594</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Short tweets</td>
<td style='border-width:0'>410</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2209</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/2r28be7y/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 @bcdreyer's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/1uihefwp) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/1uihefwp/artifacts) is logged and versioned.
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for text generation:
<pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline
generator = pipeline(<span style="color:#FF9800">'text-generation'</span>,
model=<span style="color:#FF9800">'huggingtweets/bcdreyer'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### 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>
[](https://twitter.com/intent/follow?screen_name=borisdayma)
<section class='prose'>
For more details, visit the project repository.
</section>
[](https://github.com/borisdayma/huggingtweets)
<!--- random size file -->
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bcdreyer/1601263745684/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bcdreyer
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<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'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Benjamin Dreyer AI Bot </div>
<div style="font-size: 15px; color: #657786">@bcdreyer bot</div>
</div>
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
## How does it work?
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
## Training data
The model was trained on @bcdreyer's tweets.
<table style='border-width:0'>
<thead style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
<th style='border-width:0'>Data</th>
<th style='border-width:0'>Quantity</th>
</tr>
</thead>
<tbody style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Tweets downloaded</td>
<td style='border-width:0'>3213</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Retweets</td>
<td style='border-width:0'>594</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Short tweets</td>
<td style='border-width:0'>410</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2209</td>
</tr>
</tbody>
</table>
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
## Training procedure
The model is based on a pre-trained GPT-2 which is fine-tuned on @bcdreyer's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for text generation:
<pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline
generator = pipeline(<span style="color:#FF9800">'text-generation'</span>,
model=<span style="color:#FF9800">'huggingtweets/bcdreyer'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### Limitations and bias
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
</section>
![Follow](URL
<section class='prose'>
For more details, visit the project repository.
</section>
![GitHub stars](URL
|
[
"## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on @bcdreyer's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3213</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>594</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>410</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2209</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @bcdreyer's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## Intended uses & limitations",
"### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/bcdreyer'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on @bcdreyer's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3213</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>594</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>410</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2209</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @bcdreyer's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## Intended uses & limitations",
"### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/bcdreyer'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1440017111531855879/A4p6F07H_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1440481469231558659/ZjEcoltA_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1435265846436409346/yAV2qzDs_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">6529 & Beanie & Loopify 🧙♂️</div>
<div style="text-align: center; font-size: 14px;">@beaniemaxi-loopifyyy-punk6529</div>
</div>
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 6529 & Beanie & Loopify 🧙♂️.
| Data | 6529 | Beanie | Loopify 🧙♂️ |
| --- | --- | --- | --- |
| Tweets downloaded | 3249 | 3250 | 3249 |
| Retweets | 939 | 391 | 179 |
| Short tweets | 525 | 559 | 1194 |
| Tweets kept | 1785 | 2300 | 1876 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ejmosjg/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 @beaniemaxi-loopifyyy-punk6529's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/15k8d8xn) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/15k8d8xn/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/beaniemaxi-loopifyyy-punk6529')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/beaniemaxi-loopifyyy-punk6529
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
6529 & Beanie & Loopify ️
@beaniemaxi-loopifyyy-punk6529
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from 6529 & Beanie & Loopify ️.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @beaniemaxi-loopifyyy-punk6529's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1087417311361789958/Y1NvpnuX_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Dave Davies 🤖 AI Bot </div>
<div style="font-size: 15px">@beanstalkim bot</div>
</div>
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 [@beanstalkim's tweets](https://twitter.com/beanstalkim).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3214 |
| Retweets | 121 |
| Short tweets | 269 |
| Tweets kept | 2824 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/62fffrcn/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 @beanstalkim's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ulhjijc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ulhjijc/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/beanstalkim')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/beanstalkim/1619112801821/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/beanstalkim
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Dave Davies AI Bot
@beanstalkim bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @beanstalkim's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @beanstalkim's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1342942616195895303/13ml_Pdw_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Bee Boileau 🤖 AI Bot </div>
<div style="font-size: 15px">@beeboileau bot</div>
</div>
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 [@beeboileau's tweets](https://twitter.com/beeboileau).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 238 |
| Retweets | 26 |
| Short tweets | 18 |
| Tweets kept | 194 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/gjcxmczw/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 @beeboileau's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2antyc8f) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2antyc8f/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/beeboileau')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/beeboileau/1616684430388/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/beeboileau
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Bee Boileau AI Bot
@beeboileau bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @beeboileau's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @beeboileau's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/498860581072273408/q5v6iWVw_400x400.jpeg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Bee Movie Script</div>
<div style="text-align: center; font-size: 14px;">@beemoviescript</div>
</div>
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 Bee Movie Script.
| Data | Bee Movie Script |
| --- | --- |
| Tweets downloaded | 1427 |
| Retweets | 0 |
| Short tweets | 169 |
| Tweets kept | 1258 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/291me6fz/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 @beemoviescript's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3gtdvdf3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3gtdvdf3/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/beemoviescript')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/beemoviescript
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Bee Movie Script
@beemoviescript
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Bee Movie Script.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @beemoviescript's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1429115399975497731/JZdA725e_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1434240567001636864/BkVzkg7C_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1434228331315187712/IrO7AP6L_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">✨たち Tommy’s an Orbit 🌙 たち✨ & Goose & c!tubbo + glatt</div>
<div style="text-align: center; font-size: 14px;">@beesforbo-cafe_orbitinnit-weebbutt</div>
</div>
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 ✨たち Tommy’s an Orbit 🌙 たち✨ & Goose & c!tubbo + glatt.
| Data | ✨たち Tommy’s an Orbit 🌙 たち✨ | Goose | c!tubbo + glatt |
| --- | --- | --- | --- |
| Tweets downloaded | 2241 | 3243 | 3242 |
| Retweets | 1335 | 511 | 108 |
| Short tweets | 323 | 512 | 1198 |
| Tweets kept | 583 | 2220 | 1936 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/p0uk28zi/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 @beesforbo-cafe_orbitinnit-weebbutt's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/310986pt) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/310986pt/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/beesforbo-cafe_orbitinnit-weebbutt')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/beesforbo-cafe_orbitinnit-weebbutt/1630941920455/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/beesforbo-cafe_orbitinnit-weebbutt
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
たち Tommy’s an Orbit たち & Goose & c!tubbo + glatt
@beesforbo-cafe\_orbitinnit-weebbutt
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from たち Tommy’s an Orbit たち & Goose & c!tubbo + glatt.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @beesforbo-cafe\_orbitinnit-weebbutt's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1323398842268684289/_OAtj-z-_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Cate 🤖 AI Bot </div>
<div style="font-size: 15px">@beetleboxes bot</div>
</div>
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 [@beetleboxes's tweets](https://twitter.com/beetleboxes).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2817 |
| Retweets | 883 |
| Short tweets | 404 |
| Tweets kept | 1530 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/16fa37go/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 @beetleboxes's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2sjz20j3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2sjz20j3/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/beetleboxes')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/beetleboxes/1616645225701/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/beetleboxes
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Cate AI Bot
@beetleboxes bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @beetleboxes's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @beetleboxes's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1404753773939990533/2Ol60_sO_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Mom</div>
<div style="text-align: center; font-size: 14px;">@behemilf</div>
</div>
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 Mom.
