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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/1436764466868273159/z-bXRwzQ_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Scientist</div>
<div style="text-align: center; font-size: 14px;">@ihavesexhourly</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 Scientist.
| Data | Scientist |
| --- | --- |
| Tweets downloaded | 3205 |
| Retweets | 841 |
| Short tweets | 621 |
| Tweets kept | 1743 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2qyzrpd8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ihavesexhourly's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/m2o7mtpw) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/m2o7mtpw/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ihavesexhourly')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/ihavesexhourly/1631841194880/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ihavesexhourly
|
[
"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
Scientist
@ihavesexhourly
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 Scientist.
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 @ihavesexhourly'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/1448859687449862147/frVD6mW3_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">juju 💰</div>
<div style="text-align: center; font-size: 14px;">@ihyjuju</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 juju 💰.
| Data | juju 💰 |
| --- | --- |
| Tweets downloaded | 3248 |
| Retweets | 1 |
| Short tweets | 478 |
| Tweets kept | 2769 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3n82hqbg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ihyjuju's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1t6rclcz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1t6rclcz/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ihyjuju')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/ihyjuju/1640741515385/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ihyjuju
|
[
"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
juju
@ihyjuju
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 juju .
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 @ihyjuju'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/1179205017020063744/WnOlftVe_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">¯\_(ツ)_/¯</div>
<div style="text-align: center; font-size: 14px;">@ijustbluemyself</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 ¯\_(ツ)_/¯.
| Data | ¯\_(ツ)_/¯ |
| --- | --- |
| Tweets downloaded | 3224 |
| Retweets | 250 |
| Short tweets | 982 |
| Tweets kept | 1992 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/qgmk16ox/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ijustbluemyself's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2yq2ve7k) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2yq2ve7k/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ijustbluemyself')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](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/ijustbluemyself/1625279746808/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ijustbluemyself
|
[
"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
¯\\_(ツ)\_/¯
@ijustbluemyself
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 ¯\_(ツ)\_/¯.
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 @ijustbluemyself'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/2429657879/vq8ux7qvn4ljg9oh7zzu_400x400.jpeg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Iván Díaz 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@ildiazm 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 [@ildiazm's tweets](https://twitter.com/ildiazm).
<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'>574</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'>72</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'>12</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>490</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/3cn99ecb/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ildiazm's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/167ssmah) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/167ssmah/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/ildiazm'</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/ildiazm
|
[
"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">Iván Díaz AI Bot </div>
<div style="font-size: 15px; color: #657786">@ildiazm 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 @ildiazm'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'>574</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'>72</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'>12</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>490</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 @ildiazm'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/ildiazm'</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 @ildiazm'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'>574</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'>72</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'>12</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>490</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 @ildiazm'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/ildiazm'</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 @ildiazm'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'>574</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'>72</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'>12</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>490</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 @ildiazm'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/ildiazm'</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,
427,
75,
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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/1365435709981532165/v6Dv3nvt_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Banon 🤖 AI Bot </div>
<div style="font-size: 15px">@ilike_birds 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 [@ilike_birds's tweets](https://twitter.com/ilike_birds).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1017 |
| Retweets | 39 |
| Short tweets | 337 |
| Tweets kept | 641 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/21wt3y4x/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ilike_birds's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2g2q8s1w) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2g2q8s1w/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ilike_birds')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/ilike_birds/1617813434047/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ilike_birds
|
[
"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
|
Banon AI Bot
@ilike\_birds 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 @ilike\_birds'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 @ilike\_birds'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/1370460588334268419/h0Y0-Ny__400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">IllinoisJones 🤖 AI Bot </div>
<div style="font-size: 15px">@iljone 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 [@iljone's tweets](https://twitter.com/iljone).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 337 |
| Retweets | 6 |
| Short tweets | 99 |
| Tweets kept | 232 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3l85ym1p/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @iljone's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3uzsj96o) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3uzsj96o/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/iljone')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/iljone/1616774453050/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/iljone
|
[
"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
|
IllinoisJones AI Bot
@iljone 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 @iljone'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 @iljone'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/1247219435338756099/wUX8KxD4_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Josh Cherry 🌱 🤖 AI Bot </div>
<div style="font-size: 15px">@ilovelucilius 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 [@ilovelucilius's tweets](https://twitter.com/ilovelucilius).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 331 |
| Retweets | 42 |
| Short tweets | 9 |
| Tweets kept | 280 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ztd1uk0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ilovelucilius's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1gbbrvx4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1gbbrvx4/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ilovelucilius')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/ilovelucilius/1616644679483/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ilovelucilius
|
[
"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
|
Josh Cherry AI Bot
@ilovelucilius 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 @ilovelucilius'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 @ilovelucilius'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/1425684733753626624/q521TgTG_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">Ilya Sutskever</div>
<div style="text-align: center; font-size: 14px;">@ilyasut</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 Ilya Sutskever.
| Data | Ilya Sutskever |
| --- | --- |
| Tweets downloaded | 852 |
| Retweets | 474 |
| Short tweets | 39 |
| Tweets kept | 339 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/y41t187f/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ilyasut's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2slwglzj) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2slwglzj/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ilyasut')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/ilyasut/1653408370188/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ilyasut
|
[
"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
Ilya Sutskever
@ilyasut
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 Ilya Sutskever.
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 @ilyasut'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/1356115782606852103/lawv78Xb_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Brown Timothée Chalamet 🤖 AI Bot </div>
<div style="font-size: 15px">@imaginary_bi 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 [@imaginary_bi's tweets](https://twitter.com/imaginary_bi).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1204 |
| Retweets | 189 |
| Short tweets | 72 |
| Tweets kept | 943 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3srr04nu/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @imaginary_bi's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3773072h) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3773072h/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/imaginary_bi')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/imaginary_bi/1614117239005/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/imaginary_bi
|
[
"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
|
Brown Timothée Chalamet AI Bot
@imaginary\_bi 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 @imaginary\_bi'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 @imaginary\_bi'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/1354295229654958081/FUhOGuYV_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Ace 🤖 AI Bot </div>
<div style="font-size: 15px">@imcummingonline 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 [@imcummingonline's tweets](https://twitter.com/imcummingonline).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 914 |
| Retweets | 88 |
| Short tweets | 218 |
| Tweets kept | 608 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2yh36yxx/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @imcummingonline's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3nnnr0u8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3nnnr0u8/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/imcummingonline')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/imcummingonline/1617770513198/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/imcummingonline
|
[
"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
|
Ace AI Bot
@imcummingonline 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 @imcummingonline'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 @imcummingonline'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/1397711387380617219/Hzreffrt_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">Contra</div>
<div style="text-align: center; font-size: 14px;">@imgrimevil</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 Contra.
| Data | Contra |
| --- | --- |
| Tweets downloaded | 3238 |
| Retweets | 669 |
| Short tweets | 582 |
| Tweets kept | 1987 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1kn7qqp8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @imgrimevil's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/fjaoumhd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/fjaoumhd/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/imgrimevil')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/imgrimevil/1627251988335/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/imgrimevil
|
[
"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
Contra
@imgrimevil
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 Contra.
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 @imgrimevil'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/1289653820071522304/cdikNvkG_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">Jack Rudd 🇹🇹 🏳️⚧️</div>
<div style="text-align: center; font-size: 14px;">@imjackrudd</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 Jack Rudd 🇹🇹 🏳️⚧️.
| Data | Jack Rudd 🇹🇹 🏳️⚧️ |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 55 |
| Short tweets | 327 |
| Tweets kept | 2864 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3g5589wt/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @imjackrudd's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/eyywpszu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/eyywpszu/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/imjackrudd')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/imjackrudd/1632871893609/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/imjackrudd
|
[
"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
Jack Rudd 🇹🇹 ️️
@imjackrudd
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 Jack Rudd 🇹🇹 ️️.
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 @imjackrudd'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/1363904099922677762/CZEhI56N_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Luca 🤖 AI Bot </div>
<div style="font-size: 15px">@imjustluca 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 [@imjustluca's tweets](https://twitter.com/imjustluca).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3218 |
| Retweets | 379 |
| Short tweets | 261 |
| Tweets kept | 2578 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ap66ek7/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @imjustluca's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1qfi3jgq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1qfi3jgq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/imjustluca')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/imjustluca/1614160603911/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/imjustluca
|
[
"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
|
Luca AI Bot
@imjustluca 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 @imjustluca'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 @imjustluca'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/1298909619721388035/1v9WJxu7_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Jaelynn 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@imjustuhgrl 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 [@imjustuhgrl's tweets](https://twitter.com/imjustuhgrl).
