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transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1307765220107001859/cEfzmr1c_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Adrian Gregory 🤖 AI Bot </div>
<div style="font-size: 15px">@adriangregory20 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 [@adriangregory20's tweets](https://twitter.com/adriangregory20).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 587 |
| Short tweets | 204 |
| Tweets kept | 2455 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/4phwvtdq/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 @adriangregory20's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3tlt3nyy) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3tlt3nyy/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/adriangregory20')
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/adriangregory20/1617002077884/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/adriangregory20
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Adrian Gregory AI Bot
@adriangregory20 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 @adriangregory20'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 @adriangregory20'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
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/1267145246359474176/OtRIrSIL_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Adrienna Wong 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@adrienna_w 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 [@adrienna_w's tweets](https://twitter.com/adrienna_w).
<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'>2000</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'>1570</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'>46</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>384</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3r42s34p/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 @adrienna_w's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3n5znqzh) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3n5znqzh/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/adrienna_w'</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/adrienna_w/1610164811243/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/adrienna_w
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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">Adrienna Wong AI Bot </div>
<div style="font-size: 15px; color: #657786">@adrienna_w 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 @adrienna_w'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'>2000</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'>1570</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'>46</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>384</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 @adrienna_w'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/adrienna_w'</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 @adrienna_w'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'>2000</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'>1570</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'>46</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>384</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 @adrienna_w'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/adrienna_w'</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 @adrienna_w'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'>2000</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'>1570</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'>46</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>384</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 @adrienna_w'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/adrienna_w'</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"
] |
[
57,
34,
430,
77,
9,
169,
48,
58
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[
"passage: 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."
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1385948567622324230/XKbD4BWp_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">aeva 🤖 AI Bot </div>
<div style="font-size: 15px">@ae333mage 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 [@ae333mage's tweets](https://twitter.com/ae333mage).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3169 |
| Retweets | 1776 |
| Short tweets | 592 |
| Tweets kept | 801 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/31fkltap/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 @ae333mage's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/jg2xbkk5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/jg2xbkk5/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/ae333mage')
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/ae333mage/1619282749797/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ae333mage
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
aeva AI Bot
@ae333mage 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 @ae333mage'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 @ae333mage'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1471448753353670660/T0h3zXn-_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">aeva</div>
<div style="text-align: center; font-size: 14px;">@aevaeavaevevave</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 aeva.
| Data | aeva |
| --- | --- |
| Tweets downloaded | 3184 |
| Retweets | 985 |
| Short tweets | 659 |
| Tweets kept | 1540 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3g4kejp0/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 @aevaeavaevevave's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ikuw0pg) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ikuw0pg/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/aevaeavaevevave')
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/aevaeavaevevave/1642691608974/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/aevaeavaevevave
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
aeva
@aevaeavaevevave
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 aeva.
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 @aevaeavaevevave'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1250126825109544960/8ndvxL2E_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Ashley Finch 🔞 🤖 AI Bot </div>
<div style="font-size: 15px">@afinchwrites 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 [@afinchwrites's tweets](https://twitter.com/afinchwrites).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3214 |
| Retweets | 1236 |
| Short tweets | 265 |
| Tweets kept | 1713 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1bwfztuv/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 @afinchwrites's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/39vriclf) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/39vriclf/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/afinchwrites')
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/afinchwrites/1617758836679/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/afinchwrites
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Ashley Finch AI Bot
@afinchwrites 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 @afinchwrites'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 @afinchwrites'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1216156392/afm-marketing_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">AFM Marketing</div>
<div style="text-align: center; font-size: 14px;">@afm_marketing</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 AFM Marketing.
| Data | AFM Marketing |
| --- | --- |
| Tweets downloaded | 3238 |
| Retweets | 1051 |
| Short tweets | 64 |
| Tweets kept | 2123 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/6tgdc3wa/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 @afm_marketing's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/36mudapr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/36mudapr/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/afm_marketing')
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"}]}
|
text-generation
|
huggingtweets/afm_marketing
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
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
AFM Marketing
@afm\_marketing
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 AFM Marketing.
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 @afm\_marketing'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"
] |
[
58
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1223585534561472512/QO-CQ64Z_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">Alfredo Casero</div>
<div style="text-align: center; font-size: 14px;">@agencialavieja</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 Alfredo Casero.
| Data | Alfredo Casero |
| --- | --- |
| Tweets downloaded | 3197 |
| Retweets | 854 |
| Short tweets | 565 |
| Tweets kept | 1778 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2xpelzjw/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 @agencialavieja's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1q128hty) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1q128hty/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/agencialavieja')
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/agencialavieja/1621053473805/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/agencialavieja
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
Alfredo Casero
@agencialavieja
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 Alfredo Casero.
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 @agencialavieja'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1279628481073041409/mtT5QVq__400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">🥰Gender Nihilist/Nihilist Anarchist🥰 🤖 AI Bot </div>
<div style="font-size: 15px">@agendernihilist 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 [@agendernihilist's tweets](https://twitter.com/agendernihilist).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3172 |
| Retweets | 1457 |
| Short tweets | 187 |
| Tweets kept | 1528 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/37jo5lqx/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 @agendernihilist's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3hzj8j9p) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3hzj8j9p/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/agendernihilist')
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/agendernihilist/1617923598463/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/agendernihilist
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Gender Nihilist/Nihilist Anarchist AI Bot
@agendernihilist 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 @agendernihilist'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 @agendernihilist'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1344775686586847233/QkHU_dIP_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">A.G. Holdier Loves Coors Cat</div>
<div style="text-align: center; font-size: 14px;">@agholdier</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 A.G. Holdier Loves Coors Cat.
| Data | A.G. Holdier Loves Coors Cat |
| --- | --- |
| Tweets downloaded | 3235 |
| Retweets | 460 |
| Short tweets | 423 |
| Tweets kept | 2352 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2xot2p53/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 @agholdier's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2fke0tr2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2fke0tr2/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/agholdier')
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"}]}
|
text-generation
|
huggingtweets/agholdier
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
A.G. Holdier Loves Coors Cat
@agholdier
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 A.G. Holdier Loves Coors Cat.
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 @agholdier'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1302422740507516929/zD7GvA0H_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Agnes Callard 🤖 AI Bot </div>
<div style="font-size: 15px">@agnescallard 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 [@agnescallard's tweets](https://twitter.com/agnescallard).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3240 |
| Retweets | 371 |
| Short tweets | 410 |
| Tweets kept | 2459 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1w2jn5h4/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 @agnescallard's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/hgprm6he) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/hgprm6he/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/agnescallard')
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/agnescallard/1616718656775/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/agnescallard
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Agnes Callard AI Bot
@agnescallard 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 @agnescallard'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 @agnescallard'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1404846924226695174/_oELkFsx_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">##ahleeuwu</div>
<div style="text-align: center; font-size: 14px;">@ahleemuhleek</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 ##ahleeuwu.
| Data | ##ahleeuwu |
| --- | --- |
| Tweets downloaded | 480 |
| Retweets | 149 |
| Short tweets | 86 |
| Tweets kept | 245 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/17rz3rct/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 @ahleemuhleek's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/32bqa4q7) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/32bqa4q7/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/ahleemuhleek')
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/ahleemuhleek/1623782310895/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ahleemuhleek
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
##ahleeuwu
@ahleemuhleek
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 ##ahleeuwu.
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 @ahleemuhleek'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1297351407809380352/gW1wWpRv_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Ahmed 🤖 AI Bot </div>
<div style="font-size: 15px">@ahmedallibhoy 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 [@ahmedallibhoy's tweets](https://twitter.com/ahmedallibhoy).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 226 |
| Retweets | 82 |
| Short tweets | 1 |
| Tweets kept | 143 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/6cjgzd9a/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 @ahmedallibhoy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3g9v31lb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3g9v31lb/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/ahmedallibhoy')
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/ahmedallibhoy/1616643813999/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ahmedallibhoy
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Ahmed AI Bot
@ahmedallibhoy 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 @ahmedallibhoy'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 @ahmedallibhoy'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1250356895199760384/fOxe1Ymd_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/1391882949650440200/lmEKl2ZQ_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">Art Prompts & AI Hexcrawl</div>
<div style="text-align: center; font-size: 14px;">@ai_hexcrawl-dailyartprompts</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 Art Prompts & AI Hexcrawl.
| Data | Art Prompts | AI Hexcrawl |
| --- | --- | --- |
| Tweets downloaded | 726 | 741 |
| Retweets | 16 | 27 |
| Short tweets | 1 | 1 |
| Tweets kept | 709 | 713 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/prw4k5r4/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 @ai_hexcrawl-dailyartprompts's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1kxaov1u) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1kxaov1u/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/ai_hexcrawl-dailyartprompts')
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/ai_hexcrawl-dailyartprompts/1632004437614/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ai_hexcrawl-dailyartprompts
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
Art Prompts & AI Hexcrawl
@ai\_hexcrawl-dailyartprompts
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 Art Prompts & AI Hexcrawl.
Data: Tweets downloaded, Art Prompts: 726, AI Hexcrawl: 741
Data: Retweets, Art Prompts: 16, AI Hexcrawl: 27
Data: Short tweets, Art Prompts: 1, AI Hexcrawl: 1
Data: Tweets kept, Art Prompts: 709, AI Hexcrawl: 713
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 @ai\_hexcrawl-dailyartprompts'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1374924360780242944/-Q8NfgEr_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/1386749605216407555/QIJeyWfE_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/1391882949650440200/lmEKl2ZQ_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">wintbot_neo & wint but Al & AI Hexcrawl</div>
<div style="text-align: center; font-size: 14px;">@ai_hexcrawl-dril_gpt2-drilbot_neo</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 wintbot_neo & wint but Al & AI Hexcrawl.
| Data | wintbot_neo | wint but Al | AI Hexcrawl |
| --- | --- | --- | --- |
| Tweets downloaded | 3207 | 3198 | 737 |
| Retweets | 268 | 41 | 26 |
| Short tweets | 272 | 49 | 1 |
| Tweets kept | 2667 | 3108 | 710 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2g9pfbo8/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 @ai_hexcrawl-dril_gpt2-drilbot_neo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/226pt34g) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/226pt34g/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/ai_hexcrawl-dril_gpt2-drilbot_neo')
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/ai_hexcrawl-dril_gpt2-drilbot_neo/1631932214962/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ai_hexcrawl-dril_gpt2-drilbot_neo
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
wintbot\_neo & wint but Al & AI Hexcrawl
@ai\_hexcrawl-dril\_gpt2-drilbot\_neo
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 wintbot\_neo & wint but Al & AI Hexcrawl.