| Data | Mom |
| --- | --- |
| Tweets downloaded | 3241 |
| Retweets | 858 |
| Short tweets | 346 |
| Tweets kept | 2037 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/34zvujdl/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 @behemilf's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ss8n55dy) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ss8n55dy/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/behemilf')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/behemilf
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Mom
@behemilf
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Mom.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @behemilf's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1348756593052176385/TjNU6-T__400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Evan (master saucier) 🤖 AI Bot </div>
<div style="font-size: 15px">@beingandslime bot</div>
</div>
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 [@beingandslime's tweets](https://twitter.com/beingandslime).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 55 |
| Short tweets | 473 |
| Tweets kept | 2717 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2hj6ebde/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 @beingandslime's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2vtowykv) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2vtowykv/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/beingandslime')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/beingandslime/1616648200015/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/beingandslime
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Evan (master saucier) AI Bot
@beingandslime bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @beingandslime's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @beingandslime's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1365987365525815304/uxdWurnN_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Anarcho-Moses 🐍 🤖 AI Bot </div>
<div style="font-size: 15px">@ben_r_hoffman bot</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on [@ben_r_hoffman's tweets](https://twitter.com/ben_r_hoffman).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 107 |
| Short tweets | 264 |
| Tweets kept | 2876 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/vlvpdufz/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ben_r_hoffman's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2nf4hyti) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2nf4hyti/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ben_r_hoffman')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/ben_r_hoffman/1618455389168/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/ben_r_hoffman
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Anarcho-Moses AI Bot
@ben\_r\_hoffman bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @ben\_r\_hoffman's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @ben\_r\_hoffman's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1045023385816686592/7wIqU8ZY_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ben Chestnut</div>
<div style="text-align: center; font-size: 14px;">@benchestnut</div>
</div>
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 Ben Chestnut.
| Data | Ben Chestnut |
| --- | --- |
| Tweets downloaded | 3229 |
| Retweets | 943 |
| Short tweets | 124 |
| Tweets kept | 2162 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/lyrugs4q/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 @benchestnut's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2u96gtbs) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2u96gtbs/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/benchestnut')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/benchestnut/1626453250687/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/benchestnut
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Ben Chestnut
@benchestnut
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Ben Chestnut.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @benchestnut's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/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'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('http://pbs.twimg.com/profile_images/845396356579508224/1ruiOPLI_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Benedict Evans 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@benedictevans bot</div>
</div>
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 [@benedictevans's tweets](https://twitter.com/benedictevans).
<table style='border-width:0'>
<thead style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
<th style='border-width:0'>Data</th>
<th style='border-width:0'>Quantity</th>
</tr>
</thead>
<tbody style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Tweets downloaded</td>
<td style='border-width:0'>3236</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Retweets</td>
<td style='border-width:0'>662</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Short tweets</td>
<td style='border-width:0'>174</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2400</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/3an7r50z/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 @benedictevans's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/1k099xll) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/1k099xll/artifacts) is logged and versioned.
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for text generation:
<pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline
generator = pipeline(<span style="color:#FF9800">'text-generation'</span>,
model=<span style="color:#FF9800">'huggingtweets/benedictevans'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### 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>
[](https://twitter.com/borisdayma)
<section class='prose'>
For more details, visit the project repository.
</section>
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo_share.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/benedictevans
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<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'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Benedict Evans AI Bot </div>
<div style="font-size: 15px; color: #657786">@benedictevans bot</div>
</div>
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
## How does it work?
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
## Training data
The model was trained on @benedictevans's tweets.
<table style='border-width:0'>
<thead style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
<th style='border-width:0'>Data</th>
<th style='border-width:0'>Quantity</th>
</tr>
</thead>
<tbody style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Tweets downloaded</td>
<td style='border-width:0'>3236</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Retweets</td>
<td style='border-width:0'>662</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Short tweets</td>
<td style='border-width:0'>174</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2400</td>
</tr>
</tbody>
</table>
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
## Training procedure
The model is based on a pre-trained GPT-2 which is fine-tuned on @benedictevans's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for text generation:
<pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline
generator = pipeline(<span style="color:#FF9800">'text-generation'</span>,
model=<span style="color:#FF9800">'huggingtweets/benedictevans'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### Limitations and bias
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
</section>
![Follow](URL
<section class='prose'>
For more details, visit the project repository.
</section>
![GitHub stars](URL
|
[
"## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on @benedictevans's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3236</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>662</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>174</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2400</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @benedictevans's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## Intended uses & limitations",
"### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/benedictevans'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on @benedictevans's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3236</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>662</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>174</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2400</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @benedictevans's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## Intended uses & limitations",
"### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/benedictevans'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1421907606105329672/ypXqcYtY_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Marc Benioff</div>
<div style="text-align: center; font-size: 14px;">@benioff</div>
</div>
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 Marc Benioff.