<table style='border-width:0'>
<thead style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
<th style='border-width:0'>Data</th>
<th style='border-width:0'>Quantity</th>
</tr>
</thead>
<tbody style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Tweets downloaded</td>
<td style='border-width:0'>3236</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Retweets</td>
<td style='border-width:0'>15</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'>512</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2709</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/4phdk9xl/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @imjustuhgrl's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/22432rm3) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/22432rm3/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/imjustuhgrl'</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/imjustuhgrl/1601318938681/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/imjustuhgrl
|
[
"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">Jaelynn AI Bot </div>
<div style="font-size: 15px; color: #657786">@imjustuhgrl 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 @imjustuhgrl's tweets.
<table style='border-width:0'>
<thead style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
<th style='border-width:0'>Data</th>
<th style='border-width:0'>Quantity</th>
</tr>
</thead>
<tbody style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Tweets downloaded</td>
<td style='border-width:0'>3236</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Retweets</td>
<td style='border-width:0'>15</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'>512</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2709</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 @imjustuhgrl'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/imjustuhgrl'</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 @imjustuhgrl's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3236</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>15</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'>512</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2709</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 @imjustuhgrl'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/imjustuhgrl'</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 @imjustuhgrl's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3236</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>15</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'>512</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2709</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 @imjustuhgrl'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/imjustuhgrl'</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
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1371650533111529472/0wqXcosZ_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">gabagol cawfee 🤖 AI Bot </div>
<div style="font-size: 15px">@immarxistonline 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 [@immarxistonline's tweets](https://twitter.com/immarxistonline).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3226 |
| Retweets | 340 |
| Short tweets | 732 |
| Tweets kept | 2154 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3f3uoi57/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @immarxistonline's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1tynoxd5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1tynoxd5/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/immarxistonline')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/immarxistonline/1617769482090/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/immarxistonline
|
[
"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
|
gabagol cawfee AI Bot
@immarxistonline 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 @immarxistonline'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 @immarxistonline'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/1276507373260214275/RZ9iZEmJ_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">The Immersive Kind 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@immersivekind 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 [@immersivekind's tweets](https://twitter.com/immersivekind).
<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'>435</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'>171</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'>4</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>260</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1bh9dpmh/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @immersivekind's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ovh81f8f) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ovh81f8f/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/immersivekind'</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://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/immersivekind
|
[
"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">The Immersive Kind AI Bot </div>
<div style="font-size: 15px; color: #657786">@immersivekind 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 @immersivekind'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'>435</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'>171</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'>4</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>260</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 @immersivekind'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/immersivekind'</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 @immersivekind'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'>435</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'>171</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'>4</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>260</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 @immersivekind'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/immersivekind'</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 @immersivekind'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'>435</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'>171</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'>4</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>260</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 @immersivekind'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/immersivekind'</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,
428,
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/1297793420094607360/3hhcM4L2_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">🇨🇦📎 🤖 AI Bot </div>
<div style="font-size: 15px">@imnotseto 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 [@imnotseto's tweets](https://twitter.com/imnotseto).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 342 |
| Retweets | 15 |
| Short tweets | 50 |
| Tweets kept | 277 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/33rcvwm6/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @imnotseto's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/35wya1gp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/35wya1gp/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/imnotseto')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/imnotseto/1614213422097/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/imnotseto
|
[
"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
@imnotseto 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 @imnotseto'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 @imnotseto'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/1335360624646295552/kaAOgc0s_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">imo !!! 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@imogenloisfox 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 [@imogenloisfox's tweets](https://twitter.com/imogenloisfox).
<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'>2473</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'>883</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'>219</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1371</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2dm16o1m/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @imogenloisfox's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ectjmyn) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ectjmyn/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/imogenloisfox'</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/imogenloisfox/1608309297782/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/imogenloisfox
|
[
"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">imo !!! AI Bot </div>
<div style="font-size: 15px; color: #657786">@imogenloisfox 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 @imogenloisfox'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'>2473</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'>883</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'>219</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1371</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 @imogenloisfox'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/imogenloisfox'</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 @imogenloisfox'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'>2473</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'>883</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'>219</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1371</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 @imogenloisfox'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/imogenloisfox'</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 @imogenloisfox'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'>2473</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'>883</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'>219</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1371</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 @imogenloisfox'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/imogenloisfox'</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,
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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/1319291252718759938/q2NdOiAb_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">Robert Yi 🐳</div>
<div style="text-align: center; font-size: 14px;">@imrobertyi</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 Robert Yi 🐳.
| Data | Robert Yi 🐳 |
| --- | --- |
| Tweets downloaded | 1353 |
| Retweets | 61 |
| Short tweets | 130 |
| Tweets kept | 1162 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3cmckdcz/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @imrobertyi's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/fi24mvdb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/fi24mvdb/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/imrobertyi')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/imrobertyi/1631652694998/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/imrobertyi
|
[
"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
Robert Yi
@imrobertyi
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 Robert Yi .
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 @imrobertyi'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/1327751099336552449/R5srCw96_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">carson 🤖 AI Bot </div>
<div style="font-size: 15px">@imscribbledude 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 [@imscribbledude's tweets](https://twitter.com/imscribbledude).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2286 |
| Retweets | 458 |
| Short tweets | 252 |
| Tweets kept | 1576 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2eyhb2dr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @imscribbledude's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1t0me7sm) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1t0me7sm/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/imscribbledude')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/imscribbledude/1614102197502/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/imscribbledude
|
[
"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
|
carson AI Bot
@imscribbledude 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 @imscribbledude'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 @imscribbledude'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/706481670090690563/LXli4ovR_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Matthew Incantalupo 🤖 AI Bot </div>
<div style="font-size: 15px">@incantalupo 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 [@incantalupo's tweets](https://twitter.com/incantalupo).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1738 |
| Retweets | 36 |
| Short tweets | 61 |
| Tweets kept | 1641 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/12pm0jbi/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @incantalupo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3vnxuapw) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3vnxuapw/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/incantalupo')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/incantalupo/1616711390839/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/incantalupo
|
[
"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
|
Matthew Incantalupo AI Bot
@incantalupo 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 @incantalupo'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 @incantalupo'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/581592941124153346/5nfUJyU2_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/561419401145376768/7OIwxUCC_400x400.jpeg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1190256978007904257/TsXH7_nP_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">Charmeuse & Sad Socrates & Vincent Van Gone</div>
<div style="text-align: center; font-size: 14px;">@incharmuese-sadsocrates-vvangone</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 Charmeuse & Sad Socrates & Vincent Van Gone.
| Data | Charmeuse | Sad Socrates | Vincent Van Gone |
| --- | --- | --- | --- |
| Tweets downloaded | 3238 | 3197 | 3233 |
| Retweets | 1165 | 40 | 1054 |
| Short tweets | 248 | 161 | 266 |
| Tweets kept | 1825 | 2996 | 1913 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/13ochftk/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @incharmuese-sadsocrates-vvangone's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/173sb7ob) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/173sb7ob/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/incharmuese-sadsocrates-vvangone')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/incharmuese-sadsocrates-vvangone/1635521727120/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/incharmuese-sadsocrates-vvangone
|
[
"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
Charmeuse & Sad Socrates & Vincent Van Gone
@incharmuese-sadsocrates-vvangone
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 Charmeuse & Sad Socrates & Vincent Van Gone.