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 @ai\_hexcrawl-dril\_gpt2-drilbot\_neo'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1391882949650440200/lmEKl2ZQ_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/1288860183515607041/uHoTEsFz_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">AI Hexcrawl & GPT-2 Religion AI</div>
<div style="text-align: center; font-size: 14px;">@ai_hexcrawl-gods_txt</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 AI Hexcrawl & GPT-2 Religion AI.
| Data | AI Hexcrawl | GPT-2 Religion AI |
| --- | --- | --- |
| Tweets downloaded | 245 | 3249 |
| Retweets | 8 | 68 |
| Short tweets | 0 | 9 |
| Tweets kept | 237 | 3172 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/37knqj1s/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 @ai_hexcrawl-gods_txt's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/acyab0oh) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/acyab0oh/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/ai_hexcrawl-gods_txt')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](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/ai_hexcrawl-gods_txt/1623775960967/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ai_hexcrawl-gods_txt
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
AI Hexcrawl & GPT-2 Religion AI
@ai\_hexcrawl-gods\_txt
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 AI Hexcrawl & GPT-2 Religion AI.
Data: Tweets downloaded, AI Hexcrawl: 245, GPT-2 Religion AI: 3249
Data: Retweets, AI Hexcrawl: 8, GPT-2 Religion AI: 68
Data: Short tweets, AI Hexcrawl: 0, GPT-2 Religion AI: 9
Data: Tweets kept, AI Hexcrawl: 237, GPT-2 Religion AI: 3172
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 @ai\_hexcrawl-gods\_txt'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1391882949650440200/lmEKl2ZQ_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/1261895681561804800/r6vOZGoH_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">AI Hexcrawl & GPT2-Microfic</div>
<div style="text-align: center; font-size: 14px;">@ai_hexcrawl-gptmicrofic</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 AI Hexcrawl & GPT2-Microfic.
| Data | AI Hexcrawl | GPT2-Microfic |
| --- | --- | --- |
| Tweets downloaded | 737 | 1127 |
| Retweets | 26 | 9 |
| Short tweets | 1 | 9 |
| Tweets kept | 710 | 1109 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2cmbpada/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 @ai_hexcrawl-gptmicrofic's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/5g9tts1o) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/5g9tts1o/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/ai_hexcrawl-gptmicrofic')
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/ai_hexcrawl-gptmicrofic/1631934945678/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ai_hexcrawl-gptmicrofic
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
AI Hexcrawl & GPT2-Microfic
@ai\_hexcrawl-gptmicrofic
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 AI Hexcrawl & GPT2-Microfic.
Data: Tweets downloaded, AI Hexcrawl: 737, GPT2-Microfic: 1127
Data: Retweets, AI Hexcrawl: 26, GPT2-Microfic: 9
Data: Short tweets, AI Hexcrawl: 1, GPT2-Microfic: 9
Data: Tweets kept, AI Hexcrawl: 710, GPT2-Microfic: 1109
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 @ai\_hexcrawl-gptmicrofic'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1467327234365181953/gFho8YCv_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">AI Hexcrawl</div>
<div style="text-align: center; font-size: 14px;">@ai_hexcrawl</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 AI Hexcrawl.
| Data | AI Hexcrawl |
| --- | --- |
| Tweets downloaded | 1164 |
| Retweets | 42 |
| Short tweets | 2 |
| Tweets kept | 1120 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/vdxugbwr/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 @ai_hexcrawl's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/r9ejkubu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/r9ejkubu/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/ai_hexcrawl')
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/ai_hexcrawl/1639597537705/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ai_hexcrawl
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
AI Hexcrawl
@ai\_hexcrawl
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 AI Hexcrawl.
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 @ai\_hexcrawl'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1374061160132186116/NV6XVCdH_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Jritter AI 🤖 AI Bot </div>
<div style="font-size: 15px">@aijritter 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 [@aijritter's tweets](https://twitter.com/aijritter).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2484 |
| Retweets | 21 |
| Short tweets | 271 |
| Tweets kept | 2192 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/16pwaloe/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 @aijritter's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1l866lhx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1l866lhx/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/aijritter')
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/aijritter/1619426792472/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/aijritter
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Jritter AI AI Bot
@aijritter 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 @aijritter'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 @aijritter'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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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/1413348777243512833/dvnUJ-du_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">AimbotAimy 🍞🔞 NSFW V-Tuber & Demon Lord Yeefi NSFW🔞 & ADMIRAL JIANGSHI 🐉🇭🇹🏴☠️</div>
<div style="text-align: center; font-size: 14px;">@aimbotaimy-coldjiangshi-ladydarknest</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 AimbotAimy 🍞🔞 NSFW V-Tuber & Demon Lord Yeefi NSFW🔞 & ADMIRAL JIANGSHI 🐉🇭🇹🏴☠️.
| Data | AimbotAimy 🍞🔞 NSFW V-Tuber | Demon Lord Yeefi NSFW🔞 | ADMIRAL JIANGSHI 🐉🇭🇹🏴☠️ |
| --- | --- | --- | --- |
| Tweets downloaded | 518 | 3242 | 2899 |
| Retweets | 60 | 957 | 1462 |
| Short tweets | 127 | 392 | 324 |
| Tweets kept | 331 | 1893 | 1113 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/348if7b6/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 @aimbotaimy-coldjiangshi-ladydarknest's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1dzd34gb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1dzd34gb/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/aimbotaimy-coldjiangshi-ladydarknest')
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/aimbotaimy-coldjiangshi-ladydarknest/1627243217316/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/aimbotaimy-coldjiangshi-ladydarknest
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
AimbotAimy NSFW V-Tuber & Demon Lord Yeefi NSFW & ADMIRAL JIANGSHI 🇭🇹️
@aimbotaimy-coldjiangshi-ladydarknest
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 AimbotAimy NSFW V-Tuber & Demon Lord Yeefi NSFW & ADMIRAL JIANGSHI 🇭🇹️.
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 @aimbotaimy-coldjiangshi-ladydarknest'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
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/1374872808136835072/hPahIg-A_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/1405364006475296773/0i4RCEH5_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/1408966863804063749/fTuaNcZ__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">AimbotAimy 🍞🔞 NSFW V-Tuber & Poe's Law 🇷🇺: 3.33 You can (not) redo & Demi 'ドヤ顔' Naga</div>
<div style="text-align: center; font-size: 14px;">@aimbotaimy-demi_naga-livingscribe</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 AimbotAimy 🍞🔞 NSFW V-Tuber & Poe's Law 🇷🇺: 3.33 You can (not) redo & Demi 'ドヤ顔' Naga.
| Data | AimbotAimy 🍞🔞 NSFW V-Tuber | Poe's Law 🇷🇺: 3.33 You can (not) redo | Demi 'ドヤ顔' Naga |
| --- | --- | --- | --- |
| Tweets downloaded | 497 | 3242 | 3234 |
| Retweets | 60 | 433 | 909 |
| Short tweets | 125 | 564 | 341 |
| Tweets kept | 312 | 2245 | 1984 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/32v27r5o/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 @aimbotaimy-demi_naga-livingscribe's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2qs4c0sr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2qs4c0sr/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/aimbotaimy-demi_naga-livingscribe')
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/aimbotaimy-demi_naga-livingscribe/1627235967135/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/aimbotaimy-demi_naga-livingscribe
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
AimbotAimy NSFW V-Tuber & Poe's Law 🇷🇺: 3.33 You can (not) redo & Demi 'ドヤ顔' Naga
@aimbotaimy-demi\_naga-livingscribe
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 AimbotAimy NSFW V-Tuber & Poe's Law 🇷🇺: 3.33 You can (not) redo & Demi 'ドヤ顔' Naga.
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 @aimbotaimy-demi\_naga-livingscribe'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
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/1374872808136835072/hPahIg-A_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/1409725677495009283/RPVDIGan_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">AimbotAimy 🍞🔞 NSFW V-Tuber & Demon Lord Yeefi NSFW🔞</div>
<div style="text-align: center; font-size: 14px;">@aimbotaimy-ladydarknest</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 AimbotAimy 🍞🔞 NSFW V-Tuber & Demon Lord Yeefi NSFW🔞.
| Data | AimbotAimy 🍞🔞 NSFW V-Tuber | Demon Lord Yeefi NSFW🔞 |
| --- | --- | --- |
| Tweets downloaded | 528 | 3242 |
| Retweets | 61 | 957 |
| Short tweets | 130 | 392 |
| Tweets kept | 337 | 1893 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/uz56dprc/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 @aimbotaimy-ladydarknest's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1di7czlx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1di7czlx/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/aimbotaimy-ladydarknest')
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/aimbotaimy-ladydarknest/1627245180529/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/aimbotaimy-ladydarknest
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
AimbotAimy NSFW V-Tuber & Demon Lord Yeefi NSFW
@aimbotaimy-ladydarknest
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 AimbotAimy NSFW V-Tuber & Demon Lord Yeefi NSFW.
Data: Tweets downloaded, AimbotAimy NSFW V-Tuber: 528, Demon Lord Yeefi NSFW: 3242
Data: Retweets, AimbotAimy NSFW V-Tuber: 61, Demon Lord Yeefi NSFW: 957
Data: Short tweets, AimbotAimy NSFW V-Tuber: 130, Demon Lord Yeefi NSFW: 392
Data: Tweets kept, AimbotAimy NSFW V-Tuber: 337, Demon Lord Yeefi NSFW: 1893
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 @aimbotaimy-ladydarknest'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1374872808136835072/hPahIg-A_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">AimbotAimy 🍞🔞 NSFW V-Tuber</div>
<div style="text-align: center; font-size: 14px;">@aimbotaimy</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 AimbotAimy 🍞🔞 NSFW V-Tuber.
| Data | AimbotAimy 🍞🔞 NSFW V-Tuber |
| --- | --- |
| Tweets downloaded | 491 |
| Retweets | 59 |
| Short tweets | 125 |
| Tweets kept | 307 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/38rsh6x7/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 @aimbotaimy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2sn41u12) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2sn41u12/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/aimbotaimy')
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/aimbotaimy/1627185142630/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/aimbotaimy
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
AimbotAimy NSFW V-Tuber
@aimbotaimy
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 AimbotAimy NSFW V-Tuber.