| Data | Marc Benioff |
| --- | --- |
| Tweets downloaded | 3242 |
| Retweets | 2645 |
| Short tweets | 67 |
| Tweets kept | 530 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1jmodnqz/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 @benioff's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/alpvpdqh) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/alpvpdqh/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/benioff')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/benioff
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Marc Benioff
@benioff
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Marc Benioff.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @benioff's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1241961365725540353/s1_Jg8dJ_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Steven Suptic 🤖 AI Bot </div>
<div style="font-size: 15px">@stevensuptic bot</div>
</div>
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 [@stevensuptic's tweets](https://twitter.com/stevensuptic).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3190 |
| Retweets | 211 |
| Short tweets | 507 |
| Tweets kept | 2472 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3cj525fz/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 @stevensuptic's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/55ewqb50) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/55ewqb50/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/stevensuptic')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/benjinaesen
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Steven Suptic AI Bot
@stevensuptic bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @stevensuptic's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @stevensuptic's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1108382380723597313/hDIUPnFe_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Ben Congdon 🤖 AI Bot </div>
<div style="font-size: 15px">@benrcongdon bot</div>
</div>
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 [@benrcongdon's tweets](https://twitter.com/benrcongdon).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3219 |
| Retweets | 394 |
| Short tweets | 515 |
| Tweets kept | 2310 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3aazmqd6/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 @benrcongdon's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/7zvkav4e) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/7zvkav4e/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/benrcongdon')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/benrcongdon/1616637140236/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/benrcongdon
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Ben Congdon AI Bot
@benrcongdon bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @benrcongdon's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @benrcongdon's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/573946913327878144/d67c1agn_400x400.jpeg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">bens-i kareem 🤖 AI Bot </div>
<div style="font-size: 15px">@benskerim bot</div>
</div>
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 [@benskerim's tweets](https://twitter.com/benskerim).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 546 |
| Retweets | 12 |
| Short tweets | 152 |
| Tweets kept | 382 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1vrptz87/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 @benskerim's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2tluib3u) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2tluib3u/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/benskerim')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/benskerim/1616795883644/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/benskerim
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
bens-i kareem AI Bot
@benskerim bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @benskerim's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @benskerim's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1340882135893364741/B3Xl8AlK_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Bentley Rubinstein 🤖 AI Bot </div>
<div style="font-size: 15px">@bentley bot</div>
</div>
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 [@bentley's tweets](https://twitter.com/bentley).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 119 |
| Retweets | 43 |
| Short tweets | 1 |
| Tweets kept | 75 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/exsqu0gt/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 @bentley's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/212vaa17) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/212vaa17/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/bentley')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bentley/1616467138651/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bentley
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Bentley Rubinstein AI Bot
@bentley bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @bentley's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bentley's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1097820307388334080/9ddg5F6v_400x400.png')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/847818629840228354/VXyQHfn0_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1278259160644227073/MfCyF7CG_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Bernie Sanders & wint & CNN</div>
<div style="text-align: center; font-size: 14px;">@berniesanders-cnn-dril</div>
</div>
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 Bernie Sanders & wint & CNN.
| Data | Bernie Sanders | wint | CNN |
| --- | --- | --- | --- |
| Tweets downloaded | 3250 | 3229 | 3250 |
| Retweets | 429 | 473 | 30 |
| Short tweets | 10 | 300 | 6 |
| Tweets kept | 2811 | 2456 | 3214 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1yapgpjj/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 @berniesanders-cnn-dril's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1hmm651a) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1hmm651a/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/berniesanders-cnn-dril')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/berniesanders-cnn-dril/1646127802129/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/berniesanders-cnn-dril
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Bernie Sanders & wint & CNN
@berniesanders-cnn-dril
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Bernie Sanders & wint & CNN.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @berniesanders-cnn-dril's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/794725967948181506/Zn4x_F6i_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/794619281271033856/Fs0QQaH7_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1097820307388334080/9ddg5F6v_400x400.png')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Coffee Burger & Bernie Sanders & Bernie Sanders</div>
<div style="text-align: center; font-size: 14px;">@berniesanders-coffee__burger-sensanders</div>
</div>
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 Coffee Burger & Bernie Sanders & Bernie Sanders.
| Data | Coffee Burger | Bernie Sanders | Bernie Sanders |
| --- | --- | --- | --- |
| Tweets downloaded | 2471 | 3249 | 3250 |
| Retweets | 525 | 296 | 429 |
| Short tweets | 337 | 5 | 10 |
| Tweets kept | 1609 | 2948 | 2811 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2k4t7tx8/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 @berniesanders-coffee__burger-sensanders's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/31ey7s5h) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/31ey7s5h/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/berniesanders-coffee__burger-sensanders')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/berniesanders-coffee__burger-sensanders
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Coffee Burger & Bernie Sanders & Bernie Sanders
@berniesanders-coffee\_\_burger-sensanders
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Coffee Burger & Bernie Sanders & Bernie Sanders.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @berniesanders-coffee\_\_burger-sensanders's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/794725967948181506/Zn4x_F6i_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1097820307388334080/9ddg5F6v_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Coffee Burger & Bernie Sanders</div>
<div style="text-align: center; font-size: 14px;">@berniesanders-coffee__burger</div>
</div>
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 Coffee Burger & Bernie Sanders.
| Data | Coffee Burger | Bernie Sanders |
| --- | --- | --- |
| Tweets downloaded | 2471 | 3250 |
| Retweets | 525 | 429 |
| Short tweets | 337 | 10 |
| Tweets kept | 1609 | 2811 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ltwd1tj1/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 @berniesanders-coffee__burger's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/121buw7a) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/121buw7a/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/berniesanders-coffee__burger')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/berniesanders-coffee__burger
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Coffee Burger & Bernie Sanders
@berniesanders-coffee\_\_burger
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Coffee Burger & Bernie Sanders.
Data: Tweets downloaded, Coffee Burger: 2471, Bernie Sanders: 3250
Data: Retweets, Coffee Burger: 525, Bernie Sanders: 429
Data: Short tweets, Coffee Burger: 337, Bernie Sanders: 10
Data: Tweets kept, Coffee Burger: 1609, Bernie Sanders: 2811
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @berniesanders-coffee\_\_burger's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/847818629840228354/VXyQHfn0_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1097820307388334080/9ddg5F6v_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">wint & Bernie Sanders</div>
<div style="text-align: center; font-size: 14px;">@berniesanders-dril</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from wint & Bernie Sanders.
| Data | wint | Bernie Sanders |
| --- | --- | --- |
| Tweets downloaded | 3229 | 3250 |
| Retweets | 473 | 429 |
| Short tweets | 300 | 10 |
| Tweets kept | 2456 | 2811 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/yw6378l1/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 @berniesanders-dril's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3pydufi9) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3pydufi9/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/berniesanders-dril')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/berniesanders-dril
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
wint & Bernie Sanders
@berniesanders-dril
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from wint & Bernie Sanders.
Data: Tweets downloaded, wint: 3229, Bernie Sanders: 3250
Data: Retweets, wint: 473, Bernie Sanders: 429
Data: Short tweets, wint: 300, Bernie Sanders: 10
Data: Tweets kept, wint: 2456, Bernie Sanders: 2811
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @berniesanders-dril's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1097820307388334080/9ddg5F6v_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Bernie Sanders</div>
<div style="text-align: center; font-size: 14px;">@berniesanders</div>
</div>
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 Bernie Sanders.
| Data | Bernie Sanders |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 387 |
| Short tweets | 7 |
| Tweets kept | 2856 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1peyp14p/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 @berniesanders's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1w2aztyw) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1w2aztyw/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/berniesanders')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/berniesanders/1661604495356/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/berniesanders
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Bernie Sanders
@berniesanders
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Bernie Sanders.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @berniesanders's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/2113290180/images-1_400x400.jpeg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1284655541227323395/4E-Y6plH_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Best Music Lyric & Wisdom_by_GPT3</div>
<div style="text-align: center; font-size: 14px;">@bestmusiclyric-bygpt3</div>
</div>
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 Best Music Lyric & Wisdom_by_GPT3.
| Data | Best Music Lyric | Wisdom_by_GPT3 |
| --- | --- | --- |
| Tweets downloaded | 3248 | 293 |
| Retweets | 1092 | 3 |
| Short tweets | 834 | 86 |
| Tweets kept | 1322 | 204 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/101pevjn/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 @bestmusiclyric-bygpt3's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3qkafun2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3qkafun2/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/bestmusiclyric-bygpt3')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bestmusiclyric-bygpt3/1621260459372/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bestmusiclyric-bygpt3
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Best Music Lyric & Wisdom\_by\_GPT3
@bestmusiclyric-bygpt3
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Best Music Lyric & Wisdom\_by\_GPT3.