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 @incharmuese-sadsocrates-vvangone'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/1357846260934352899/EWTPeA8__400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">indi 🍔 🤖 AI Bot </div>
<div style="font-size: 15px">@indiburger 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 [@indiburger's tweets](https://twitter.com/indiburger).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3104 |
| Retweets | 712 |
| Short tweets | 372 |
| Tweets kept | 2020 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3emok4ku/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @indiburger's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/rpeuqv5y) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/rpeuqv5y/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/indiburger')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/indiburger/1614096163881/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/indiburger
|
[
"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
|
indi AI Bot
@indiburger 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 @indiburger'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 @indiburger'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/1298927466074046464/rYBDt889_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Алексей Целищев 🤖 AI Bot </div>
<div style="font-size: 15px">@infernocav 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 [@infernocav's tweets](https://twitter.com/infernocav).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 129 |
| Retweets | 8 |
| Short tweets | 16 |
| Tweets kept | 105 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3fbjwvhg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @infernocav's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/cxwbz9yp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/cxwbz9yp/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/infernocav')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/infernocav/1616656950369/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/infernocav
|
[
"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
|
Алексей Целищев AI Bot
@infernocav 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 @infernocav'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 @infernocav'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/1346117150621257728/FeSBhcrN_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">RIP TO THE VILLIAN 🤖 AI Bot </div>
<div style="font-size: 15px">@infinitedodge 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 [@infinitedodge's tweets](https://twitter.com/infinitedodge).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2774 |
| Retweets | 1524 |
| Short tweets | 123 |
| Tweets kept | 1127 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/qerz9onf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @infinitedodge's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2gw3u22x) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2gw3u22x/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/infinitedodge')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/infinitedodge/1614135156383/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/infinitedodge
|
[
"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
|
RIP TO THE VILLIAN AI Bot
@infinitedodge 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 @infinitedodge'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 @infinitedodge'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/542050723521773568/XfMb_pUx_400x400.jpeg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">infosec_dominatrix 🤖 AI Bot </div>
<div style="font-size: 15px">@infosec_domme 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 [@infosec_domme's tweets](https://twitter.com/infosec_domme).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 542 |
| Retweets | 64 |
| Short tweets | 57 |
| Tweets kept | 421 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1s8mwvc2/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @infosec_domme's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3qb5k1m0) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3qb5k1m0/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/infosec_domme')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/infosec_domme/1616349133246/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/infosec_domme
|
[
"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
|
infosec\_dominatrix AI Bot
@infosec\_domme 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 @infosec\_domme'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 @infosec\_domme'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/1122970155204730882/KMOOjnGR_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ingrida Šimonytė</div>
<div style="text-align: center; font-size: 14px;">@ingridasimonyte</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 Ingrida Šimonytė.
| Data | Ingrida Šimonytė |
| --- | --- |
| Tweets downloaded | 283 |
| Retweets | 17 |
| Short tweets | 10 |
| Tweets kept | 256 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1vod103u/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ingridasimonyte's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2xm136ry) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2xm136ry/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ingridasimonyte')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/ingridasimonyte/1620506733305/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ingridasimonyte
|
[
"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
Ingrida Šimonytė
@ingridasimonyte
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 Ingrida Šimonytė.
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 @ingridasimonyte'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/1346164467130884098/8w4pqpbj_400x400.png')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">borghisattva 🤖 AI Bot </div>
<div style="font-size: 15px">@ingroupist 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 [@ingroupist's tweets](https://twitter.com/ingroupist).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 154 |
| Retweets | 0 |
| Short tweets | 0 |
| Tweets kept | 154 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/fl5icybp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ingroupist's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/218gj8om) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/218gj8om/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ingroupist')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/ingroupist/1616685344882/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ingroupist
|
[
"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
|
borghisattva AI Bot
@ingroupist 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 @ingroupist'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 @ingroupist'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: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">INHALING MY SHEET OF SUN</div>
<div style="text-align: center; font-size: 14px;">@inhalingmy</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 INHALING MY SHEET OF SUN.
| Data | INHALING MY SHEET OF SUN |
| --- | --- |
| Tweets downloaded | 2647 |
| Retweets | 0 |
| Short tweets | 838 |
| Tweets kept | 1809 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/r1ksmwi2/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @inhalingmy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2e1lrid4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2e1lrid4/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/inhalingmy')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/inhalingmy/1631843035059/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/inhalingmy
|
[
"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
INHALING MY SHEET OF SUN
@inhalingmy
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 INHALING MY SHEET OF SUN.
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 @inhalingmy'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/1062301628475289600/sCq-edVm_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Su Başak 🤖 AI Bot </div>
<div style="font-size: 15px">@inmidonot 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 [@inmidonot's tweets](https://twitter.com/inmidonot).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 344 |
| Retweets | 6 |
| Short tweets | 15 |
| Tweets kept | 323 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2mghgkpx/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @inmidonot's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/12l0mm4t) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/12l0mm4t/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/inmidonot')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/inmidonot/1616937673978/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/inmidonot
|
[
"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
|
Su Başak AI Bot
@inmidonot 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 @inmidonot'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 @inmidonot'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/1342654008520011783/ELNBkoe__400x400.png')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Insert 🚩🦮 🤖 AI Bot </div>
<div style="font-size: 15px">@insert_name27 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 [@insert_name27's tweets](https://twitter.com/insert_name27).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 111 |
| Short tweets | 491 |
| Tweets kept | 2644 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3m2d1hmb/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @insert_name27's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ajldnpxe) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ajldnpxe/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/insert_name27')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/insert_name27/1617820538616/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/insert_name27
|
[
"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
|
Insert AI Bot
@insert\_name27 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 @insert\_name27'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 @insert\_name27'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"
] |
[
0.004547144751995802,
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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/1418652395119153153/dvMUbHmM_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/1449364913890074627/SNmSlTYD_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/1450840619132260357/r9rdJtIp_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">Pratham & Insha & Savio Martin ⚡️</div>
<div style="text-align: center; font-size: 14px;">@insharamin-prathkum-saviomartin7</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 Pratham & Insha & Savio Martin ⚡️.
| Data | Pratham | Insha | Savio Martin ⚡️ |
| --- | --- | --- | --- |
| Tweets downloaded | 3246 | 3249 | 3249 |
| Retweets | 461 | 24 | 118 |
| Short tweets | 317 | 457 | 201 |
| Tweets kept | 2468 | 2768 | 2930 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/o7jfvmhp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @insharamin-prathkum-saviomartin7's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/p2md0wva) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/p2md0wva/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/insharamin-prathkum-saviomartin7')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/insharamin-prathkum-saviomartin7/1637920907734/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/insharamin-prathkum-saviomartin7
|
[
"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
Pratham & Insha & Savio Martin ️
@insharamin-prathkum-saviomartin7
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 Pratham & Insha & Savio Martin ️.
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 @insharamin-prathkum-saviomartin7'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/1276993788821540872/edbR86Jw_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Insufficiently Outraged 🤖 AI Bot </div>
<div style="font-size: 15px">@insufficientout 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 [@insufficientout's tweets](https://twitter.com/insufficientout).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 784 |
| Retweets | 26 |
| Short tweets | 68 |
| Tweets kept | 690 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/5cu9fjjj/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @insufficientout's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2c1v17ew) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2c1v17ew/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/insufficientout')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/insufficientout/1616757946042/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/insufficientout
|
[
"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
|
Insufficiently Outraged AI Bot
@insufficientout 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 @insufficientout'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 @insufficientout'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/1374410731358019593/eBVT1vhW_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Spark Of Inquiry 🤖 AI Bot </div>
<div style="font-size: 15px">@interro__bang 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 [@interro__bang's tweets](https://twitter.com/interro__bang).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 114 |
| Retweets | 2 |
| Short tweets | 19 |
| Tweets kept | 93 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1k112d2n/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @interro__bang's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/uppi8vz0) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/uppi8vz0/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/interro__bang')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/interro__bang/1616611219490/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/interro__bang
|
[
"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
|
Spark Of Inquiry AI Bot
@interro\_\_bang 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 @interro\_\_bang'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 @interro\_\_bang'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/608132742224568320/x3yrArdT_400x400.png')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Electronic Intifada 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@intifada 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 [@intifada's tweets](https://twitter.com/intifada).
<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'>3241</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'>6</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'>0</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>3235</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/1qmm4ybr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @intifada's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/8f4jzilg) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/8f4jzilg/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/intifada'</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/intifada/1603110719648/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/intifada
|
[
"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">Electronic Intifada AI Bot </div>
<div style="font-size: 15px; color: #657786">@intifada 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 @intifada'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'>3241</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'>6</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'>0</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>3235</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 @intifada'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/intifada'</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 @intifada'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'>3241</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'>6</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'>0</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>3235</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 @intifada'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/intifada'</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 @intifada'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'>3241</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'>6</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'>0</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>3235</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 @intifada'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/intifada'</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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] |
[
"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('http://pbs.twimg.com/profile_images/922432805426130944/Zv5SABlH_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Carlos E. Perez 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@intuitmachine 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 [@intuitmachine's tweets](https://twitter.com/intuitmachine).