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 @aimbotaimy'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
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/1433245625429204993/xzzFE2CJ_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/1405992051427229698/V3W-1gOb_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✨ & everyone in the system this isn’t normal & AK</div>
<div style="text-align: center; font-size: 14px;">@ak92501-cafe_orbitinnit-ihatesinglets</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✨ & everyone in the system this isn’t normal & AK.
| Data | ✨たち Tommy’s an Orbit 🌙 たち✨ | everyone in the system this isn’t normal | AK |
| --- | --- | --- | --- |
| Tweets downloaded | 2256 | 1151 | 3250 |
| Retweets | 1350 | 78 | 403 |
| Short tweets | 323 | 352 | 464 |
| Tweets kept | 583 | 721 | 2383 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/mhwl02od/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 @ak92501-cafe_orbitinnit-ihatesinglets's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/m05466la) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/m05466la/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/ak92501-cafe_orbitinnit-ihatesinglets')
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/ak92501-cafe_orbitinnit-ihatesinglets/1630972983357/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ak92501-cafe_orbitinnit-ihatesinglets
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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 & everyone in the system this isn’t normal & AK
@ak92501-cafe\_orbitinnit-ihatesinglets
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 & everyone in the system this isn’t normal & AK.
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 @ak92501-cafe\_orbitinnit-ihatesinglets'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
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/1017476480501104640/KJ_2cey1_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Sarah Sweeney 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@akasarahjean 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 [@akasarahjean's tweets](https://twitter.com/akasarahjean).
<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'>1116</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'>358</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'>68</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>690</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/2hxdrlnu/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 @akasarahjean's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/38b2s9q1) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/38b2s9q1/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/akasarahjean'</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/akasarahjean/1603135242100/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/akasarahjean
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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">Sarah Sweeney AI Bot </div>
<div style="font-size: 15px; color: #657786">@akasarahjean 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 @akasarahjean'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'>1116</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'>358</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'>68</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>690</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 @akasarahjean'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/akasarahjean'</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 @akasarahjean'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'>1116</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'>358</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'>68</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>690</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 @akasarahjean'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/akasarahjean'</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 @akasarahjean'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'>1116</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'>358</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'>68</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>690</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 @akasarahjean'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/akasarahjean'</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"
] |
[
57,
34,
430,
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[
"passage: 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."
] |
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] |
null | null |
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/1410861223273549825/HwwcW6y2_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">Matthew</div>
<div style="text-align: center; font-size: 14px;">@alampaydavis</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 Matthew.
| Data | Matthew |
| --- | --- |
| Tweets downloaded | 3219 |
| Retweets | 1067 |
| Short tweets | 228 |
| Tweets kept | 1924 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1qawxu8m/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 @alampaydavis's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ub62hd1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ub62hd1/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/alampaydavis')
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/alampaydavis/1626995945354/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alampaydavis
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
Matthew
@alampaydavis
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 Matthew.
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 @alampaydavis'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1227172949033177088/La6S5irD_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Alan O'Callaghan 🤖 AI Bot </div>
<div style="font-size: 15px">@alanbocallaghan 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 [@alanbocallaghan's tweets](https://twitter.com/alanbocallaghan).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3238 |
| Retweets | 319 |
| Short tweets | 218 |
| Tweets kept | 2701 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/96yxlut9/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 @alanbocallaghan's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ma22odg) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ma22odg/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/alanbocallaghan')
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/alanbocallaghan/1616681961860/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alanbocallaghan
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Alan O'Callaghan AI Bot
@alanbocallaghan 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 @alanbocallaghan'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 @alanbocallaghan'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
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/974155432678785024/dFFYSfSi_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Alan Watts 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@alanwattsdaily 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 [@alanwattsdaily's tweets](https://twitter.com/alanwattsdaily).
<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'>3248</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'>4</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'>17</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>3227</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3k8o9ly2/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 @alanwattsdaily's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/32i7r9zd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/32i7r9zd/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/alanwattsdaily'</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/alanwattsdaily/1611766517715/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alanwattsdaily
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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">Alan Watts AI Bot </div>
<div style="font-size: 15px; color: #657786">@alanwattsdaily 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 @alanwattsdaily'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'>3248</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'>4</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'>17</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>3227</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 @alanwattsdaily'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/alanwattsdaily'</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 @alanwattsdaily'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'>3248</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'>4</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'>17</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>3227</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 @alanwattsdaily'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/alanwattsdaily'</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 @alanwattsdaily'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'>3248</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'>4</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'>17</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>3227</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 @alanwattsdaily'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/alanwattsdaily'</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"
] |
[
57,
34,
430,
76,
9,
168,
48,
58
] |
[
"passage: 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."
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1148966885024837635/8ihdfQKv_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Albert 🤖 AI Bot </div>
<div style="font-size: 15px">@albertletranger 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 [@albertletranger's tweets](https://twitter.com/albertletranger).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3230 |
| Retweets | 1299 |
| Short tweets | 362 |
| Tweets kept | 1569 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/x4s90a6l/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 @albertletranger's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/10wrv1a0) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/10wrv1a0/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/albertletranger')
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/albertletranger/1616779907134/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/albertletranger
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Albert AI Bot
@albertletranger 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 @albertletranger'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 @albertletranger'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
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; }
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<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/849030859462246401/ATk3_aiW_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Alberto Bagnai 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@albertobagnai 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 [@albertobagnai's tweets](https://twitter.com/albertobagnai).
<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'>3216</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'>1688</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'>447</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1081</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/p67geizd/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 @albertobagnai's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/1m7quiii) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/1m7quiii/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/albertobagnai'</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/albertobagnai/1600589127001/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/albertobagnai
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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">Alberto Bagnai AI Bot </div>
<div style="font-size: 15px; color: #657786">@albertobagnai 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 @albertobagnai'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'>3216</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'>1688</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'>447</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1081</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 @albertobagnai'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/albertobagnai'</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 @albertobagnai'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'>3216</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'>1688</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'>447</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1081</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 @albertobagnai'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/albertobagnai'</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 @albertobagnai'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'>3216</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'>1688</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'>447</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1081</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 @albertobagnai'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/albertobagnai'</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"
] |
[
57,
34,
432,
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[
"passage: 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."
] |
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] |
null | null |
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/1410065266847985667/Sj4WiXAu_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">albert 🇹🇼</div>
<div style="text-align: center; font-size: 14px;">@albertsstuff</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 albert 🇹🇼.
| Data | albert 🇹🇼 |
| --- | --- |
| Tweets downloaded | 3187 |
| Retweets | 240 |
| Short tweets | 825 |
| Tweets kept | 2122 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2e0c8502/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 @albertsstuff's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2rsgjsom) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2rsgjsom/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/albertsstuff')
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/albertsstuff/1627873459813/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/albertsstuff
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
albert 🇹🇼
@albertsstuff
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 albert 🇹🇼.
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 @albertsstuff'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1425007522067386368/k0GygSdD_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">Albin Kurti</div>
<div style="text-align: center; font-size: 14px;">@albinkurti</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 Albin Kurti.
| Data | Albin Kurti |
| --- | --- |
| Tweets downloaded | 741 |
| Retweets | 32 |
| Short tweets | 11 |
| Tweets kept | 698 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1yhql26z/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 @albinkurti's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/txe5baun) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/txe5baun/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/albinkurti')
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/albinkurti/1644579521299/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/albinkurti
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
Albin Kurti
@albinkurti
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 Albin Kurti.
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 @albinkurti'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1369997482000781312/kRWof8b8_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Albi 🌸 🤖 AI Bot </div>
<div style="font-size: 15px">@albiuwu_ 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 [@albiuwu_'s tweets](https://twitter.com/albiuwu_).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3248 |
| Retweets | 38 |
| Short tweets | 569 |
| Tweets kept | 2641 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1tndawti/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 @albiuwu_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/gswiupus) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/gswiupus/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/albiuwu_')
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/albiuwu_/1617915531860/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/albiuwu_
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Albi AI Bot
@albiuwu\_ 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 @albiuwu\_'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 @albiuwu\_'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/480053897382199298/jZba2UiA_400x400.jpeg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Alec Dawson 🤖 AI Bot </div>
<div style="font-size: 15px">@aledaws 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 [@aledaws's tweets](https://twitter.com/aledaws).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1155 |
| Retweets | 67 |
| Short tweets | 71 |
| Tweets kept | 1017 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3agqmwhg/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 @aledaws's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3xwitci1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3xwitci1/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/aledaws')
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/aledaws/1617245961730/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/aledaws
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Alec Dawson AI Bot
@aledaws 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 @aledaws'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 @aledaws'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
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/1128605157602877441/R2nQEZZZ_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Alexandre Sanchez 🦦 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@alex73630 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 [@alex73630's tweets](https://twitter.com/alex73630).
<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'>3160</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'>928</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'>296</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1936</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/ru1nivmp/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 @alex73630's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/14qg9e3j) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/14qg9e3j/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/alex73630'</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/alex73630/1600703549505/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alex73630
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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">Alexandre Sanchez AI Bot </div>
<div style="font-size: 15px; color: #657786">@alex73630 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 @alex73630'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'>3160</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'>928</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'>296</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1936</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 @alex73630'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/alex73630'</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 @alex73630'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'>3160</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'>928</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'>296</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1936</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 @alex73630'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/alex73630'</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 @alex73630'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'>3160</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'>928</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'>296</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1936</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 @alex73630'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/alex73630'</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"
] |
[
57,
34,
430,
76,
9,
168,
48,
58
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[
"passage: 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."
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1063527363638525952/H-DKF-LP_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Alex Ramek 🤖 AI Bot </div>
<div style="font-size: 15px">@alexanderramek 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 [@alexanderramek's tweets](https://twitter.com/alexanderramek).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 402 |
| Retweets | 171 |
| Short tweets | 60 |
| Tweets kept | 171 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1fmckgrk/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 @alexanderramek's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3clt5uj2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3clt5uj2/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/alexanderramek')
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/alexanderramek/1614096947716/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alexanderramek
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Alex Ramek AI Bot
@alexanderramek 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 @alexanderramek'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 @alexanderramek'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1231013808560197632/QRIgsFUE_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">NomaK96 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@alexfiguii 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 [@alexfiguii's tweets](https://twitter.com/alexfiguii).