Data: Tweets downloaded, Best Music Lyric: 3248, Wisdom\_by\_GPT3: 293
Data: Retweets, Best Music Lyric: 1092, Wisdom\_by\_GPT3: 3
Data: Short tweets, Best Music Lyric: 834, Wisdom\_by\_GPT3: 86
Data: Tweets kept, Best Music Lyric: 1322, Wisdom\_by\_GPT3: 204
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bestmusiclyric-bygpt3's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/2113290180/images-1_400x400.jpeg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/903769803768217600/EKtan_aM_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Best Music Lyric & mark normand</div>
<div style="text-align: center; font-size: 14px;">@bestmusiclyric-marknorm</div>
</div>
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 Best Music Lyric & mark normand.
| Data | Best Music Lyric | mark normand |
| --- | --- | --- |
| Tweets downloaded | 3247 | 3247 |
| Retweets | 1112 | 138 |
| Short tweets | 820 | 521 |
| Tweets kept | 1315 | 2588 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/41htfpjc/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 @bestmusiclyric-marknorm's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2715uuiw) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2715uuiw/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/bestmusiclyric-marknorm')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bestmusiclyric-marknorm/1621435674803/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bestmusiclyric-marknorm
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Best Music Lyric & mark normand
@bestmusiclyric-marknorm
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Best Music Lyric & mark normand.
Data: Tweets downloaded, Best Music Lyric: 3247, mark normand: 3247
Data: Retweets, Best Music Lyric: 1112, mark normand: 138
Data: Short tweets, Best Music Lyric: 820, mark normand: 521
Data: Tweets kept, Best Music Lyric: 1315, mark normand: 2588
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bestmusiclyric-marknorm's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/2113290180/images-1_400x400.jpeg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/818597091215716353/5ejD1Ojs_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Best Music Lyric & Poets.org</div>
<div style="text-align: center; font-size: 14px;">@bestmusiclyric-poetsorg</div>
</div>
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 Best Music Lyric & Poets.org.
| Data | Best Music Lyric | Poets.org |
| --- | --- | --- |
| Tweets downloaded | 3246 | 3249 |
| Retweets | 1150 | 143 |
| Short tweets | 807 | 13 |
| Tweets kept | 1289 | 3093 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/11s270ah/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 @bestmusiclyric-poetsorg's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/p26o0mhb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/p26o0mhb/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/bestmusiclyric-poetsorg')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bestmusiclyric-poetsorg/1622303204122/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bestmusiclyric-poetsorg
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Best Music Lyric & URL
@bestmusiclyric-poetsorg
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Best Music Lyric & URL.
Data: Tweets downloaded, Best Music Lyric: 3246, URL: 3249
Data: Retweets, Best Music Lyric: 1150, URL: 143
Data: Short tweets, Best Music Lyric: 807, URL: 13
Data: Tweets kept, Best Music Lyric: 1289, URL: 3093
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bestmusiclyric-poetsorg's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/2113290180/images-1_400x400.jpeg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Best Music Lyric 🤖 AI Bot </div>
<div style="font-size: 15px">@bestmusiclyric bot</div>
</div>
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 [@bestmusiclyric's tweets](https://twitter.com/bestmusiclyric).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 1060 |
| Short tweets | 853 |
| Tweets kept | 1331 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ilv29ew/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 @bestmusiclyric's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1wqx12s6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1wqx12s6/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/bestmusiclyric')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bestmusiclyric/1620313468667/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bestmusiclyric
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Best Music Lyric AI Bot
@bestmusiclyric bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @bestmusiclyric's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bestmusiclyric's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1442634650703237120/mXIcYtIs_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1441096557944737802/y56EUiiU_400x400.png')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1431003324157812739/QYyroq6k_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Beth Kindig & I/O Fund Official</div>
<div style="text-align: center; font-size: 14px;">@beth_kindig-elonmusk-iofundofficial</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Elon Musk & Beth Kindig & I/O Fund Official.
| Data | Elon Musk | Beth Kindig | I/O Fund Official |
| --- | --- | --- | --- |
| Tweets downloaded | 2400 | 3247 | 1935 |
| Retweets | 127 | 484 | 143 |
| Short tweets | 642 | 273 | 8 |
| Tweets kept | 1631 | 2490 | 1784 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3pyiqrq2/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 @beth_kindig-elonmusk-iofundofficial's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3anxlpvl) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3anxlpvl/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/beth_kindig-elonmusk-iofundofficial')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/beth_kindig-elonmusk-iofundofficial
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Elon Musk & Beth Kindig & I/O Fund Official
@beth\_kindig-elonmusk-iofundofficial
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Elon Musk & Beth Kindig & I/O Fund Official.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @beth\_kindig-elonmusk-iofundofficial's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1005205545144336384/tRX1NN3Z_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Brian likes Communism 🤖 AI Bot </div>
<div style="font-size: 15px">@bfkelleher bot</div>
</div>
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 [@bfkelleher's tweets](https://twitter.com/bfkelleher).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3097 |
| Retweets | 1424 |
| Short tweets | 248 |
| Tweets kept | 1425 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3gd8lb66/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 @bfkelleher's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2xmh1eim) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2xmh1eim/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/bfkelleher')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bfkelleher/1614109725850/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bfkelleher
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Brian likes Communism AI Bot
@bfkelleher bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @bfkelleher's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bfkelleher's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1399217176343547904/rFLGp2F7_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Harsha Bhogle</div>
<div style="text-align: center; font-size: 14px;">@bhogleharsha</div>
</div>
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 Harsha Bhogle.