<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'>222</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'>82</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2912</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/3a25w014/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @intuitmachine's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/g4lfqgv1) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/g4lfqgv1/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/intuitmachine'</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/intuitmachine
|
[
"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">Carlos E. Perez AI Bot </div>
<div style="font-size: 15px; color: #657786">@intuitmachine 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 @intuitmachine'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'>222</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'>82</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2912</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 @intuitmachine'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/intuitmachine'</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 @intuitmachine'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'>222</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'>82</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2912</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 @intuitmachine'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/intuitmachine'</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 @intuitmachine'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'>222</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'>82</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2912</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 @intuitmachine'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/intuitmachine'</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,
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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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<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">Steve | Millionaire Habits & Investor's Theory</div>
<div style="text-align: center; font-size: 14px;">@investorstheory-steveonspeed</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 Steve | Millionaire Habits & Investor's Theory.
| Data | Steve | Millionaire Habits | Investor's Theory |
| --- | --- | --- |
| Tweets downloaded | 3245 | 3250 |
| Retweets | 330 | 168 |
| Short tweets | 320 | 660 |
| Tweets kept | 2595 | 2422 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2yk0pwia/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @investorstheory-steveonspeed's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3hmaq3cx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3hmaq3cx/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/investorstheory-steveonspeed')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/investorstheory-steveonspeed/1622080865723/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/investorstheory-steveonspeed
|
[
"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
|
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<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">Steve | Millionaire Habits & Investor's Theory</div>
<div style="text-align: center; font-size: 14px;">@investorstheory-steveonspeed</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 tweets from Steve | Millionaire Habits & Investor's Theory.
| Data | Steve | Millionaire Habits | Investor's Theory |
| --- | --- | --- |
| Tweets downloaded | 3245 | 3250 |
| Retweets | 330 | 168 |
| Short tweets | 320 | 660 |
| Tweets kept | 2595 | 2422 |
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 @investorstheory-steveonspeed'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
|
[
"## 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 tweets from Steve | Millionaire Habits & Investor's Theory.\n\n| Data | Steve | Millionaire Habits | Investor's Theory |\n| --- | --- | --- |\n| Tweets downloaded | 3245 | 3250 |\n| Retweets | 330 | 168 |\n| Short tweets | 320 | 660 |\n| Tweets kept | 2595 | 2422 |\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 @investorstheory-steveonspeed'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.",
"## How to use\n\nYou can use this model directly with a pipeline for text generation:",
"## 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![Follow](URL\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL"
] |
[
"TAGS\n#transformers #pytorch #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 tweets from Steve | Millionaire Habits & Investor's Theory.\n\n| Data | Steve | Millionaire Habits | Investor's Theory |\n| --- | --- | --- |\n| Tweets downloaded | 3245 | 3250 |\n| Retweets | 330 | 168 |\n| Short tweets | 320 | 660 |\n| Tweets kept | 2595 | 2422 |\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 @investorstheory-steveonspeed'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.",
"## How to use\n\nYou can use this model directly with a pipeline for text generation:",
"## 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![Follow](URL\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL"
] |
[
54,
34,
140,
81,
18,
47,
38
] |
[
"passage: TAGS\n#transformers #pytorch #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 tweets from Steve | Millionaire Habits & Investor's Theory.\n\n| Data | Steve | Millionaire Habits | Investor's Theory |\n| --- | --- | --- |\n| Tweets downloaded | 3245 | 3250 |\n| Retweets | 330 | 168 |\n| Short tweets | 320 | 660 |\n| Tweets kept | 2595 | 2422 |\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 @investorstheory-steveonspeed'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.## How to use\n\nYou can use this model directly with a pipeline for text generation:## 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![Follow](URL\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL"
] |
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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/1343581966541545472/Bs7oM0IV_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Ada IO 🤖 AI Bot </div>
<div style="font-size: 15px">@ioorbust 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 [@ioorbust's tweets](https://twitter.com/ioorbust).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 789 |
| Retweets | 79 |
| Short tweets | 102 |
| Tweets kept | 608 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/zuxd4c8i/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ioorbust's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1nt569uh) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1nt569uh/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ioorbust')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/ioorbust/1617757328084/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ioorbust
|
[
"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
|
Ada IO AI Bot
@ioorbust 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 @ioorbust'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 @ioorbust'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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0.11728470027446747,
-0.07783648371696472,
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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/1144996963252940800/VIHkkMCF_400x400.png')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">⌞ʙᴀʟᴀᴢꜱ⌝ 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@iotnerd 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 [@iotnerd's tweets](https://twitter.com/iotnerd).
<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'>3200</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'>915</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'>102</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2183</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2gq45sm3/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @iotnerd's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ksu06s41) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ksu06s41/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/iotnerd'</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/iotnerd/1611677898375/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/iotnerd
|
[
"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">⌞ʙᴀʟᴀᴢꜱ⌝ AI Bot </div>
<div style="font-size: 15px; color: #657786">@iotnerd 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 @iotnerd'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'>3200</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'>915</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'>102</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2183</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 @iotnerd'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/iotnerd'</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 @iotnerd'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'>3200</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'>915</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'>102</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2183</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 @iotnerd'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/iotnerd'</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 @iotnerd'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'>3200</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'>915</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'>102</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2183</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 @iotnerd'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/iotnerd'</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 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/1420146838779400197/98VH7-UW_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">Ivan Poduje</div>
<div style="text-align: center; font-size: 14px;">@ipoduje</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 Ivan Poduje.
| Data | Ivan Poduje |
| --- | --- |
| Tweets downloaded | 3230 |
| Retweets | 1035 |
| Short tweets | 135 |
| Tweets kept | 2060 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/gyttyi09/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ipoduje's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/29wmg1mk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/29wmg1mk/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ipoduje')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/ipoduje/1641572179072/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ipoduje
|
[
"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
Ivan Poduje
@ipoduje
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 Ivan Poduje.
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 @ipoduje'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/1432037158072856578/a_Fty68E_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">Riikka Purra</div>
<div style="text-align: center; font-size: 14px;">@ir_rkp</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 Riikka Purra.
| Data | Riikka Purra |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 141 |
| Short tweets | 78 |
| Tweets kept | 3031 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1w0bzvgu/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ir_rkp's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1nj4v31w) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1nj4v31w/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ir_rkp')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/ir_rkp/1643976228944/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ir_rkp
|
[
"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
Riikka Purra
@ir\_rkp
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 Riikka Purra.
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 @ir\_rkp'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/1360344954485227529/r2dktZMm_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Kevin 🤖 AI Bot </div>
<div style="font-size: 15px">@is_he_batman 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 [@is_he_batman's tweets](https://twitter.com/is_he_batman).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 960 |
| Retweets | 51 |
| Short tweets | 75 |
| Tweets kept | 834 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/25g6159m/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @is_he_batman's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2yerrfcg) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2yerrfcg/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/is_he_batman')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/is_he_batman/1614109879160/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/is_he_batman
|
[
"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
|
Kevin AI Bot
@is\_he\_batman 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 @is\_he\_batman'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 @is\_he\_batman'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/1092831572645036035/yvgPGtOn_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Ishan 🤖 AI Bot </div>
<div style="font-size: 15px">@ishanspatil 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 [@ishanspatil's tweets](https://twitter.com/ishanspatil).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2468 |
| Retweets | 346 |
| Short tweets | 231 |
| Tweets kept | 1891 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/4iupc1l1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ishanspatil's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/k7nyg63n) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/k7nyg63n/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ishanspatil')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/ishanspatil/1617782474953/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ishanspatil
|
[
"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
|
Ishan AI Bot
@ishanspatil 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 @ishanspatil'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 @ishanspatil'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/1448436144388009985/zWh5cSQ3_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">نورهان</div>
<div style="text-align: center; font-size: 14px;">@islamocommunism</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 نورهان.
| Data | نورهان |
| --- | --- |
| Tweets downloaded | 3196 |
| Retweets | 1205 |
| Short tweets | 227 |
| Tweets kept | 1764 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2l8ikj22/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @islamocommunism's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2kngkxcq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2kngkxcq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/islamocommunism')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/islamocommunism/1635014280450/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/islamocommunism
|
[
"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
نورهان
@islamocommunism
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 نورهان.
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 @islamocommunism'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/1381764452098437120/74IgKP07_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/1368077075127603200/Z08slO2P_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">Boston Psychology PhD & keyvan</div>
<div style="text-align: center; font-size: 14px;">@islamphobiacow-praisegodbarbon</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 Boston Psychology PhD & keyvan.
| Data | Boston Psychology PhD | keyvan |
| --- | --- | --- |
| Tweets downloaded | 3224 | 3242 |
| Retweets | 858 | 179 |
| Short tweets | 251 | 223 |
| Tweets kept | 2115 | 2840 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3egvdux4/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @islamphobiacow-praisegodbarbon's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/34hmjrwi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/34hmjrwi/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/islamphobiacow-praisegodbarbon')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](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/islamphobiacow-praisegodbarbon/1627056382131/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/islamphobiacow-praisegodbarbon
|
[
"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
Boston Psychology PhD & keyvan
@islamphobiacow-praisegodbarbon
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 Boston Psychology PhD & keyvan.