<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'>2867</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'>1539</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'>127</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1201</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/38gby6t0/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 @alexfiguii's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/3fa48eut) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/3fa48eut/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/alexfiguii'</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/alexfiguii/1601463760497/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alexfiguii
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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">NomaK96 AI Bot </div>
<div style="font-size: 15px; color: #657786">@alexfiguii 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 @alexfiguii'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'>2867</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'>1539</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'>127</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1201</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 @alexfiguii'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/alexfiguii'</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 @alexfiguii'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'>2867</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'>1539</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'>127</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1201</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 @alexfiguii'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/alexfiguii'</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 @alexfiguii'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'>2867</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'>1539</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'>127</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1201</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 @alexfiguii'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/alexfiguii'</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"
] |
[
57,
34,
432,
77,
9,
169,
48,
58
] |
[
"passage: 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."
] |
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null | null |
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; }
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</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/1186330591383474178/etcJHSkY_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Alexis Perrier 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@alexip 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 [@alexip's tweets](https://twitter.com/alexip).
<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'>3199</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'>2059</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'>49</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1091</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/157sg90v/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @alexip's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/1wz9te3l) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/1wz9te3l/artifacts) is logged and versioned.
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for text generation:
<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/alexip'</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/alexip/1602315863564/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alexip
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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">Alexis Perrier AI Bot </div>
<div style="font-size: 15px; color: #657786">@alexip 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 @alexip'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'>3199</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'>2059</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'>49</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1091</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 @alexip'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/alexip'</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 @alexip'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'>3199</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'>2059</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'>49</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1091</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 @alexip'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/alexip'</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 @alexip'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'>3199</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'>2059</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'>49</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1091</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 @alexip'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/alexip'</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"
] |
[
57,
34,
430,
75,
9,
167,
48,
58
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[
"passage: 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."
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1274068177215827968/g9sB0dE1_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">alexis 🤖 AI Bot </div>
<div style="font-size: 15px">@alexisgallagher 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 [@alexisgallagher's tweets](https://twitter.com/alexisgallagher).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 104 |
| Short tweets | 232 |
| Tweets kept | 2914 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/28ak07sx/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 @alexisgallagher's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1kmu6pnu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1kmu6pnu/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/alexisgallagher')
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/alexisgallagher/1616871355671/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alexisgallagher
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
alexis AI Bot
@alexisgallagher 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 @alexisgallagher'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 @alexisgallagher'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1337389555863982083/GFu_etbo_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">Alexis (she/her) 🏳️⚧️</div>
<div style="text-align: center; font-size: 14px;">@alexisuwualexis</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 Alexis (she/her) 🏳️⚧️.
| Data | Alexis (she/her) 🏳️⚧️ |
| --- | --- |
| Tweets downloaded | 3219 |
| Retweets | 2988 |
| Short tweets | 64 |
| Tweets kept | 167 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/t0aheh4s/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 @alexisuwualexis's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/18q8udnh) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/18q8udnh/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/alexisuwualexis')
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/alexisuwualexis/1624474156240/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alexisuwualexis
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
Alexis (she/her) ️️
@alexisuwualexis
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 Alexis (she/her) ️️.
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 @alexisuwualexis'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/929801699623088129/gNlIjLwr_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Alex Salmond 🤖 AI Bot </div>
<div style="font-size: 15px">@alexsalmond 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 [@alexsalmond's tweets](https://twitter.com/alexsalmond).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3194 |
| Retweets | 1155 |
| Short tweets | 19 |
| Tweets kept | 2020 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1fhlpwx8/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 @alexsalmond's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2esw52d4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2esw52d4/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/alexsalmond')
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/alexsalmond/1617827259731/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alexsalmond
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Alex Salmond AI Bot
@alexsalmond 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 @alexsalmond'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 @alexsalmond'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1104572830123986944/3eG16BFY_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Alexander Wade Craig 🤖 AI Bot </div>
<div style="font-size: 15px">@alexwadecraig 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 [@alexwadecraig's tweets](https://twitter.com/alexwadecraig).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3220 |
| Retweets | 404 |
| Short tweets | 112 |
| Tweets kept | 2704 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3l824189/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 @alexwadecraig's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3kat9k6l) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3kat9k6l/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/alexwadecraig')
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/alexwadecraig/1616646989893/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alexwadecraig
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Alexander Wade Craig AI Bot
@alexwadecraig 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 @alexwadecraig'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 @alexwadecraig'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
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<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/1241620963768201216/sG68m_iE_400x400.jpg')">
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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/1097426990699855873/lEI3EWIL_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">Cyrus & Aleyda Solis 👩🏻💻 & 🧀 John 🧀</div>
<div style="text-align: center; font-size: 14px;">@aleyda-cyrusshepard-johnmu</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 Cyrus & Aleyda Solis 👩🏻💻 & 🧀 John 🧀.
| Data | Cyrus | Aleyda Solis 👩🏻💻 | 🧀 John 🧀 |
| --- | --- | --- | --- |
| Tweets downloaded | 3248 | 3247 | 3251 |
| Retweets | 343 | 995 | 358 |
| Short tweets | 729 | 128 | 267 |
| Tweets kept | 2176 | 2124 | 2626 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3jr2ggcg/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 @aleyda-cyrusshepard-johnmu's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/elwosmqy) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/elwosmqy/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/aleyda-cyrusshepard-johnmu')
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/aleyda-cyrusshepard-johnmu/1632495995480/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/aleyda-cyrusshepard-johnmu
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
Cyrus & Aleyda Solis & John
@aleyda-cyrusshepard-johnmu
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 Cyrus & Aleyda Solis & John .
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 @aleyda-cyrusshepard-johnmu'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1321484463365361664/uJaI229z_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">🏴🏳️🌈Alfy🏳️🌈🚩 🤖 AI Bot </div>
<div style="font-size: 15px">@alfieghill1 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 [@alfieghill1's tweets](https://twitter.com/alfieghill1).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3171 |
| Retweets | 1187 |
| Short tweets | 510 |
| Tweets kept | 1474 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2e2bmrwg/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 @alfieghill1's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1n271342) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1n271342/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/alfieghill1')
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/alfieghill1/1614109293232/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alfieghill1
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
️Alfy️ AI Bot
@alfieghill1 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 @alfieghill1'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 @alfieghill1'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1039301837885661184/UoKzoFb__400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Ali Abunimah 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@aliabunimah 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 [@aliabunimah's tweets](https://twitter.com/aliabunimah).
<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'>3239</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'>1336</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'>96</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1807</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/3esavnex/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 @aliabunimah's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/2mzn2mn5) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/2mzn2mn5/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/aliabunimah'</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/aliabunimah/1603107521865/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/aliabunimah
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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">Ali Abunimah AI Bot </div>
<div style="font-size: 15px; color: #657786">@aliabunimah 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 @aliabunimah'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'>3239</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'>1336</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'>96</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1807</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 @aliabunimah'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/aliabunimah'</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 @aliabunimah'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'>3239</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'>1336</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'>96</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1807</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 @aliabunimah'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/aliabunimah'</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 @aliabunimah'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'>3239</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'>1336</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'>96</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1807</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 @aliabunimah'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/aliabunimah'</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"
] |
[
57,
34,
431,
76,
9,
168,
48,
58
] |
[
"passage: 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."
] |
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null | null |
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/922921690459283456/rwVj6I1R_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Alibaba Group 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@alibabagroup 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 [@alibabagroup's tweets](https://twitter.com/alibabagroup).
<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'>3218</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'>981</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'>31</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2206</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/32a64yp5/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 @alibabagroup's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/30lknnvx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/30lknnvx/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/alibabagroup'</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/alibabagroup/1609715889377/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alibabagroup
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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">Alibaba Group AI Bot </div>
<div style="font-size: 15px; color: #657786">@alibabagroup 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 @alibabagroup'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'>3218</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'>981</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'>31</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2206</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 @alibabagroup'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/alibabagroup'</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 @alibabagroup'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'>3218</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'>981</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'>31</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2206</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 @alibabagroup'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/alibabagroup'</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 @alibabagroup'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'>3218</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'>981</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'>31</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2206</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 @alibabagroup'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/alibabagroup'</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"
] |
[
57,
34,
429,
75,
9,
167,
48,
58
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[
"passage: 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."
] |
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] |
null | null |
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/1410515009252302852/sah1ksNb_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/1393311358293356546/tXc-X9fx_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">Alice Cream 🐇🍓 Vtuber & 👁️⃤ lison</div>
<div style="text-align: center; font-size: 14px;">@alice333ai-alicecweam</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 Alice Cream 🐇🍓 Vtuber & 👁️⃤ lison.
| Data | Alice Cream 🐇🍓 Vtuber | 👁️⃤ lison |
| --- | --- | --- |
| Tweets downloaded | 3244 | 3216 |
| Retweets | 359 | 1062 |
| Short tweets | 463 | 200 |
| Tweets kept | 2422 | 1954 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/4cfpc23c/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @alice333ai-alicecweam's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2r62epp4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2r62epp4/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/alice333ai-alicecweam')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](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/alice333ai-alicecweam/1625611976936/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alice333ai-alicecweam
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
Alice Cream Vtuber & ️⃤ lison
@alice333ai-alicecweam
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 Alice Cream Vtuber & ️⃤ lison.
Data: Tweets downloaded, Alice Cream Vtuber: 3244, ️⃤ lison: 3216
Data: Retweets, Alice Cream Vtuber: 359, ️⃤ lison: 1062
Data: Short tweets, Alice Cream Vtuber: 463, ️⃤ lison: 200
Data: Tweets kept, Alice Cream Vtuber: 2422, ️⃤ lison: 1954
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 @alice333ai-alicecweam'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1393311358293356546/tXc-X9fx_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/1412466315240030217/yDDNt3-0_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">👁️⃤ lison & JJ (comms closed)</div>
<div style="text-align: center; font-size: 14px;">@alice333ai-jj_visuals</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 👁️⃤ lison & JJ (comms closed).
| Data | 👁️⃤ lison | JJ (comms closed) |
| --- | --- | --- |
| Tweets downloaded | 3216 | 3221 |
| Retweets | 1062 | 781 |
| Short tweets | 200 | 229 |
| Tweets kept | 1954 | 2211 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1sqkkxt9/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @alice333ai-jj_visuals's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/327x2oet) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/327x2oet/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/alice333ai-jj_visuals')
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/alice333ai-jj_visuals/1625605011527/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alice333ai-jj_visuals
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
️⃤ lison & JJ (comms closed)
@alice333ai-jj\_visuals
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 ️⃤ lison & JJ (comms closed).