| Data | Harsha Bhogle |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 1399 |
| Short tweets | 92 |
| Tweets kept | 1759 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/i02fyzkh/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 @bhogleharsha's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/39kwdhq0) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/39kwdhq0/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/bhogleharsha')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bhogleharsha
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Harsha Bhogle
@bhogleharsha
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Harsha Bhogle.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bhogleharsha's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1080013557717192707/Cm9m3alh_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">┼aco d▲ve 🤖 AI Bot </div>
<div style="font-size: 15px">@bibliobabble bot</div>
</div>
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 [@bibliobabble's tweets](https://twitter.com/bibliobabble).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3206 |
| Retweets | 77 |
| Short tweets | 695 |
| Tweets kept | 2434 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3sdlkzqx/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 @bibliobabble's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/e6mj14rl) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/e6mj14rl/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/bibliobabble')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bibliobabble/1614116708796/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bibliobabble
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
┼aco d▲ve AI Bot
@bibliobabble bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @bibliobabble's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bibliobabble's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1356414477143519232/H2T46KhD_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Ellie 🐰 🤖 AI Bot </div>
<div style="font-size: 15px">@bichebuni bot</div>
</div>
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 [@bichebuni's tweets](https://twitter.com/bichebuni).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1578 |
| Retweets | 559 |
| Short tweets | 216 |
| Tweets kept | 803 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2jluupd2/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 @bichebuni's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2a0ttba9) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2a0ttba9/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/bichebuni')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bichebuni/1614096170963/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bichebuni
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Ellie AI Bot
@bichebuni bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @bichebuni's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bichebuni's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/716292569395699712/FxrNVTxe_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Bill Cipher 🤖 AI Bot </div>
<div style="font-size: 15px">@biiiclpher bot</div>
</div>
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 [@biiiclpher's tweets](https://twitter.com/biiiclpher).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3240 |
| Retweets | 0 |
| Short tweets | 404 |
| Tweets kept | 2836 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/iieebvnq/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 @biiiclpher's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/280557pf) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/280557pf/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/biiiclpher')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/biiiclpher/1613324925504/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/biiiclpher
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Bill Cipher AI Bot
@biiiclpher bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @biiiclpher's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @biiiclpher's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/847818629840228354/VXyQHfn0_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1419807498228273155/aAmpG4n-_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1421709337580294148/sjrv_GDf_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">wint & hood barbie & kevin</div>
<div style="text-align: center; font-size: 14px;">@biinx_-dril-milkman409</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from wint & hood barbie & kevin.
| Data | wint | hood barbie | kevin |
| --- | --- | --- | --- |
| Tweets downloaded | 3188 | 1176 | 3232 |
| Retweets | 456 | 419 | 1026 |
| Short tweets | 307 | 205 | 488 |
| Tweets kept | 2425 | 552 | 1718 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ztraqgze/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 @biinx_-dril-milkman409's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/39g1ujvg) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/39g1ujvg/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/biinx_-dril-milkman409')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/biinx_-dril-milkman409/1628066338698/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/biinx_-dril-milkman409
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
wint & hood barbie & kevin
@biinx\_-dril-milkman409
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from wint & hood barbie & kevin.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @biinx\_-dril-milkman409's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1414439092373254147/JdS8yLGI_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1115644092329758721/AFjOr-K8_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Bill Gates & jack</div>
<div style="text-align: center; font-size: 14px;">@billgates-jack</div>
</div>
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 Bill Gates & jack.
| Data | Bill Gates | jack |
| --- | --- | --- |
| Tweets downloaded | 3250 | 3196 |
| Retweets | 198 | 1168 |
| Short tweets | 6 | 808 |
| Tweets kept | 3046 | 1220 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/20z4h7xy/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 @billgates-jack's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1wd36v62) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1wd36v62/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/billgates-jack')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/billgates-jack/1627986735283/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/billgates-jack
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Bill Gates & jack
@billgates-jack
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Bill Gates & jack.
Data: Tweets downloaded, Bill Gates: 3250, jack: 3196
Data: Retweets, Bill Gates: 198, jack: 1168
Data: Short tweets, Bill Gates: 6, jack: 808
Data: Tweets kept, Bill Gates: 3046, jack: 1220
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @billgates-jack's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1564398871996174336/M-hffw5a_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Bill Gates</div>
<div style="text-align: center; font-size: 14px;">@billgates</div>
</div>
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 Bill Gates.
| Data | Bill Gates |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 176 |
| Short tweets | 10 |
| Tweets kept | 3064 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2e81cpn8/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 @billgates's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/x3nz24e9) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/x3nz24e9/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/billgates')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/billgates/1669939035568/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/billgates
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Bill Gates
@billgates
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Bill Gates.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @billgates's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1371497779772715009/_Fgk6GbC_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Will 🕊️🌷 🤖 AI Bot </div>
<div style="font-size: 15px">@billpshort bot</div>
</div>
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 [@billpshort's tweets](https://twitter.com/billpshort).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3224 |
| Retweets | 502 |
| Short tweets | 646 |
| Tweets kept | 2076 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/qcn56oc9/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 @billpshort's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/85i3zyjz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/85i3zyjz/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/billpshort')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/billpshort/1616611559120/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/billpshort
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Will ️ AI Bot
@billpshort bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @billpshort's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @billpshort's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1322690501481533440/gmJWNIzA_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">juggalo kokopelli 🤖 AI Bot </div>
<div style="font-size: 15px">@billtheponyfan bot</div>
</div>
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 [@billtheponyfan's tweets](https://twitter.com/billtheponyfan).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3178 |
| Retweets | 452 |
| Short tweets | 397 |
| Tweets kept | 2329 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ghn4ya3z/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 @billtheponyfan's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/31cu6fzd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/31cu6fzd/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/billtheponyfan')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/billtheponyfan
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
juggalo kokopelli AI Bot
@billtheponyfan bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @billtheponyfan's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @billtheponyfan's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1016862391823360000/BeQ1lMU7_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">bill wurtz</div>
<div style="text-align: center; font-size: 14px;">@billwurtz</div>
</div>
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 bill wurtz.
| Data | bill wurtz |
| --- | --- |
| Tweets downloaded | 3113 |
| Retweets | 29 |
| Short tweets | 97 |
| Tweets kept | 2987 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/18dzkuk8/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 @billwurtz's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/sqtnvoy5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/sqtnvoy5/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/billwurtz')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/billwurtz/1668310271499/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/billwurtz
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
bill wurtz
@billwurtz
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from bill wurtz.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @billwurtz's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1466001345324875784/4RrjsTR__400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Binance</div>
<div style="text-align: center; font-size: 14px;">@binance</div>
</div>
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 Binance.
| Data | Binance |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 268 |
| Short tweets | 353 |
| Tweets kept | 2629 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/m31ml960/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 @binance's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2vx6m0ip) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2vx6m0ip/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/binance')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/binance/1638367358099/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/binance
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Binance
@binance
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Binance.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @binance's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1398220397049434117/3i7JMNiF_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1399230370109825024/FypJacJv_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1404352885815664642/BEvtg0q4_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">bladee & Nothing person 2 & headaches</div>
<div style="text-align: center; font-size: 14px;">@biocrimed-bladeecity-w3bcam</div>
</div>
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 bladee & Nothing person 2 & headaches.