Data: Tweets downloaded, Boston Psychology PhD: 3224, keyvan: 3242
Data: Retweets, Boston Psychology PhD: 858, keyvan: 179
Data: Short tweets, Boston Psychology PhD: 251, keyvan: 223
Data: Tweets kept, Boston Psychology PhD: 2115, keyvan: 2840
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 @islamphobiacow-praisegodbarbon'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/1368077075127603200/Z08slO2P_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">beff jezos</div>
<div style="text-align: center; font-size: 14px;">@islamphobiacow</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 beff jezos.
| Data | beff jezos |
| --- | --- |
| Tweets downloaded | 395 |
| Retweets | 36 |
| Short tweets | 37 |
| Tweets kept | 322 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1crtakdb/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @islamphobiacow's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/29lljwti) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/29lljwti/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/islamphobiacow')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/islamphobiacow/1627597861566/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/islamphobiacow
|
[
"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
beff jezos
@islamphobiacow
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 beff jezos.
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 @islamphobiacow'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/1344394699470082049/YzE4UMsj_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Rizza Islam 🤖 AI Bot </div>
<div style="font-size: 15px">@islamrizza 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 [@islamrizza's tweets](https://twitter.com/islamrizza).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3195 |
| Retweets | 73 |
| Short tweets | 394 |
| Tweets kept | 2728 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/t09cn5o0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @islamrizza's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/m6l6wkff) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/m6l6wkff/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/islamrizza')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/islamrizza/1619378181874/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/islamrizza
|
[
"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
|
Rizza Islam AI Bot
@islamrizza 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 @islamrizza'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 @islamrizza'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/1152361188862365697/HWUuVltf_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">nick casino 🤖 AI Bot </div>
<div style="font-size: 15px">@island_iverson 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 [@island_iverson's tweets](https://twitter.com/island_iverson).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3182 |
| Retweets | 367 |
| Short tweets | 193 |
| Tweets kept | 2622 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/dlr58v3e/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @island_iverson's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1vy3qci6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1vy3qci6/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/island_iverson')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/island_iverson/1614113195211/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/island_iverson
|
[
"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
|
nick casino AI Bot
@island\_iverson 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 @island\_iverson'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 @island\_iverson'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/1340996475472494593/yqCQjZ06_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/1341001037142999041/h86Ch8TO_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">Science Bits & International Science Teaching Foundation</div>
<div style="text-align: center; font-size: 14px;">@istfoundation-sciencebits</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 Science Bits & International Science Teaching Foundation.
| Data | Science Bits | International Science Teaching Foundation |
| --- | --- | --- |
| Tweets downloaded | 2741 | 163 |
| Retweets | 759 | 103 |
| Short tweets | 47 | 1 |
| Tweets kept | 1935 | 59 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/c9crff9r/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @istfoundation-sciencebits's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2c68vj42) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2c68vj42/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/istfoundation-sciencebits')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/istfoundation-sciencebits/1634209108264/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/istfoundation-sciencebits
|
[
"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
Science Bits & International Science Teaching Foundation
@istfoundation-sciencebits
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 Science Bits & International Science Teaching Foundation.
Data: Tweets downloaded, Science Bits: 2741, International Science Teaching Foundation: 163
Data: Retweets, Science Bits: 759, International Science Teaching Foundation: 103
Data: Short tweets, Science Bits: 47, International Science Teaching Foundation: 1
Data: Tweets kept, Science Bits: 1935, International Science Teaching Foundation: 59
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 @istfoundation-sciencebits'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/1359348009725808641/KyPjQGzk_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">itemLabel 🤖 AI Bot </div>
<div style="font-size: 15px">@itemlabel 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 [@itemlabel's tweets](https://twitter.com/itemlabel).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3188 |
| Retweets | 1796 |
| Short tweets | 389 |
| Tweets kept | 1003 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/10hookja/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @itemlabel's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1u63m0wj) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1u63m0wj/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/itemlabel')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/itemlabel
|
[
"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
|
itemLabel AI Bot
@itemlabel 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 @itemlabel'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 @itemlabel'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/1378965688459489280/VViTlDIl_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Google ‘Its All Bullshit’ 🤖 AI Bot </div>
<div style="font-size: 15px">@itsall_bullshit 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 [@itsall_bullshit's tweets](https://twitter.com/itsall_bullshit).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3158 |
| Retweets | 1762 |
| Short tweets | 98 |
| Tweets kept | 1298 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/25y8c5ov/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @itsall_bullshit's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/y0ks8zfn) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/y0ks8zfn/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/itsall_bullshit')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/itsall_bullshit/1617823122662/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/itsall_bullshit
|
[
"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
|
Google ‘Its All Bullshit’ AI Bot
@itsall\_bullshit 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 @itsall\_bullshit'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 @itsall\_bullshit'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/1346051916556623875/e66ZNvO2_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Big Ian 🤖 AI Bot </div>
<div style="font-size: 15px">@itsbigian 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 [@itsbigian's tweets](https://twitter.com/itsbigian).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3238 |
| Retweets | 218 |
| Short tweets | 552 |
| Tweets kept | 2468 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2oczo3b8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @itsbigian's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/245obnds) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/245obnds/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/itsbigian')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/itsbigian/1616883483325/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/itsbigian
|
[
"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
|
Big Ian AI Bot
@itsbigian 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 @itsbigian'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 @itsbigian'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/1376928476633137157/d4J78Fmv_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Harveen 🤖 AI Bot </div>
<div style="font-size: 15px">@itsharveen 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 [@itsharveen's tweets](https://twitter.com/itsharveen).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 632 |
| Retweets | 30 |
| Short tweets | 40 |
| Tweets kept | 562 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/a779ia8t/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @itsharveen's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1dip1d5b) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1dip1d5b/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/itsharveen')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/itsharveen/1617627052674/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/itsharveen
|
[
"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
|
Harveen AI Bot
@itsharveen 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 @itsharveen'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 @itsharveen'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/1348712980112936966/i5-XHX3G_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">jane.flowers 🤖 AI Bot </div>
<div style="font-size: 15px">@itsjaneflowers 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 [@itsjaneflowers's tweets](https://twitter.com/itsjaneflowers).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1054 |
| Retweets | 166 |
| Short tweets | 79 |
| Tweets kept | 809 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1af8sp4r/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @itsjaneflowers's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/25kv3ol0) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/25kv3ol0/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/itsjaneflowers')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/itsjaneflowers/1616859152962/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/itsjaneflowers
|
[
"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
|
jane.flowers AI Bot
@itsjaneflowers 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 @itsjaneflowers'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 @itsjaneflowers'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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</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">june party corner</div>
<div style="text-align: center; font-size: 14px;">@itskillerdog</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 june party corner.
| Data | june party corner |
| --- | --- |
| Tweets downloaded | 196 |
| Retweets | 20 |
| Short tweets | 30 |
| Tweets kept | 146 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1u7twx27/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @itskillerdog's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1vg0bbs8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1vg0bbs8/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/itskillerdog')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/itskillerdog/1630971994166/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/itskillerdog
|
[
"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
june party corner
@itskillerdog
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 june party 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 @itskillerdog'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/1337464500446957570/ptHOR4kZ_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Luci Keller 🤖 AI Bot </div>
<div style="font-size: 15px">@itslucikeller 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 [@itslucikeller's tweets](https://twitter.com/itslucikeller).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 69 |
| Short tweets | 352 |
| Tweets kept | 2825 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3nhr24ju/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @itslucikeller's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2zv0hvjq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2zv0hvjq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/itslucikeller')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/itslucikeller/1616622417664/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/itslucikeller
|
[
"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
|
Luci Keller AI Bot
@itslucikeller 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 @itslucikeller'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 @itslucikeller'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/1389992995848658948/XT1CKTIg_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">Aqsa.</div>
<div style="text-align: center; font-size: 14px;">@itsmeaqsaa</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 Aqsa..
| Data | Aqsa. |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 77 |
| Short tweets | 1543 |
| Tweets kept | 1626 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1xy28krg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @itsmeaqsaa's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/18kg27bt) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/18kg27bt/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/itsmeaqsaa')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/itsmeaqsaa/1631734394856/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/itsmeaqsaa
|
[
"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
Aqsa.
@itsmeaqsaa
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 Aqsa..
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 @itsmeaqsaa'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/1365914927580344322/b5PadSd5_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">NFT ChiΞf of Staff 🤖 AI Bot </div>
<div style="font-size: 15px">@itspublu 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 [@itspublu's tweets](https://twitter.com/itspublu).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1768 |
| Retweets | 481 |
| Short tweets | 282 |
| Tweets kept | 1005 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2l8q7e87/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @itspublu's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1vo0wnnt) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1vo0wnnt/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/itspublu')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/itspublu/1616709602963/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/itspublu
|
[
"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
|
NFT ChiΞf of Staff AI Bot
@itspublu 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 @itspublu'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 @itspublu'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/628257137060229120/_3q_D4g2_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">Six words story</div>
<div style="text-align: center; font-size: 14px;">@itssixword</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 Six words story.
| Data | Six words story |
| --- | --- |
| Tweets downloaded | 282 |
| Retweets | 0 |
| Short tweets | 2 |
| Tweets kept | 280 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2dbtmbzz/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @itssixword's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2wydugsv) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2wydugsv/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/itssixword')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/itssixword/1629833127428/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/itssixword
|
[
"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
Six words story
@itssixword
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 Six words story.