Data: Tweets downloaded, ️⃤ lison: 3216, JJ (comms closed): 3221
Data: Retweets, ️⃤ lison: 1062, JJ (comms closed): 781
Data: Short tweets, ️⃤ lison: 200, JJ (comms closed): 229
Data: Tweets kept, ️⃤ lison: 1954, JJ (comms closed): 2211
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 @alice333ai-jj\_visuals'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1343482928014237696/51aKOINn_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/1408716131867713538/rg3HSZ5D_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/1378382707625975812/vYek426__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">che 💜 & clementine!!!! 𓃠 & 𝓡𝓮𝓭 𝓟𝓪𝓷𝓭𝓪'𝓼 𝓖𝓪𝓶𝓮 𝓒𝓸𝓻𝓷𝓮𝓻</div>
<div style="text-align: center; font-size: 14px;">@aliceaeterna-clamtime-redpandasmash</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 che 💜 & clementine!!!! 𓃠 & 𝓡𝓮𝓭 𝓟𝓪𝓷𝓭𝓪'𝓼 𝓖𝓪𝓶𝓮 𝓒𝓸𝓻𝓷𝓮𝓻.
| Data | che 💜 | clementine!!!! 𓃠 | 𝓡𝓮𝓭 𝓟𝓪𝓷𝓭𝓪'𝓼 𝓖𝓪𝓶𝓮 𝓒𝓸𝓻𝓷𝓮𝓻 |
| --- | --- | --- | --- |
| Tweets downloaded | 1587 | 3187 | 2492 |
| Retweets | 682 | 500 | 367 |
| Short tweets | 158 | 687 | 362 |
| Tweets kept | 747 | 2000 | 1763 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1814x6xo/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 @aliceaeterna-clamtime-redpandasmash's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/kvo9buwa) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/kvo9buwa/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/aliceaeterna-clamtime-redpandasmash')
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/aliceaeterna-clamtime-redpandasmash/1625925715720/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/aliceaeterna-clamtime-redpandasmash
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
che & clementine!!!! 𓃠 & 𝓡𝓮𝓭 𝓟𝓪𝓷𝓭𝓪'𝓼 𝓖𝓪𝓶𝓮 𝓒𝓸𝓻𝓷𝓮𝓻
@aliceaeterna-clamtime-redpandasmash
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 che & clementine!!!! 𓃠 & 𝓡𝓮𝓭 𝓟𝓪𝓷𝓭𝓪'𝓼 𝓖𝓪𝓶𝓮 𝓒𝓸𝓻𝓷𝓮𝓻.
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 @aliceaeterna-clamtime-redpandasmash'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1343482928014237696/51aKOINn_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">che 💜 🤖 AI Bot </div>
<div style="font-size: 15px">@aliceaeterna 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 [@aliceaeterna's tweets](https://twitter.com/aliceaeterna).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1419 |
| Retweets | 586 |
| Short tweets | 130 |
| Tweets kept | 703 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/26doepxr/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 @aliceaeterna's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1any0jue) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1any0jue/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/aliceaeterna')
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"}]}
|
text-generation
|
huggingtweets/aliceaeterna
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
che AI Bot
@aliceaeterna 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 @aliceaeterna'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 @aliceaeterna'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1372804858068230149/aSZcjxvN_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">Dread Alice</div>
<div style="text-align: center; font-size: 14px;">@alicefromqueens</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 Dread Alice.
| Data | Dread Alice |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 50 |
| Short tweets | 511 |
| Tweets kept | 2688 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/frqs20kj/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 @alicefromqueens's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2c7152gp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2c7152gp/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/alicefromqueens')
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/alicefromqueens/1626903533456/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alicefromqueens
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
Dread Alice
@alicefromqueens
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 Dread Alice.
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 @alicefromqueens'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1354527838070779905/9ju2ltnm_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">alice 🌸 🤖 AI Bot </div>
<div style="font-size: 15px">@alicesblossoms 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 [@alicesblossoms's tweets](https://twitter.com/alicesblossoms).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3175 |
| Retweets | 1633 |
| Short tweets | 274 |
| Tweets kept | 1268 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2nufi296/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 @alicesblossoms's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3bwg8ycs) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3bwg8ycs/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/alicesblossoms')
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/alicesblossoms/1614213198879/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alicesblossoms
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
alice AI Bot
@alicesblossoms 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 @alicesblossoms'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 @alicesblossoms'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
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/1386473102171803649/nr3t9kft_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">ali</div>
<div style="text-align: center; font-size: 14px;">@alimaketweet</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 ali.
| Data | ali |
| --- | --- |
| Tweets downloaded | 2478 |
| Retweets | 38 |
| Short tweets | 927 |
| Tweets kept | 1513 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3syq75w5/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 @alimaketweet's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/7cxndgon) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/7cxndgon/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/alimaketweet')
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/alimaketweet/1621724029531/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alimaketweet
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
ali
@alimaketweet
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 ali.
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 @alimaketweet'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1369323247519608836/MsoTG4Ir_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">AAH 🤖 AI Bot </div>
<div style="font-size: 15px">@alisonaharris 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 [@alisonaharris's tweets](https://twitter.com/alisonaharris).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1616 |
| Retweets | 763 |
| Short tweets | 85 |
| Tweets kept | 768 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2hmbkdpe/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 @alisonaharris's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2c6keq3v) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2c6keq3v/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/alisonaharris')
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/alisonaharris/1617826834667/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alisonaharris
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
AAH AI Bot
@alisonaharris 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 @alisonaharris'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 @alisonaharris'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1406680256258482178/79-ZrVAg_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">Alison Selby</div>
<div style="text-align: center; font-size: 14px;">@alisonselby_</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 Alison Selby.
| Data | Alison Selby |
| --- | --- |
| Tweets downloaded | 3218 |
| Retweets | 319 |
| Short tweets | 290 |
| Tweets kept | 2609 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2e6i4sab/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 @alisonselby_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/9gpt8ktz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/9gpt8ktz/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/alisonselby_')
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/alisonselby_/1624473155604/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alisonselby_
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
Alison Selby
@alisonselby\_
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 Alison Selby.
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 @alisonselby\_'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
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/1405236436144508932/5bN_yThT_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/1374924360780242944/-Q8NfgEr_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/1416805116628422660/j0vQ8GP3_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">merzy & wintbot_neo & xoxo</div>
<div style="text-align: center; font-size: 14px;">@alivegirl001101-drilbot_neo-rusticgendarme</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 merzy & wintbot_neo & xoxo.
| Data | merzy | wintbot_neo | xoxo |
| --- | --- | --- | --- |
| Tweets downloaded | 2598 | 3244 | 2731 |
| Retweets | 449 | 218 | 574 |
| Short tweets | 440 | 271 | 812 |
| Tweets kept | 1709 | 2755 | 1345 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3600xjfx/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 @alivegirl001101-drilbot_neo-rusticgendarme's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1cv1jefk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1cv1jefk/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/alivegirl001101-drilbot_neo-rusticgendarme')
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/alivegirl001101-drilbot_neo-rusticgendarme/1627500827534/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alivegirl001101-drilbot_neo-rusticgendarme
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
merzy & wintbot\_neo & xoxo
@alivegirl001101-drilbot\_neo-rusticgendarme
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 merzy & wintbot\_neo & xoxo.
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 @alivegirl001101-drilbot\_neo-rusticgendarme'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1369015830000861191/gWkHCd-b_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">N.O.R.A 🤖 AI Bot </div>
<div style="font-size: 15px">@almostnora 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 [@almostnora's tweets](https://twitter.com/almostnora).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3230 |
| Retweets | 191 |
| Short tweets | 494 |
| Tweets kept | 2545 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3hy929cp/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 @almostnora's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3l9u4t5m) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3l9u4t5m/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/almostnora')
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/almostnora/1616897539959/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/almostnora
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
N.O.R.A AI Bot
@almostnora 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 @almostnora'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 @almostnora'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1280197719571775488/IXebaRCu_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Arturs Logins 🤖 AI Bot </div>
<div style="font-size: 15px">@alogins 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 [@alogins's tweets](https://twitter.com/alogins).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1609 |
| Retweets | 133 |
| Short tweets | 177 |
| Tweets kept | 1299 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ic2ynnv/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 @alogins's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/anvz7gt2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/anvz7gt2/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/alogins')
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/alogins/1616706593981/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alogins
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Arturs Logins AI Bot
@alogins 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 @alogins'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 @alogins'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1320844146664460288/W09Z-oPC_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">April 🤖 AI Bot </div>
<div style="font-size: 15px">@alotoforanges 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 [@alotoforanges's tweets](https://twitter.com/alotoforanges).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3240 |
| Retweets | 186 |
| Short tweets | 552 |
| Tweets kept | 2502 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2rgdnomb/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 @alotoforanges's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1e1tznc6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1e1tznc6/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/alotoforanges')
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/alotoforanges/1616898775163/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alotoforanges
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
@alotoforanges 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 @alotoforanges'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 @alotoforanges'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/711247322114609154/A2hfB3eL_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Alper Çuğun-Gscheidel 🏴🌻 🤖 AI Bot </div>
<div style="font-size: 15px">@alper 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 [@alper's tweets](https://twitter.com/alper).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 0 |
| Short tweets | 129 |
| Tweets kept | 3121 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/21a6dhyx/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 @alper's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/rkrg672y) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/rkrg672y/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/alper')
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/alper/1619479187969/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alper
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Alper Çuğun-Gscheidel AI Bot
@alper 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 @alper'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 @alper'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
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0.01947171241044998,
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] |
null | null |
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/1475337078544248835/JRWM0Hsl_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/1096066608034918401/m8wnTWsX_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/1469027897209987081/fCdlufKH_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">CoinMarketCap & TechCrunch & AlphaExchange</div>
<div style="text-align: center; font-size: 14px;">@alphaxchange-coinmarketcap-techcrunch</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 CoinMarketCap & TechCrunch & AlphaExchange.
| Data | CoinMarketCap | TechCrunch | AlphaExchange |
| --- | --- | --- | --- |
| Tweets downloaded | 3249 | 3250 | 185 |
| Retweets | 247 | 29 | 25 |
| Short tweets | 209 | 9 | 17 |
| Tweets kept | 2793 | 3212 | 143 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ii2008f/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 @alphaxchange-coinmarketcap-techcrunch's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/28z1wzo5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/28z1wzo5/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/alphaxchange-coinmarketcap-techcrunch')
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/alphaxchange-coinmarketcap-techcrunch/1643592683390/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alphaxchange-coinmarketcap-techcrunch
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
CoinMarketCap & TechCrunch & AlphaExchange
@alphaxchange-coinmarketcap-techcrunch
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 CoinMarketCap & TechCrunch & AlphaExchange.