| Data | bladee | Nothing person 2 | headaches |
| --- | --- | --- | --- |
| Tweets downloaded | 1599 | 1863 | 3231 |
| Retweets | 313 | 117 | 62 |
| Short tweets | 486 | 714 | 1451 |
| Tweets kept | 800 | 1032 | 1718 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/37jgy6z4/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 @biocrimed-bladeecity-w3bcam's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1xg0n2ib) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1xg0n2ib/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/biocrimed-bladeecity-w3bcam')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/biocrimed-bladeecity-w3bcam/1623834051692/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/biocrimed-bladeecity-w3bcam
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
bladee & Nothing person 2 & headaches
@biocrimed-bladeecity-w3bcam
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from bladee & Nothing person 2 & headaches.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @biocrimed-bladeecity-w3bcam's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1371598614263386115/7NaqMiOP_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">🐱🚀 Brikir 🤖 AI Bot </div>
<div style="font-size: 15px">@birkirh bot</div>
</div>
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 [@birkirh's tweets](https://twitter.com/birkirh).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1105 |
| Retweets | 98 |
| Short tweets | 191 |
| Tweets kept | 816 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1l7dop1n/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 @birkirh's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/zk4c602e) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/zk4c602e/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/birkirh')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/birkirh/1616669653013/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/birkirh
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Brikir AI Bot
@birkirh bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @birkirh's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @birkirh's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/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'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/421692600446619648/dWAbC2wg_400x400.jpeg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Bitcoin 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@bitcoin bot</div>
</div>
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 [@bitcoin's tweets](https://twitter.com/bitcoin).
<table style='border-width:0'>
<thead style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
<th style='border-width:0'>Data</th>
<th style='border-width:0'>Quantity</th>
</tr>
</thead>
<tbody style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Tweets downloaded</td>
<td style='border-width:0'>3206</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Retweets</td>
<td style='border-width:0'>1190</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Short tweets</td>
<td style='border-width:0'>390</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1626</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/9fss3789/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 @bitcoin's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2pqrlo2u) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2pqrlo2u/artifacts) is logged and versioned.
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for text generation:
<pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline
generator = pipeline(<span style="color:#FF9800">'text-generation'</span>,
model=<span style="color:#FF9800">'huggingtweets/bitcoin'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### 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>
[](https://twitter.com/intent/follow?screen_name=borisdayma)
<section class='prose'>
For more details, visit the project repository.
</section>
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bitcoin/1612625608055/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bitcoin
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<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'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Bitcoin AI Bot </div>
<div style="font-size: 15px; color: #657786">@bitcoin bot</div>
</div>
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
## How does it work?
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
## Training data
The model was trained on @bitcoin's tweets.
<table style='border-width:0'>
<thead style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
<th style='border-width:0'>Data</th>
<th style='border-width:0'>Quantity</th>
</tr>
</thead>
<tbody style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Tweets downloaded</td>
<td style='border-width:0'>3206</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Retweets</td>
<td style='border-width:0'>1190</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Short tweets</td>
<td style='border-width:0'>390</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1626</td>
</tr>
</tbody>
</table>
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
## Training procedure
The model is based on a pre-trained GPT-2 which is fine-tuned on @bitcoin's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for text generation:
<pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline
generator = pipeline(<span style="color:#FF9800">'text-generation'</span>,
model=<span style="color:#FF9800">'huggingtweets/bitcoin'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### Limitations and bias
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
</section>
![Follow](URL
<section class='prose'>
For more details, visit the project repository.
</section>
![GitHub stars](URL
|
[
"## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on @bitcoin's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3206</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>1190</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>390</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1626</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @bitcoin's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## Intended uses & limitations",
"### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/bitcoin'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on @bitcoin's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3206</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>1190</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>390</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1626</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @bitcoin's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## Intended uses & limitations",
"### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/bitcoin'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1394891459900231689/xXdX3yWP_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1415442891015610370/1qyYwuHx_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1357462788130578434/6ZRnYvCW_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Crypto Bros Taking Ls & Bitfinex’ed 🔥 & Xeni</div>
<div style="text-align: center; font-size: 14px;">@bitfinexed-coinerstakingls-xeni</div>
</div>
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 Crypto Bros Taking Ls & Bitfinex’ed 🔥 & Xeni.
| Data | Crypto Bros Taking Ls | Bitfinex’ed 🔥 | Xeni |
| --- | --- | --- | --- |
| Tweets downloaded | 566 | 3245 | 3229 |
| Retweets | 94 | 650 | 1834 |
| Short tweets | 222 | 613 | 402 |
| Tweets kept | 250 | 1982 | 993 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3eviqxf1/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 @bitfinexed-coinerstakingls-xeni's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1kim6sku) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1kim6sku/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/bitfinexed-coinerstakingls-xeni')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/bitfinexed-coinerstakingls-xeni/1643345731503/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bitfinexed-coinerstakingls-xeni
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Crypto Bros Taking Ls & Bitfinex’ed & Xeni
@bitfinexed-coinerstakingls-xeni
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Crypto Bros Taking Ls & Bitfinex’ed & Xeni.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bitfinexed-coinerstakingls-xeni's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1415442891015610370/1qyYwuHx_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Bitfinex'ed 🔥</div>
<div style="text-align: center; font-size: 14px;">@bitfinexed</div>
</div>
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 Bitfinex'ed 🔥.
| Data | Bitfinex'ed 🔥 |
| --- | --- |
| Tweets downloaded | 3227 |
| Retweets | 863 |
| Short tweets | 628 |
| Tweets kept | 1736 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/21r06xwr/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 @bitfinexed's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2juhurh4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2juhurh4/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/bitfinexed')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bitfinexed/1633818005154/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bitfinexed
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Bitfinex'ed
@bitfinexed
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Bitfinex'ed .
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bitfinexed's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1406669132527976453/Sv0lEtmk_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/822229503212666880/L4UutyTM_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Aim & Combat Ballerina</div>
<div style="text-align: center; font-size: 14px;">@bladeecity-robber0540</div>
</div>
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 Aim & Combat Ballerina.
| Data | Aim | Combat Ballerina |
| --- | --- | --- |
| Tweets downloaded | 1604 | 671 |
| Retweets | 314 | 66 |
| Short tweets | 487 | 303 |
| Tweets kept | 803 | 302 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3uvtcfjv/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 @bladeecity-robber0540's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/36qst0l8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/36qst0l8/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/bladeecity-robber0540')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bladeecity-robber0540/1626331680252/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bladeecity-robber0540
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Aim & Combat Ballerina
@bladeecity-robber0540
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Aim & Combat Ballerina.