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 @itssixword'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/1457774258063437824/VgJyJ_c2_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">uditgoenka.eth</div>
<div style="text-align: center; font-size: 14px;">@iuditg</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 uditgoenka.eth.
| Data | uditgoenka.eth |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 993 |
| Short tweets | 450 |
| Tweets kept | 1807 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1r2lhfr0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @iuditg's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/iswph9y4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/iswph9y4/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/iuditg')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/iuditg/1639532212187/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/iuditg
|
[
"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
URL
@iuditg
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.
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 @iuditg'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; }
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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/1202257345734037504/tRJA6HEx_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">| praveen narayan 〉 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@ivanpeer 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 [@ivanpeer's tweets](https://twitter.com/ivanpeer).
<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'>971</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'>110</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'>102</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>759</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/2thafoo8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ivanpeer's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/3fepz7hm) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/3fepz7hm/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/ivanpeer'</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/ivanpeer/1603607581850/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ivanpeer
|
[
"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">| praveen narayan 〉 AI Bot </div>
<div style="font-size: 15px; color: #657786">@ivanpeer 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 @ivanpeer'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'>971</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'>110</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'>102</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>759</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 @ivanpeer'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/ivanpeer'</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 @ivanpeer'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'>971</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'>110</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'>102</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>759</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 @ivanpeer'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/ivanpeer'</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 @ivanpeer'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'>971</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'>110</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'>102</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>759</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 @ivanpeer'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/ivanpeer'</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,
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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/1404902950607089665/CLa3e4aK_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">##lainpilled</div>
<div style="text-align: center; font-size: 14px;">@ivegottagetagf</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 ##lainpilled.
| Data | ##lainpilled |
| --- | --- |
| Tweets downloaded | 128 |
| Retweets | 7 |
| Short tweets | 16 |
| Tweets kept | 105 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/7kyd6ojb/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ivegottagetagf's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ropyewj) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ropyewj/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ivegottagetagf')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/ivegottagetagf/1623876885491/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/ivegottagetagf
|
[
"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
##lainpilled
@ivegottagetagf
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 ##lainpilled.
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 @ivegottagetagf'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/598663964340301824/im3Wzn-o_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">Robert Evans (The Only Robert Evans)</div>
<div style="text-align: center; font-size: 14px;">@iwriteok</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 Robert Evans (The Only Robert Evans).
| Data | Robert Evans (The Only Robert Evans) |
| --- | --- |
| Tweets downloaded | 3218 |
| Retweets | 1269 |
| Short tweets | 142 |
| Tweets kept | 1807 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3hjcp2ib/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @iwriteok's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/wq4n95ia) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/wq4n95ia/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/iwriteok')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/iwriteok/1668924855688/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/iwriteok
|
[
"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
Robert Evans (The Only Robert Evans)
@iwriteok
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 Robert Evans (The Only Robert Evans).
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 @iwriteok'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/1393393642958635008/P1qx1TlP_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">웃</div>
<div style="text-align: center; font-size: 14px;">@iyxnmt</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 웃.
| Data | 웃 |
| --- | --- |
| Tweets downloaded | 3073 |
| Retweets | 1416 |
| Short tweets | 660 |
| Tweets kept | 997 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1lpd2izx/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @iyxnmt's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3qg153k0) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3qg153k0/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/iyxnmt')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/iyxnmt/1621146502054/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/iyxnmt
|
[
"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
웃
@iyxnmt
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 웃.
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 @iyxnmt'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/1311033052592656385/V-9XECfj_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Jamie Beck 🤖 AI Bot </div>
<div style="font-size: 15px">@j_beck00 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 [@j_beck00's tweets](https://twitter.com/j_beck00).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 75 |
| Retweets | 14 |
| Short tweets | 4 |
| Tweets kept | 57 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/23mq58mv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @j_beck00's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2mbmtl4r) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2mbmtl4r/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/j_beck00')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/j_beck00/1617471704579/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/j_beck00
|
[
"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
|
Jamie Beck AI Bot
@j\_beck00 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 @j\_beck00'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 @j\_beck00'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/1333957151576887297/_1ExBQa3_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Jocelyn (male) of the 365 Followers 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@j_j_j_j_j_jones 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 [@j_j_j_j_j_jones's tweets](https://twitter.com/j_j_j_j_j_jones).
<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'>3225</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'>320</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'>482</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2423</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/uz60miha/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @j_j_j_j_j_jones's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/soi1lw7l) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/soi1lw7l/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/j_j_j_j_j_jones'</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/j_j_j_j_j_jones/1609141746129/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/j_j_j_j_j_jones
|
[
"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">Jocelyn (male) of the 365 Followers AI Bot </div>
<div style="font-size: 15px; color: #657786">@j_j_j_j_j_jones 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 @j_j_j_j_j_jones'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'>3225</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'>320</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'>482</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2423</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 @j_j_j_j_j_jones'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/j_j_j_j_j_jones'</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 @j_j_j_j_j_jones'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'>3225</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'>320</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'>482</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2423</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 @j_j_j_j_j_jones'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/j_j_j_j_j_jones'</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 @j_j_j_j_j_jones'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'>3225</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'>320</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'>482</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2423</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 @j_j_j_j_j_jones'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/j_j_j_j_j_jones'</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,
439,
84,
9,
176,
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 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/1115644092329758721/AFjOr-K8_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
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">jack</div>
<div style="text-align: center; font-size: 14px;">@jack</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from jack.
| Data | jack |
| --- | --- |
| Tweets downloaded | 3231 |
| Retweets | 1147 |
| Short tweets | 817 |
| Tweets kept | 1267 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/dibfzjll/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jack's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3f3e0roo) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3f3e0roo/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jack')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/jack/1653287961086/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jack
|
[
"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
jack
@jack
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from jack.
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 @jack's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
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/1314324082457018369/nPHIUIxe_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Jack Walsh 🤖 AI Bot </div>
<div style="font-size: 15px">@jack_walshh 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 [@jack_walshh's tweets](https://twitter.com/jack_walshh).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1095 |
| Retweets | 234 |
| Short tweets | 121 |
| Tweets kept | 740 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1o93caoq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jack_walshh's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/23dq75x4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/23dq75x4/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jack_walshh')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jack_walshh/1616646386178/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jack_walshh
|
[
"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
|
Jack Walsh AI Bot
@jack\_walshh 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 @jack\_walshh'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 @jack\_walshh'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/1251200537388695557/96JxUIrJ_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/1384243878748856321/vreel6UH_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/1417910390051246080/wKq6pjPR_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">DAN KOE & humble farmer & Jack Butcher</div>
<div style="text-align: center; font-size: 14px;">@jackbutcher-paikcapital-thedankoe</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 DAN KOE & humble farmer & Jack Butcher.
| Data | DAN KOE | humble farmer | Jack Butcher |
| --- | --- | --- | --- |
| Tweets downloaded | 3249 | 3247 | 3220 |
| Retweets | 18 | 601 | 208 |
| Short tweets | 899 | 500 | 1048 |
| Tweets kept | 2332 | 2146 | 1964 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/mvqun4ol/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jackbutcher-paikcapital-thedankoe's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2qd8720q) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2qd8720q/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jackbutcher-paikcapital-thedankoe')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jackbutcher-paikcapital-thedankoe
|
[
"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
DAN KOE & humble farmer & Jack Butcher
@jackbutcher-paikcapital-thedankoe
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 DAN KOE & humble farmer & Jack Butcher.
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 @jackbutcher-paikcapital-thedankoe'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/726446881547517952/ULhSTKxN_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Jack Clark 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@jackclarksf 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://bit.ly/2TGXMZf).
## Training data
The model was trained on [@jackclarksf's tweets](https://twitter.com/jackclarksf).
<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'>603</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'>187</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2426</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/3r89xyps/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jackclarksf's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/3ovybsy5) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/3ovybsy5/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/jackclarksf'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
</section>
[](https://twitter.com/borisdayma)
<section class='prose'>
For more details, visit the project repository.