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 @alphaxchange-coinmarketcap-techcrunch'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1357168007055872000/QQez_OqS_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Kia☆ 🤖 AI Bot </div>
<div style="font-size: 15px">@alt_kia 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 [@alt_kia's tweets](https://twitter.com/alt_kia).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3243 |
| Retweets | 715 |
| Short tweets | 449 |
| Tweets kept | 2079 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2oea8dpz/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 @alt_kia's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1aog3cgu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1aog3cgu/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/alt_kia')
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/alt_kia/1616891056624/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alt_kia
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Kia AI Bot
@alt\_kia 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 @alt\_kia'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 @alt\_kia'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
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/1388134163753185283/OrCvyNfy_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/1004150565302034432/kRnEUZA8_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/1343657798895366152/RMYAEzre_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">artchick.eth 🔥 & Altcoin Psycho & JUSTIN</div>
<div style="text-align: center; font-size: 14px;">@altcoinpsycho-digitalartchick-justintrimble</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 artchick.eth 🔥 & Altcoin Psycho & JUSTIN.
| Data | artchick.eth 🔥 | Altcoin Psycho | JUSTIN |
| --- | --- | --- | --- |
| Tweets downloaded | 3250 | 3249 | 3248 |
| Retweets | 142 | 34 | 254 |
| Short tweets | 654 | 461 | 863 |
| Tweets kept | 2454 | 2754 | 2131 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3uuqza2m/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 @altcoinpsycho-digitalartchick-justintrimble's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/gis597aj) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/gis597aj/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/altcoinpsycho-digitalartchick-justintrimble')
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/altcoinpsycho-digitalartchick-justintrimble/1620934521680/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/altcoinpsycho-digitalartchick-justintrimble
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
URL & Altcoin Psycho & JUSTIN
@altcoinpsycho-digitalartchick-justintrimble
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 & Altcoin Psycho & JUSTIN.
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 @altcoinpsycho-digitalartchick-justintrimble'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1467618648961527812/jtH0RZpT_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/1468367771746672643/21w6R4SP_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">Alter Huss & Zainab</div>
<div style="text-align: center; font-size: 14px;">@alterhuss-zainabverse</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 Alter Huss & Zainab.
| Data | Alter Huss | Zainab |
| --- | --- | --- |
| Tweets downloaded | 3229 | 3246 |
| Retweets | 125 | 95 |
| Short tweets | 1004 | 426 |
| Tweets kept | 2100 | 2725 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/8ibzokov/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 @alterhuss-zainabverse's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3d8wr9hg) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3d8wr9hg/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/alterhuss-zainabverse')
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"}]}
|
text-generation
|
huggingtweets/alterhuss-zainabverse
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
Alter Huss & Zainab
@alterhuss-zainabverse
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 Alter Huss & Zainab.
Data: Tweets downloaded, Alter Huss: 3229, Zainab: 3246
Data: Retweets, Alter Huss: 125, Zainab: 95
Data: Short tweets, Alter Huss: 1004, Zainab: 426
Data: Tweets kept, Alter Huss: 2100, Zainab: 2725
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 @alterhuss-zainabverse'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1341634979587977217/1Dg48qEr_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">alth0u 😷🏠💉 🤖 AI Bot </div>
<div style="font-size: 15px">@alth0u 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 [@alth0u's tweets](https://twitter.com/alth0u).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 38 |
| Short tweets | 371 |
| Tweets kept | 2841 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/uywhay29/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 @alth0u's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ipq5xuk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ipq5xuk/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/alth0u')
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/alth0u/1616652713319/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/alth0u
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
alth0u AI Bot
@alth0u 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 @alth0u'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 @alth0u'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/479052171837984768/mlO43FWa_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">Álvaro Uribe Vélez</div>
<div style="text-align: center; font-size: 14px;">@alvarouribevel</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 Álvaro Uribe Vélez.
| Data | Álvaro Uribe Vélez |
| --- | --- |
| Tweets downloaded | 3240 |
| Retweets | 1335 |
| Short tweets | 228 |
| Tweets kept | 1677 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1439yxv6/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 @alvarouribevel's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ly70v6r) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ly70v6r/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/alvarouribevel')
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"}]}
|
text-generation
|
huggingtweets/alvarouribevel
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
Álvaro Uribe Vélez
@alvarouribevel
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 Álvaro Uribe Vélez.
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 @alvarouribevel'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
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/1416541994952937474/yi5cJxnq_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/1368667185879584770/pKNxJut-_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/1393327649318076417/cQWDVv-q_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">haley o'shaughnessy & Santiago & Aly Dixon</div>
<div style="text-align: center; font-size: 14px;">@aly__dixon-haleyosomething-svpino</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 haley o'shaughnessy & Santiago & Aly Dixon.
| Data | haley o'shaughnessy | Santiago | Aly Dixon |
| --- | --- | --- | --- |
| Tweets downloaded | 3241 | 3250 | 3003 |
| Retweets | 430 | 7 | 426 |
| Short tweets | 460 | 316 | 195 |
| Tweets kept | 2351 | 2927 | 2382 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1mt8xsda/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 @aly__dixon-haleyosomething-svpino's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/31g4nsgq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/31g4nsgq/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/aly__dixon-haleyosomething-svpino')
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/aly__dixon-haleyosomething-svpino/1632660543535/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/aly__dixon-haleyosomething-svpino
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
haley o'shaughnessy & Santiago & Aly Dixon
@aly\_\_dixon-haleyosomething-svpino
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 haley o'shaughnessy & Santiago & Aly Dixon.
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 @aly\_\_dixon-haleyosomething-svpino'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
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/949070360103698432/kXSiPeTk_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Amazon 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@amazon 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 [@amazon's tweets](https://twitter.com/amazon).
<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'>3242</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'>40</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'>60</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>3142</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1fd78mc2/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 @amazon's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/76pxw0n0) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/76pxw0n0/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/amazon'</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/amazon/1609713999453/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/amazon
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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">Amazon AI Bot </div>
<div style="font-size: 15px; color: #657786">@amazon 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 @amazon'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'>3242</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'>40</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'>60</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>3142</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 @amazon'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/amazon'</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 @amazon'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'>3242</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'>40</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'>60</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>3142</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 @amazon'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/amazon'</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 @amazon'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'>3242</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'>40</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'>60</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>3142</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 @amazon'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/amazon'</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"
] |
[
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34,
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[
"passage: 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."
] |
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null | null |
transformers
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</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Adi/Gojiᵇˡᵐ⁺ᵇᵗˡᵐ⁺ᵃᶜᵃᵇ 🤖 AI Bot </div>
<div style="font-size: 15px">@amberblaziken 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 [@amberblaziken's tweets](https://twitter.com/amberblaziken).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3195 |
| Retweets | 907 |
| Short tweets | 503 |
| Tweets kept | 1785 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ebavto2/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 @amberblaziken's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2hz6lllz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2hz6lllz/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/amberblaziken')
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/amberblaziken/1617804897376/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/amberblaziken
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Adi/Gojiᵇˡᵐ⁺ᵇᵗˡᵐ⁺ᵃᶜᵃᵇ AI Bot
@amberblaziken 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 @amberblaziken'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 @amberblaziken'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
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/1404698622579462144/8oiBunaK_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/847818629840228354/VXyQHfn0_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">tomboy housewives against cops & wint</div>
<div style="text-align: center; font-size: 14px;">@ambivalegenic-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 tomboy housewives against cops & wint.
| Data | tomboy housewives against cops | wint |
| --- | --- | --- |
| Tweets downloaded | 3154 | 3226 |
| Retweets | 781 | 472 |
| Short tweets | 266 | 304 |
| Tweets kept | 2107 | 2450 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3m5g8gro/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 @ambivalegenic-dril's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/27fdnf8e) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/27fdnf8e/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/ambivalegenic-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/ambivalegenic-dril/1639117433317/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ambivalegenic-dril
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
tomboy housewives against cops & wint
@ambivalegenic-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 tomboy housewives against cops & wint.
Data: Tweets downloaded, tomboy housewives against cops: 3154, wint: 3226
Data: Retweets, tomboy housewives against cops: 781, wint: 472
Data: Short tweets, tomboy housewives against cops: 266, wint: 304
Data: Tweets kept, tomboy housewives against cops: 2107, wint: 2450
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 @ambivalegenic-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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1364898993998680066/stqI7iN8_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">not the kind of princess that is princess-cis 🤖 AI Bot </div>
<div style="font-size: 15px">@ambivalegenic 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 [@ambivalegenic's tweets](https://twitter.com/ambivalegenic).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2614 |
| Retweets | 664 |
| Short tweets | 228 |
| Tweets kept | 1722 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1mvt2owy/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 @ambivalegenic's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/25yttpuo) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/25yttpuo/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/ambivalegenic')
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/ambivalegenic/1616659230833/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ambivalegenic
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
not the kind of princess that is princess-cis AI Bot
@ambivalegenic 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 @ambivalegenic'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 @ambivalegenic'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/83933348/IMG00128_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Alan McCarty 🤖 AI Bot </div>
<div style="font-size: 15px">@amccarty 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 [@amccarty's tweets](https://twitter.com/amccarty).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 569 |
| Retweets | 172 |
| Short tweets | 30 |
| Tweets kept | 367 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/l51uxin3/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 @amccarty's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1bw34kk4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1bw34kk4/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/amccarty')
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/amccarty/1617899959147/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/amccarty
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Alan McCarty AI Bot
@amccarty 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 @amccarty'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 @amccarty'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1453350245383946240/cBFwCk3J_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">Amelka</div>
<div style="text-align: center; font-size: 14px;">@amelamelcia</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 Amelka.
| Data | Amelka |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 101 |
| Short tweets | 550 |
| Tweets kept | 2593 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/tomda94s/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @amelamelcia's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1hxvf49x) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1hxvf49x/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/amelamelcia')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](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/amelamelcia/1637950041914/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/amelamelcia
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
Amelka
@amelamelcia
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 Amelka.