Data: Tweets downloaded, Aim: 1604, Combat Ballerina: 671
Data: Retweets, Aim: 314, Combat Ballerina: 66
Data: Short tweets, Aim: 487, Combat Ballerina: 303
Data: Tweets kept, Aim: 803, Combat Ballerina: 302
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bladeecity-robber0540's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1406669132527976453/Sv0lEtmk_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1409559937445990403/9bkJBvX9_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1264902234703265794/lC3YnIYF_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Aim & jamar 🇵🇸 & Adrian Wojnarowski</div>
<div style="text-align: center; font-size: 14px;">@bladeecity-rxmaybike-wojespn</div>
</div>
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 Aim & jamar 🇵🇸 & Adrian Wojnarowski.
| Data | Aim | jamar 🇵🇸 | Adrian Wojnarowski |
| --- | --- | --- | --- |
| Tweets downloaded | 1601 | 3071 | 3250 |
| Retweets | 314 | 1694 | 777 |
| Short tweets | 486 | 325 | 34 |
| Tweets kept | 801 | 1052 | 2439 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2h7w61mh/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 @bladeecity-rxmaybike-wojespn's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3mkjmebf) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3mkjmebf/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/bladeecity-rxmaybike-wojespn')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bladeecity-rxmaybike-wojespn/1624998722915/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bladeecity-rxmaybike-wojespn
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Aim & jamar 🇵🇸 & Adrian Wojnarowski
@bladeecity-rxmaybike-wojespn
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Aim & jamar 🇵🇸 & Adrian Wojnarowski.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bladeecity-rxmaybike-wojespn's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1501634135378391044/6FiRJ7RP_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1454672063319392260/iwO_Ll7D_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Aim Nothyng & jamar " The Fool ” majima 🇵🇸</div>
<div style="text-align: center; font-size: 14px;">@bladeecity-rxmaybike</div>
</div>
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 Aim Nothyng & jamar " The Fool ” majima 🇵🇸.
| Data | Aim Nothyng | jamar " The Fool ” majima 🇵🇸 |
| --- | --- | --- |
| Tweets downloaded | 1620 | 3058 |
| Retweets | 322 | 1792 |
| Short tweets | 492 | 331 |
| Tweets kept | 806 | 935 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ujud5vr2/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 @bladeecity-rxmaybike's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2am1uizy) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2am1uizy/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/bladeecity-rxmaybike')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/bladeecity-rxmaybike/1653254735804/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bladeecity-rxmaybike
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Aim Nothyng & jamar " The Fool ” majima 🇵🇸
@bladeecity-rxmaybike
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Aim Nothyng & jamar " The Fool ” majima 🇵🇸.
Data: Tweets downloaded, Aim Nothyng: 1620, jamar " The Fool ” majima 🇵🇸: 3058
Data: Retweets, Aim Nothyng: 322, jamar " The Fool ” majima 🇵🇸: 1792
Data: Short tweets, Aim Nothyng: 492, jamar " The Fool ” majima 🇵🇸: 331
Data: Tweets kept, Aim Nothyng: 806, jamar " The Fool ” majima 🇵🇸: 935
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bladeecity-rxmaybike's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1406669132527976453/Sv0lEtmk_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1193884102023237632/d21-RRy2_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Aim & SGCHIEFCOMMANDA</div>
<div style="text-align: center; font-size: 14px;">@bladeecity-thaiboygoon</div>
</div>
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 Aim & SGCHIEFCOMMANDA.
| Data | Aim | SGCHIEFCOMMANDA |
| --- | --- | --- |
| Tweets downloaded | 1601 | 3176 |
| Retweets | 314 | 460 |
| Short tweets | 486 | 463 |
| Tweets kept | 801 | 2253 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3mwc0s6y/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 @bladeecity-thaiboygoon's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2uwsmmzd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2uwsmmzd/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/bladeecity-thaiboygoon')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bladeecity-thaiboygoon/1625039802520/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bladeecity-thaiboygoon
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Aim & SGCHIEFCOMMANDA
@bladeecity-thaiboygoon
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Aim & SGCHIEFCOMMANDA.
Data: Tweets downloaded, Aim: 1601, SGCHIEFCOMMANDA: 3176
Data: Retweets, Aim: 314, SGCHIEFCOMMANDA: 460
Data: Short tweets, Aim: 486, SGCHIEFCOMMANDA: 463
Data: Tweets kept, Aim: 801, SGCHIEFCOMMANDA: 2253
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bladeecity-thaiboygoon's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1501634135378391044/6FiRJ7RP_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Aim Nothyng</div>
<div style="text-align: center; font-size: 14px;">@bladeecity</div>
</div>
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 Aim Nothyng.
| Data | Aim Nothyng |
| --- | --- |
| Tweets downloaded | 1620 |
| Retweets | 322 |
| Short tweets | 492 |
| Tweets kept | 806 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/195wt1qo/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 @bladeecity's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/239lpzqe) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/239lpzqe/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/bladeecity')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/bladeecity/1653403106456/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bladeecity
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Aim Nothyng
@bladeecity
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Aim Nothyng.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bladeecity's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1470642032851009537/LWrcZk48_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">sweetie p1e</div>
<div style="text-align: center; font-size: 14px;">@bladeefan91</div>
</div>
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 sweetie p1e.
| Data | sweetie p1e |
| --- | --- |
| Tweets downloaded | 2249 |
| Retweets | 351 |
| Short tweets | 547 |
| Tweets kept | 1351 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/cacbnxbr/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 @bladeefan91's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2kupw7ab) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2kupw7ab/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/bladeefan91')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/bladeefan91/1639726754777/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bladeefan91
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
sweetie p1e
@bladeefan91
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from sweetie p1e.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bladeefan91's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1357543393757433856/lBrNiipb_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Ghislaine Maxwell's Fat Tiddies Apologist 🤖 AI Bot </div>
<div style="font-size: 15px">@bleaksigilkeep bot</div>
</div>
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 [@bleaksigilkeep's tweets](https://twitter.com/bleaksigilkeep).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3206 |
| Retweets | 1132 |
| Short tweets | 387 |
| Tweets kept | 1687 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/200hepvo/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 @bleaksigilkeep's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ugdbbm9) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ugdbbm9/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/bleaksigilkeep')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bleaksigilkeep/1614100737277/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bleaksigilkeep
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Ghislaine Maxwell's Fat Tiddies Apologist AI Bot
@bleaksigilkeep bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @bleaksigilkeep's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bleaksigilkeep's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1253341078637273089/PO6bqj0P_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Bloodwarriorguru</div>
<div style="text-align: center; font-size: 14px;">@bloodwarrioroc1</div>
</div>
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 Bloodwarriorguru.