</section>
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo_share.png?raw=true", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jackclarksf
|
[
"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">Jack Clark AI Bot </div>
<div style="font-size: 15px; color: #657786">@jackclarksf 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 @jackclarksf'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'>603</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'>187</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2426</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 @jackclarksf'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/jackclarksf'</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 @jackclarksf'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'>603</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'>187</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2426</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 @jackclarksf'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/jackclarksf'</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 @jackclarksf'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'>603</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'>187</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2426</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 @jackclarksf'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/jackclarksf'</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
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1316066224938332162/hVIofspH_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">JackGordon 🤖 AI Bot </div>
<div style="font-size: 15px">@jackgordonyt 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 [@jackgordonyt's tweets](https://twitter.com/jackgordonyt).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 660 |
| Retweets | 146 |
| Short tweets | 106 |
| Tweets kept | 408 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3d7wzfbd/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jackgordonyt's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/fa0cjwj6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/fa0cjwj6/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jackgordonyt')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jackgordonyt/1615830241451/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jackgordonyt
|
[
"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
|
JackGordon AI Bot
@jackgordonyt 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 @jackgordonyt'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 @jackgordonyt'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/1365531191470923776/iPBbGURg_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">JackieRacc_VTuber</div>
<div style="text-align: center; font-size: 14px;">@jackieracc_</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 JackieRacc_VTuber.
| Data | JackieRacc_VTuber |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 252 |
| Short tweets | 827 |
| Tweets kept | 2170 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/gx7e8h18/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jackieracc_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1cvwo68s) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1cvwo68s/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jackieracc_')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jackieracc_/1620680912006/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jackieracc_
|
[
"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
JackieRacc\_VTuber
@jackieracc\_
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 JackieRacc\_VTuber.
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 @jackieracc\_'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('http://pbs.twimg.com/profile_images/1026642891374874625/GPdw8p_L_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Jacknjellify 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@jacknjellify 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 [@jacknjellify's tweets](https://twitter.com/jacknjellify).
<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'>3103</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'>1025</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'>336</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1742</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/nmeryp1f/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jacknjellify's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/3q5b8kag) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/3q5b8kag/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/jacknjellify'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
</section>
[](https://twitter.com/borisdayma)
<section class='prose'>
For more details, visit the project repository.
</section>
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo_share.png?raw=true", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jacknjellify
|
[
"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">Jacknjellify AI Bot </div>
<div style="font-size: 15px; color: #657786">@jacknjellify 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 @jacknjellify'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'>3103</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'>1025</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'>336</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1742</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 @jacknjellify'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/jacknjellify'</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 @jacknjellify'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'>3103</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'>1025</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'>336</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1742</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 @jacknjellify'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/jacknjellify'</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 @jacknjellify'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'>3103</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'>1025</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'>336</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1742</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 @jacknjellify'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/jacknjellify'</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 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/1418813091140227072/iXDCqBz0_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">Jack Posobiec 🇺🇸</div>
<div style="text-align: center; font-size: 14px;">@jackposobiec</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 Jack Posobiec 🇺🇸.
| Data | Jack Posobiec 🇺🇸 |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 818 |
| Short tweets | 511 |
| Tweets kept | 1917 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3s4mnium/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jackposobiec's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2vllrmfa) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2vllrmfa/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jackposobiec')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jackposobiec/1630169093455/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jackposobiec
|
[
"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
Jack Posobiec 🇺🇸
@jackposobiec
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 Jack Posobiec 🇺🇸.
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 @jackposobiec'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/1523106752668966913/tWNV2zbS_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">jacksfilms🌹</div>
<div style="text-align: center; font-size: 14px;">@jacksfilms</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 jacksfilms🌹.
| Data | jacksfilms🌹 |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 97 |
| Short tweets | 444 |
| Tweets kept | 2708 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1hsenlsv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jacksfilms's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ow20675) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ow20675/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jacksfilms')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jacksfilms/1653095886748/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jacksfilms
|
[
"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
jacksfilms
@jacksfilms
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 jacksfilms.
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 @jacksfilms'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/1284100202421342209/MVXATULR_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Day6 Jae 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@jae_day6 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 [@jae_day6's tweets](https://twitter.com/jae_day6).
<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'>3229</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'>123</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'>1021</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2085</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/3lpvhxwq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jae_day6's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/3vyjrutx) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/3vyjrutx/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/jae_day6'</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/jae_day6/1601274497991/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jae_day6
|
[
"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">Day6 Jae AI Bot </div>
<div style="font-size: 15px; color: #657786">@jae_day6 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 @jae_day6'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'>3229</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'>123</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'>1021</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2085</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 @jae_day6'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/jae_day6'</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 @jae_day6'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'>3229</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'>123</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'>1021</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2085</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 @jae_day6'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/jae_day6'</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 @jae_day6'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'>3229</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'>123</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'>1021</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2085</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 @jae_day6'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/jae_day6'</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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48,
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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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<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">Programo, luego existo</div>
<div style="text-align: center; font-size: 14px;">@jagedn</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 Programo, luego existo.
| Data | Programo, luego existo |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 549 |
| Short tweets | 220 |
| Tweets kept | 2475 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ptz28obp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jagedn's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1i8g6srp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1i8g6srp/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jagedn')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jagedn/1625062317603/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jagedn
|
[
"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
Programo, luego existo
@jagedn
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 Programo, luego existo.
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 @jagedn'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/1374457518995349507/LPSYSW4N_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">certified cael moment™ 🔜 BLFC 🤖 AI Bot </div>
<div style="font-size: 15px">@jaguarunlocked 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 [@jaguarunlocked's tweets](https://twitter.com/jaguarunlocked).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3176 |
| Retweets | 1521 |
| Short tweets | 203 |
| Tweets kept | 1452 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2j5t38f8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jaguarunlocked's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3n6tm7lj) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3n6tm7lj/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jaguarunlocked')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jaguarunlocked/1617770655879/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jaguarunlocked
|
[
"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
|
certified cael moment™ BLFC AI Bot
@jaguarunlocked 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 @jaguarunlocked'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 @jaguarunlocked'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/1374180612609880068/QkkHvC6R_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">jacob 🤖 AI Bot </div>
<div style="font-size: 15px">@jakeaccino 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 [@jakeaccino's tweets](https://twitter.com/jakeaccino).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 179 |
| Retweets | 8 |
| Short tweets | 53 |
| Tweets kept | 118 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/239ufxkc/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jakeaccino's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3myo5k1y) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3myo5k1y/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jakeaccino')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jakeaccino
|
[
"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
|
jacob AI Bot
@jakeaccino 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 @jakeaccino'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 @jakeaccino'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('http://pbs.twimg.com/profile_images/1253134985948614657/xN4lDF3W_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">James Cham ✍🏻 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@jamescham 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://bit.ly/2TGXMZf).
## Training data
The model was trained on [@jamescham's tweets](https://twitter.com/jamescham).
<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'>744</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'>317</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2152</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/20ku8js2/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jamescham's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/32to3ioi) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/32to3ioi/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/jamescham'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
</section>
[](https://twitter.com/borisdayma)
<section class='prose'>
For more details, visit the project repository.
</section>
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo_share.png?raw=true", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jamescham
|
[
"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">James Cham AI Bot </div>
<div style="font-size: 15px; color: #657786">@jamescham 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 @jamescham'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'>744</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'>317</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2152</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 @jamescham'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/jamescham'</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 @jamescham'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'>744</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'>317</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2152</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 @jamescham'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/jamescham'</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 @jamescham'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'>744</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'>317</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2152</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 @jamescham'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/jamescham'</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,
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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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<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">CANCELLED & James Charles & Logan Paul</div>
<div style="text-align: center; font-size: 14px;">@jamescharles-loganpaul-tanamongeau</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 CANCELLED & James Charles & Logan Paul.
| Data | CANCELLED | James Charles | Logan Paul |
| --- | --- | --- | --- |
| Tweets downloaded | 3167 | 3182 | 3246 |
| Retweets | 938 | 480 | 98 |
| Short tweets | 522 | 496 | 287 |
| Tweets kept | 1707 | 2206 | 2861 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2avr905u/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jamescharles-loganpaul-tanamongeau's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2at101p1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2at101p1/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jamescharles-loganpaul-tanamongeau')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jamescharles-loganpaul-tanamongeau/1631598787303/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jamescharles-loganpaul-tanamongeau
|
[
"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
CANCELLED & James Charles & Logan Paul
@jamescharles-loganpaul-tanamongeau
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 CANCELLED & James Charles & Logan Paul.