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 @amelamelcia'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1347029113173798912/ayKe9SJB_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Quilogorath 🤖 AI Bot </div>
<div style="font-size: 15px">@americanpineapp 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 [@americanpineapp's tweets](https://twitter.com/americanpineapp).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3205 |
| Retweets | 1339 |
| Short tweets | 446 |
| Tweets kept | 1420 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ouupjoy/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 @americanpineapp's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/x8qz0hii) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/x8qz0hii/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/americanpineapp')
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/americanpineapp/1617768265807/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/americanpineapp
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Quilogorath AI Bot
@americanpineapp 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 @americanpineapp'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 @americanpineapp'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1374784520742866949/RBO-C7n8_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Amir of Amirs 🤖 AI Bot </div>
<div style="font-size: 15px">@amirism_ 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 [@amirism_'s tweets](https://twitter.com/amirism_).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 137 |
| Short tweets | 655 |
| Tweets kept | 2454 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3jwwptdm/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 @amirism_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/jf0rjdbf) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/jf0rjdbf/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/amirism_')
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/amirism_/1616611950115/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/amirism_
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Amir of Amirs AI Bot
@amirism\_ 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 @amirism\_'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 @amirism\_'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1324792261775798272/hlRK8lBU_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Anne-Marie Scott 🤖 AI Bot </div>
<div style="font-size: 15px">@ammienoot 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 [@ammienoot's tweets](https://twitter.com/ammienoot).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3251 |
| Retweets | 355 |
| Short tweets | 209 |
| Tweets kept | 2687 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/372xzuxt/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 @ammienoot's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2l19ykmz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2l19ykmz/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/ammienoot')
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"}]}
|
text-generation
|
huggingtweets/ammienoot
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Anne-Marie Scott AI Bot
@ammienoot 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 @ammienoot'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 @ammienoot'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1433365322313043974/gPI08qaY_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/1377835980552474624/sxTjuspv_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">halal talal & amna</div>
<div style="text-align: center; font-size: 14px;">@amnananadeem-talal916</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 halal talal & amna.
| Data | halal talal | amna |
| --- | --- | --- |
| Tweets downloaded | 3187 | 3132 |
| Retweets | 484 | 778 |
| Short tweets | 532 | 369 |
| Tweets kept | 2171 | 1985 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/42dvu161/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 @amnananadeem-talal916's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2irbhtmu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2irbhtmu/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/amnananadeem-talal916')
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"}]}
|
text-generation
|
huggingtweets/amnananadeem-talal916
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
halal talal & amna
@amnananadeem-talal916
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 halal talal & amna.
Data: Tweets downloaded, halal talal: 3187, amna: 3132
Data: Retweets, halal talal: 484, amna: 778
Data: Short tweets, halal talal: 532, amna: 369
Data: Tweets kept, halal talal: 2171, amna: 1985
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 @amnananadeem-talal916'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1455270159975796736/PqmjT7Dr_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">Among Us</div>
<div style="text-align: center; font-size: 14px;">@amongusgame</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 Among Us.
| Data | Among Us |
| --- | --- |
| Tweets downloaded | 3248 |
| Retweets | 75 |
| Short tweets | 1295 |
| Tweets kept | 1878 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2pyl3gg2/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 @amongusgame's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1vvgbbml) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1vvgbbml/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/amongusgame')
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/amongusgame/1643327156823/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/amongusgame
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
Among Us
@amongusgame
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 Among Us.
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 @amongusgame'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1377851124569370625/vh0fnxXt_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">amphy #nicechan 🤖 AI Bot </div>
<div style="font-size: 15px">@amphydelic 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 [@amphydelic's tweets](https://twitter.com/amphydelic).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3142 |
| Retweets | 770 |
| Short tweets | 711 |
| Tweets kept | 1661 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3o1nuvfq/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 @amphydelic's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3mitl8mt) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3mitl8mt/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/amphydelic')
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/amphydelic/1617771402481/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/amphydelic
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
amphy #nicechan AI Bot
@amphydelic 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 @amphydelic'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 @amphydelic'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
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/1309620020981374976/VD0TF3jf_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">ANA’s SOUL v The Machine 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@ana_couper 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 [@ana_couper's tweets](https://twitter.com/ana_couper).
<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'>231</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'>1138</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1844</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/13v94unk/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 @ana_couper's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/1bnsypnj) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/1bnsypnj/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/ana_couper'</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/ana_couper/1601267274995/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ana_couper
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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">ANA’s SOUL v The Machine AI Bot </div>
<div style="font-size: 15px; color: #657786">@ana_couper 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 @ana_couper'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'>231</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'>1138</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1844</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 @ana_couper'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/ana_couper'</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 @ana_couper'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'>231</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'>1138</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1844</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 @ana_couper'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/ana_couper'</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 @ana_couper'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'>231</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'>1138</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1844</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 @ana_couper'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/ana_couper'</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"
] |
[
57,
34,
432,
76,
9,
168,
48,
58
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[
"passage: 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."
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1304485450103439360/mD4PsYPQ_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Clara, Social Distancing World Champ (2010-2019) 🤖 AI Bot </div>
<div style="font-size: 15px">@analogcitizen 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 [@analogcitizen's tweets](https://twitter.com/analogcitizen).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2997 |
| Retweets | 1309 |
| Short tweets | 189 |
| Tweets kept | 1499 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3od4vbha/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 @analogcitizen's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1del2d6l) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1del2d6l/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/analogcitizen')
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/analogcitizen/1617805157885/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/analogcitizen
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Clara, Social Distancing World Champ (2010-2019) AI Bot
@analogcitizen 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 @analogcitizen'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 @analogcitizen'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1372091789549654016/L09IStLl_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Terra 🐧🏴☠️ 🤖 AI Bot </div>
<div style="font-size: 15px">@anarchystax 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 [@anarchystax's tweets](https://twitter.com/anarchystax).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 239 |
| Retweets | 59 |
| Short tweets | 43 |
| Tweets kept | 137 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ouqtufl/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 @anarchystax's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3d1tkfmr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3d1tkfmr/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/anarchystax')
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/anarchystax/1616622386680/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/anarchystax
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Terra ️ AI Bot
@anarchystax 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 @anarchystax'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 @anarchystax'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1364764641633701889/wk_YVSbd_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">N.P.C. Lovecraft 🤖 AI Bot </div>
<div style="font-size: 15px">@ancapkid 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 [@ancapkid's tweets](https://twitter.com/ancapkid).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2738 |
| Retweets | 166 |
| Short tweets | 589 |
| Tweets kept | 1983 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1to3139m/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 @ancapkid's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/27sth5f2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/27sth5f2/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/ancapkid')
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/ancapkid/1617897872455/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ancapkid
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
N.P.C. Lovecraft AI Bot
@ancapkid 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 @ancapkid'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 @ancapkid'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1376978076962291717/HedQhFmm_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Abigail Devereaux 🏴 🏳️🌈🐿️ 🤘 🤖 AI Bot </div>
<div style="font-size: 15px">@andevereaux 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 [@andevereaux's tweets](https://twitter.com/andevereaux).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3239 |
| Retweets | 359 |
| Short tweets | 240 |
| Tweets kept | 2640 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1q4g34cr/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 @andevereaux's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3dbw2lmp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3dbw2lmp/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/andevereaux')
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/andevereaux/1617929324096/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/andevereaux
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Abigail Devereaux ️️ AI Bot
@andevereaux 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 @andevereaux'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 @andevereaux'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
transformers
|
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<style>
@media (prefers-color-scheme: dark) {
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.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
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<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/962354978680487937/EXnFWdcZ_400x400.jpg')">
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<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Andrés Dae Keun Kwon 권대건 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@andreskwon 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 [@andreskwon's tweets](https://twitter.com/andreskwon).
<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'>3150</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'>2468</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'>163</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>519</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/1magewvo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @andreskwon's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/2en2cxq7) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/2en2cxq7/artifacts) is logged and versioned.
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for text generation:
<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/andreskwon'</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/andreskwon/1600798823307/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/andreskwon
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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">Andrés Dae Keun Kwon 권대건 AI Bot </div>
<div style="font-size: 15px; color: #657786">@andreskwon 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 @andreskwon'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'>3150</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'>2468</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'>163</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>519</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 @andreskwon'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/andreskwon'</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 @andreskwon'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'>3150</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'>2468</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'>163</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>519</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 @andreskwon'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/andreskwon'</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 @andreskwon'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'>3150</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'>2468</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'>163</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>519</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 @andreskwon'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/andreskwon'</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"
] |
[
57,
34,
430,
75,
9,
167,
48,
58
] |
[
"passage: 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."
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/999284567369383936/Zm7tWU0S_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Andrew Cuomo 🤖 AI Bot </div>
<div style="font-size: 15px">@andrewcuomo 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 [@andrewcuomo's tweets](https://twitter.com/andrewcuomo).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1074 |
| Retweets | 353 |
| Short tweets | 9 |
| Tweets kept | 712 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2slpq0r3/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 @andrewcuomo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/39xi2g7u) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/39xi2g7u/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/andrewcuomo')
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/andrewcuomo/1619299470278/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/andrewcuomo
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Andrew Cuomo AI Bot
@andrewcuomo 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 @andrewcuomo'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 @andrewcuomo'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
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/1160595659482902528/qDolL48j_400x400.png')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Andrew Fleer 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@andrewfleer 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 [@andrewfleer's tweets](https://twitter.com/andrewfleer).
<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'>3177</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'>691</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'>493</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1993</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/sln2oh3p/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 @andrewfleer's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/1zx31faw) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/1zx31faw/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/andrewfleer'</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/andrewfleer/1602258436498/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/andrewfleer
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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">Andrew Fleer AI Bot </div>
<div style="font-size: 15px; color: #657786">@andrewfleer 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 @andrewfleer'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'>3177</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'>691</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'>493</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1993</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 @andrewfleer'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/andrewfleer'</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 @andrewfleer'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'>3177</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'>691</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'>493</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1993</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 @andrewfleer'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/andrewfleer'</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 @andrewfleer'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'>3177</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'>691</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'>493</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1993</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 @andrewfleer'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/andrewfleer'</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"
] |
[
57,
34,
431,
76,
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[
"passage: 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."