| Data | Bloodwarriorguru |
| --- | --- |
| Tweets downloaded | 1206 |
| Retweets | 67 |
| Short tweets | 266 |
| Tweets kept | 873 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2x3rus6s/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 @bloodwarrioroc1's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2s1u2k3b) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2s1u2k3b/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/bloodwarrioroc1')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bloodwarrioroc1/1624210855980/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bloodwarrioroc1
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Bloodwarriorguru
@bloodwarrioroc1
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Bloodwarriorguru.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bloodwarrioroc1's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1478488866730524675/y4KIjwym_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">ᵍᵐBlueEyedGirl.ᴺᶠᵀ😎🔻🦴</div>
<div style="text-align: center; font-size: 14px;">@blueeyedgirlnft</div>
</div>
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 ᵍᵐBlueEyedGirl.ᴺᶠᵀ😎🔻🦴.
| Data | ᵍᵐBlueEyedGirl.ᴺᶠᵀ😎🔻🦴 |
| --- | --- |
| Tweets downloaded | 588 |
| Retweets | 349 |
| Short tweets | 154 |
| Tweets kept | 85 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/9tllree8/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 @blueeyedgirlnft's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2q6w52hj) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2q6w52hj/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/blueeyedgirlnft')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/blueeyedgirlnft/1642199309839/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/blueeyedgirlnft
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
ᵍᵐBlueEyedGirl.ᴺᶠᵀ
@blueeyedgirlnft
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from ᵍᵐBlueEyedGirl.ᴺᶠᵀ.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @blueeyedgirlnft's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/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'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1306249141018910726/o3bCj_sP_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Barnes & Noble 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@bnbuzz bot</div>
</div>
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).
<table style='border-width:0'>
<thead style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
<th style='border-width:0'>Data</th>
<th style='border-width:0'>Quantity</th>
</tr>
</thead>
<tbody style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Tweets downloaded</td>
<td style='border-width:0'>3156</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Retweets</td>
<td style='border-width:0'>821</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Short tweets</td>
<td style='border-width:0'>124</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2211</td>
</tr>
</tbody>
</table>
[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:
<pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline
generator = pipeline(<span style="color:#FF9800">'text-generation'</span>,
model=<span style="color:#FF9800">'huggingtweets/bnbuzz'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### 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>
[](https://twitter.com/intent/follow?screen_name=borisdayma)
<section class='prose'>
For more details, visit the project repository.
</section>
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bnbuzz
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<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'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Barnes & Noble AI Bot </div>
<div style="font-size: 15px; color: #657786">@bnbuzz bot</div>
</div>
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
## How does it work?
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
## Training data
The model was trained on @bnbuzz's tweets.
<table style='border-width:0'>
<thead style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
<th style='border-width:0'>Data</th>
<th style='border-width:0'>Quantity</th>
</tr>
</thead>
<tbody style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Tweets downloaded</td>
<td style='border-width:0'>3156</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Retweets</td>
<td style='border-width:0'>821</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Short tweets</td>
<td style='border-width:0'>124</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2211</td>
</tr>
</tbody>
</table>
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
## Training procedure
The model is based on a pre-trained GPT-2 which is fine-tuned on @bnbuzz's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for text generation:
<pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline
generator = pipeline(<span style="color:#FF9800">'text-generation'</span>,
model=<span style="color:#FF9800">'huggingtweets/bnbuzz'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### Limitations and bias
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
</section>
![Follow](URL
<section class='prose'>
For more details, visit the project repository.
</section>
![GitHub stars](URL
|
[
"## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on @bnbuzz's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3156</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>821</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>124</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2211</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @bnbuzz's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## Intended uses & limitations",
"### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/bnbuzz'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on @bnbuzz's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3156</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>821</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>124</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2211</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @bnbuzz's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.",
"## Intended uses & limitations",
"### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/bnbuzz'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/2315549670/b4ekpxtfpd1p5ip0t95g_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">bobuk</div>
<div style="text-align: center; font-size: 14px;">@bobuk</div>
</div>
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 bobuk.
| Data | bobuk |
| --- | --- |
| Tweets downloaded | 3206 |
| Retweets | 12 |
| Short tweets | 268 |
| Tweets kept | 2926 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1dvjqdl0/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 @bobuk's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/227ixa7i) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/227ixa7i/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/bobuk')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bobuk/1633096176390/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bobuk
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
bobuk
@bobuk
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from bobuk.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bobuk's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1311301456805281793/wiVELS8Y_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">bogna 🤖 AI Bot </div>
<div style="font-size: 15px">@bognamk bot</div>
</div>
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 [@bognamk's tweets](https://twitter.com/bognamk).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 273 |
| Retweets | 26 |
| Short tweets | 22 |
| Tweets kept | 225 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/yelcjqqa/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 @bognamk's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/asd5yjvu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/asd5yjvu/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/bognamk')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/bognamk/1616663275652/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/bognamk
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
bogna AI Bot
@bognamk bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @bognamk's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @bognamk's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1366991202000777216/E5Qeu37S_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">boogie2988 😭🤡</div>
<div style="text-align: center; font-size: 14px;">@boogie2988</div>
</div>
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 boogie2988 😭🤡.
| Data | boogie2988 😭🤡 |
| --- | --- |
| Tweets downloaded | 3242 |
| Retweets | 216 |
| Short tweets | 392 |
| Tweets kept | 2634 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/30jummgi/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 @boogie2988's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/38yren3o) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/38yren3o/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/boogie2988')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/boogie2988/1622323494391/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/boogie2988
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
boogie2988
@boogie2988
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from boogie2988 .
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @boogie2988's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('USER_PROFILE')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Elon Musk & Boris Dayma 🤖 AI Bot </div>
<div style="font-size: 15px">@borisdayma-elonmusk bot</div>
</div>
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 [@borisdayma-elonmusk's tweets](https://twitter.com/borisdayma-elonmusk).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1 |
| Retweets | 1 |
| Short tweets | 1 |
| Tweets kept | 1 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/s2e28lq5/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 @borisdayma-elonmusk's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2r549ulu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2r549ulu/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/borisdayma-elonmusk')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/borisdayma-elonmusk/1620367858137/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/borisdayma-elonmusk
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Elon Musk & Boris Dayma AI Bot
@borisdayma-elonmusk bot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on @borisdayma-elonmusk's tweets.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @borisdayma-elonmusk's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1152601773330370560/UhVRDMyp_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Boris Dayma 🖍️</div>
<div style="text-align: center; font-size: 14px;">@borisdayma</div>
</div>
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 Boris Dayma 🖍️.
| Data | Boris Dayma 🖍️ |
| --- | --- |
| Tweets downloaded | 1371 |
| Retweets | 146 |
| Short tweets | 42 |
| Tweets kept | 1183 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/tlbliehz/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 @borisdayma's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3qs9dfef) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3qs9dfef/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/borisdayma')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/borisdayma/1656366383066/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/borisdayma
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Boris Dayma ️
@borisdayma
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Boris Dayma ️.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @borisdayma's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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