] |
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null | null |
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/1450701284223324169/JBNbe32v_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">Angad Singh Chowdhry</div>
<div style="text-align: center; font-size: 14px;">@angadc</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 Angad Singh Chowdhry.
| Data | Angad Singh Chowdhry |
| --- | --- |
| Tweets downloaded | 3229 |
| Retweets | 567 |
| Short tweets | 685 |
| Tweets kept | 1977 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1wsxza1p/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 @angadc's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ck4g0as) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ck4g0as/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/angadc')
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/angadc/1635793364907/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/angadc
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
Angad Singh Chowdhry
@angadc
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 Angad Singh Chowdhry.
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 @angadc'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/817164380081180673/TJnt3Lxe_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">Angelina Jolie</div>
<div style="text-align: center; font-size: 14px;">@angiejolielive</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 Angelina Jolie.
| Data | Angelina Jolie |
| --- | --- |
| Tweets downloaded | 1118 |
| Retweets | 71 |
| Short tweets | 45 |
| Tweets kept | 1002 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3fb12gam/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 @angiejolielive's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2g9ynpkt) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2g9ynpkt/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/angiejolielive')
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/angiejolielive/1638476268574/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/angiejolielive
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
Angelina Jolie
@angiejolielive
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 Angelina Jolie.
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 @angiejolielive'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1220764691829608448/QWMxSgNV_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Angle of Ocean 🤖 AI Bot </div>
<div style="font-size: 15px">@angularocean 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 [@angularocean's tweets](https://twitter.com/angularocean).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2933 |
| Retweets | 843 |
| Short tweets | 430 |
| Tweets kept | 1660 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1q9wm9nt/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 @angularocean's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1fr77sf3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1fr77sf3/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/angularocean')
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/angularocean/1616713094074/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/angularocean
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Angle of Ocean AI Bot
@angularocean 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 @angularocean'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 @angularocean'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
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/1340757816030720001/4S-FCkbq_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Ocupado a ver animes 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@animemajg 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 [@animemajg's tweets](https://twitter.com/animemajg).
<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'>3208</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'>42</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'>1190</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1976</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/10aspnal/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 @animemajg's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/37uq91db) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/37uq91db/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/animemajg'</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/animemajg/1608731707053/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/animemajg
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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">Ocupado a ver animes AI Bot </div>
<div style="font-size: 15px; color: #657786">@animemajg 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 @animemajg'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'>3208</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'>42</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'>1190</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1976</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 @animemajg'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/animemajg'</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 @animemajg'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'>3208</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'>42</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'>1190</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1976</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 @animemajg'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/animemajg'</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 @animemajg'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'>3208</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'>42</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'>1190</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1976</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 @animemajg'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/animemajg'</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"
] |
[
57,
34,
430,
76,
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48,
58
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[
"passage: 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."
] |
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] |
null | null |
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/1651738261679734786/nx4BYc5Q_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">Anitta</div>
<div style="text-align: center; font-size: 14px;">@anitta</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 Anitta.
| Data | Anitta |
| --- | --- |
| Tweets downloaded | 3232 |
| Retweets | 810 |
| Short tweets | 534 |
| Tweets kept | 1888 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/376pphij/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 @anitta's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/b4ef3pb5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/b4ef3pb5/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/anitta')
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"}]}
|
text-generation
|
huggingtweets/anitta
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
Anitta
@anitta
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 Anitta.
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 @anitta'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1392739173979680768/0-9vXPxR_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">Annas</div>
<div style="text-align: center; font-size: 14px;">@annasvirtual</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 Annas.
| Data | Annas |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 90 |
| Short tweets | 1495 |
| Tweets kept | 1662 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2n0tmbbi/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 @annasvirtual's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/133nq2yx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/133nq2yx/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/annasvirtual')
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/annasvirtual/1623063516917/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/annasvirtual
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
Annas
@annasvirtual
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 Annas.
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 @annasvirtual'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"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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/1124513114463117313/QdJB-yA6_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Anne Billot 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@annel3illot 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 [@annel3illot's tweets](https://twitter.com/annel3illot).
<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'>128</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'>74</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'>1</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>53</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/qrxucuqf/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 @annel3illot's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/3e99l3sl) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/3e99l3sl/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/annel3illot'</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"}]}
|
text-generation
|
huggingtweets/annel3illot
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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">Anne Billot AI Bot </div>
<div style="font-size: 15px; color: #657786">@annel3illot 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 @annel3illot'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'>128</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'>74</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'>1</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>53</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 @annel3illot'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/annel3illot'</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 @annel3illot'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'>128</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'>74</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'>1</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>53</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 @annel3illot'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/annel3illot'</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 @annel3illot'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'>128</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'>74</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'>1</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>53</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 @annel3illot'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/annel3illot'</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"
] |
[
57,
34,
429,
77,
9,
169,
48,
58
] |
[
"passage: 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."
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1362547638919442435/emFneWlj_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">♡ anneliese ♡ 🤖 AI Bot </div>
<div style="font-size: 15px">@annepliese 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 [@annepliese's tweets](https://twitter.com/annepliese).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1173 |
| Retweets | 256 |
| Short tweets | 138 |
| Tweets kept | 779 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1eyoidqu/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 @annepliese's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1vex6b74) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1vex6b74/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/annepliese')
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/annepliese/1614355840660/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/annepliese
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
anneliese AI Bot
@annepliese 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 @annepliese'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 @annepliese'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1209379972382461953/NQYeAuam_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Ann Hearse 🤖 AI Bot </div>
<div style="font-size: 15px">@annhertzz 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 [@annhertzz's tweets](https://twitter.com/annhertzz).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 34 |
| Short tweets | 501 |
| Tweets kept | 2712 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3dqfesxb/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 @annhertzz's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/201k0gu2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/201k0gu2/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/annhertzz')
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/annhertzz/1617750291511/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/annhertzz
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Ann Hearse AI Bot
@annhertzz 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 @annhertzz'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 @annhertzz'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
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/1186212398006505472/YSiUz0Bt_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">anya 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@annieqqqqqq 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 [@annieqqqqqq's tweets](https://twitter.com/annieqqqqqq).
<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'>559</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'>108</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'>120</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>331</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1s097d58/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 @annieqqqqqq's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/zr7lf2if) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/zr7lf2if/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/annieqqqqqq'</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/annieqqqqqq/1608310442616/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/annieqqqqqq
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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">anya AI Bot </div>
<div style="font-size: 15px; color: #657786">@annieqqqqqq 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 @annieqqqqqq'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'>559</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'>108</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'>120</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>331</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 @annieqqqqqq'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/annieqqqqqq'</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 @annieqqqqqq'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'>559</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'>108</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'>120</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>331</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 @annieqqqqqq'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/annieqqqqqq'</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 @annieqqqqqq'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'>559</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'>108</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'>120</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>331</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 @annieqqqqqq'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/annieqqqqqq'</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"
] |
[
57,
34,
430,
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[
"passage: 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."
] |
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] |
null | null |
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/1356042144801316867/hwbU_t5x_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">FD Viktor Arvidsson</div>
<div style="text-align: center; font-size: 14px;">@anotherday____</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 FD Viktor Arvidsson.
| Data | FD Viktor Arvidsson |
| --- | --- |
| Tweets downloaded | 3248 |
| Retweets | 241 |
| Short tweets | 633 |
| Tweets kept | 2374 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/s1zlupxg/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 @anotherday____'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3tsuctw7) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3tsuctw7/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/anotherday____')
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/anotherday____/1621191811798/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/anotherday____
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
FD Viktor Arvidsson
@anotherday\_\_\_\_
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 FD Viktor Arvidsson.
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 @anotherday\_\_\_\_'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1354989664906563587/F91Gg-Qj_400x400.png')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Another Eden: The Cat Beyond Time and Space 🤖 AI Bot </div>
<div style="font-size: 15px">@anotheredenrpg 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 [@anotheredenrpg's tweets](https://twitter.com/anotheredenrpg).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1651 |
| Retweets | 82 |
| Short tweets | 75 |
| Tweets kept | 1494 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1gpo3g75/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 @anotheredenrpg's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/rb3206ol) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/rb3206ol/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/anotheredenrpg')
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/anotheredenrpg/1615596205043/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/anotheredenrpg
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Another Eden: The Cat Beyond Time and Space AI Bot
@anotheredenrpg 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 @anotheredenrpg'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 @anotheredenrpg'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1375290305046528001/ghCDyYfm_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Another 🤖 AI Bot </div>
<div style="font-size: 15px">@anotherpattern 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 [@anotherpattern's tweets](https://twitter.com/anotherpattern).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1137 |
| Retweets | 2 |
| Short tweets | 147 |
| Tweets kept | 988 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3lb52jwv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @anotherpattern's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/33p25f4f) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/33p25f4f/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/anotherpattern')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](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/anotherpattern/1616768869837/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/anotherpattern
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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
|
Another AI Bot
@anotherpattern 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 @anotherpattern'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 @anotherpattern'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"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
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/1248767771875446784/m1vW-bvg_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Anoush #JusticeforBreonnaTaylor 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@anoushnajarian 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 [@anoushnajarian's tweets](https://twitter.com/anoushnajarian).
<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'>3208</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'>2926</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'>119</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>163</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/32edy0zq/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 @anoushnajarian's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/47coh41e) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/47coh41e/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/anoushnajarian'</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/anoushnajarian/1604082481494/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/anoushnajarian
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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">Anoush #JusticeforBreonnaTaylor AI Bot </div>
<div style="font-size: 15px; color: #657786">@anoushnajarian 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 @anoushnajarian'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'>3208</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'>2926</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'>119</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>163</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 @anoushnajarian'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/anoushnajarian'</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 @anoushnajarian'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'>3208</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'>2926</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'>119</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>163</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 @anoushnajarian'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/anoushnajarian'</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 @anoushnajarian'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'>3208</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'>2926</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'>119</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>163</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 @anoushnajarian'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/anoushnajarian'</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"
] |
[
57,
34,
431,
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[
"passage: 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."
] |
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] |
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