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 @jamescharles-loganpaul-tanamongeau'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/958932211973152769/FUpkmn4u_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">James Clear 🤖 AI Bot </div>
<div style="font-size: 15px">@jamesclear 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 [@jamesclear's tweets](https://twitter.com/jamesclear).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 190 |
| Short tweets | 385 |
| Tweets kept | 2672 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2hvyoab9/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jamesclear's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/v67076s3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/v67076s3/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jamesclear')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jamesclear/1616666243525/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jamesclear
|
[
"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
|
James Clear AI Bot
@jamesclear 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 @jamesclear'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 @jamesclear'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/1309510427240534022/Us-RCD-5_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">James Hutton 🤖 AI Bot </div>
<div style="font-size: 15px">@jameshuttonphil 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 [@jameshuttonphil's tweets](https://twitter.com/jameshuttonphil).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 648 |
| Retweets | 25 |
| Short tweets | 89 |
| Tweets kept | 534 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/bamdk9dm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jameshuttonphil's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2jp3j37a) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2jp3j37a/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jameshuttonphil')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jameshuttonphil/1617296338533/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jameshuttonphil
|
[
"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
|
James Hutton AI Bot
@jameshuttonphil 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 @jameshuttonphil'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 @jameshuttonphil'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/1040878369594896384/eusyG8Np_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">James Sherlock 🤖 AI Bot </div>
<div style="font-size: 15px">@jamespsherlock 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 [@jamespsherlock's tweets](https://twitter.com/jamespsherlock).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 743 |
| Retweets | 260 |
| Short tweets | 44 |
| Tweets kept | 439 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ulatc4k/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jamespsherlock's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1btltx5f) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1btltx5f/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jamespsherlock')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jamespsherlock/1616781166201/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jamespsherlock
|
[
"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
|
James Sherlock AI Bot
@jamespsherlock 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 @jamespsherlock'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 @jamespsherlock'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/1375502415240122373/JO1DArJT_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">Jamila Husain</div>
<div style="text-align: center; font-size: 14px;">@jamz5251</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 Jamila Husain.
| Data | Jamila Husain |
| --- | --- |
| Tweets downloaded | 3234 |
| Retweets | 900 |
| Short tweets | 65 |
| Tweets kept | 2269 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/r9z40rld/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jamz5251's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/20gadkdv) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/20gadkdv/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jamz5251')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jamz5251/1622370618440/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jamz5251
|
[
"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
Jamila Husain
@jamz5251
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 Jamila Husain.
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 @jamz5251'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/1536389142287892481/N6kCwACw_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">Columbine Janie</div>
<div style="text-align: center; font-size: 14px;">@janieclone</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 Columbine Janie.
| Data | Columbine Janie |
| --- | --- |
| Tweets downloaded | 3072 |
| Retweets | 1211 |
| Short tweets | 462 |
| Tweets kept | 1399 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1divgffx/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @janieclone's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ic6ynmd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ic6ynmd/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/janieclone')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/janieclone
|
[
"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
Columbine Janie
@janieclone
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 Columbine Janie.
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 @janieclone'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/1455690959132532738/Z4UvDtLA_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">Poolgirl Janie Diamond</div>
<div style="text-align: center; font-size: 14px;">@janiedied</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 Poolgirl Janie Diamond.
| Data | Poolgirl Janie Diamond |
| --- | --- |
| Tweets downloaded | 1505 |
| Retweets | 552 |
| Short tweets | 283 |
| Tweets kept | 670 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3232onrl/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @janiedied's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/smx9pf1l) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/smx9pf1l/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/janiedied')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/janiedied/1645111847557/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/janiedied
|
[
"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
Poolgirl Janie Diamond
@janiedied
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 Poolgirl Janie Diamond.
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 @janiedied'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/2158604209/feuilles_400x400.png')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Au Jardin 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@jardininfo 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 [@jardininfo's tweets](https://twitter.com/jardininfo).
<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'>3200</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'>0</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'>375</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2825</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/48yjj01v/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jardininfo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3t0scjqn) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3t0scjqn/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/jardininfo'</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/jardininfo/1610568803876/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jardininfo
|
[
"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">Au Jardin AI Bot </div>
<div style="font-size: 15px; color: #657786">@jardininfo 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 @jardininfo'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'>3200</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'>0</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'>375</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2825</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 @jardininfo'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/jardininfo'</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 @jardininfo'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'>3200</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'>0</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'>375</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2825</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 @jardininfo'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/jardininfo'</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 @jardininfo'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'>3200</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'>0</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'>375</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2825</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 @jardininfo'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/jardininfo'</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
] |
[
"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/1360504508678246401/WpE9tJiC_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Jason Chen 🤖 AI Bot </div>
<div style="font-size: 15px">@jasonchen0325 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 [@jasonchen0325's tweets](https://twitter.com/jasonchen0325).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 354 |
| Retweets | 30 |
| Short tweets | 9 |
| Tweets kept | 315 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1wsqq8bl/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jasonchen0325's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/w2gqjxjr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/w2gqjxjr/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jasonchen0325')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jasonchen0325/1616715271094/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jasonchen0325
|
[
"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
|
Jason Chen AI Bot
@jasonchen0325 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 @jasonchen0325'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 @jasonchen0325'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/757580607882948608/-KXkY5qL_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">J.A. Sutherland SciFi Books 🤖 AI Bot </div>
<div style="font-size: 15px">@jasutherlandbks 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 [@jasutherlandbks's tweets](https://twitter.com/jasutherlandbks).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3192 |
| Retweets | 952 |
| Short tweets | 169 |
| Tweets kept | 2071 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/210hhn5z/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jasutherlandbks's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/23qtgnsl) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/23qtgnsl/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jasutherlandbks')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jasutherlandbks/1616634473974/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jasutherlandbks
|
[
"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
|
J.A. Sutherland SciFi Books AI Bot
@jasutherlandbks 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 @jasutherlandbks'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 @jasutherlandbks'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/1370572501269553152/Tl_3viV2_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">🧠Jattazo Shin🧠 !!COMMISSIONS OPEN!!</div>
<div style="text-align: center; font-size: 14px;">@jattazo</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 🧠Jattazo Shin🧠 !!COMMISSIONS OPEN!!.
| Data | 🧠Jattazo Shin🧠 !!COMMISSIONS OPEN!! |
| --- | --- |
| Tweets downloaded | 3243 |
| Retweets | 196 |
| Short tweets | 757 |
| Tweets kept | 2290 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/oc8tbgql/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jattazo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/7n3lt4bb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/7n3lt4bb/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jattazo')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jattazo/1620679164511/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jattazo
|
[
"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
Jattazo Shin !!COMMISSIONS OPEN!!
@jattazo
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 Jattazo Shin !!COMMISSIONS OPEN!!.
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 @jattazo'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/1356081411762040834/9L5GPrEi_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">🧠Jattazo Shin🧠 🤖 AI Bot </div>
<div style="font-size: 15px">@jattazoshin 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 [@jattazoshin's tweets](https://twitter.com/jattazoshin).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2768 |
| Retweets | 179 |
| Short tweets | 414 |
| Tweets kept | 2175 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2gto8yaa/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jattazoshin's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1gdg6xx3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1gdg6xx3/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jattazoshin')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jattazoshin/1613105660546/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jattazoshin
|
[
"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
|
Jattazo Shin AI Bot
@jattazoshin 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 @jattazoshin'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 @jattazoshin'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/1377879160173993987/20XH6CdP_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Cool Narcissist 🤖 AI Bot </div>
<div style="font-size: 15px">@java_jigga 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 [@java_jigga's tweets](https://twitter.com/java_jigga).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 313 |
| Short tweets | 426 |
| Tweets kept | 2507 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/kvpyc8u1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @java_jigga's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/6p3ishch) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/6p3ishch/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/java_jigga')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/java_jigga/1617788084385/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/java_jigga
|
[
"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
|
Cool Narcissist AI Bot
@java\_jigga 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 @java\_jigga'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 @java\_jigga'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/1329236115325390850/L6QYc5Qd_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Javi Ballester 🤖 AI Bot </div>
<div style="font-size: 15px">@javiballester4 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 [@javiballester4's tweets](https://twitter.com/javiballester4).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 369 |
| Retweets | 4 |
| Short tweets | 108 |
| Tweets kept | 257 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/33xklndf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @javiballester4's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/s6kbzp61) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/s6kbzp61/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/javiballester4')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/javiballester4/1616630748333/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/javiballester4
|
[
"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
|
Javi Ballester AI Bot
@javiballester4 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 @javiballester4'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 @javiballester4'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/1355323445622558725/vl1-gUcf_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Javierhalamadrid 🤖 AI Bot </div>
<div style="font-size: 15px">@javierito321 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 [@javierito321's tweets](https://twitter.com/javierito321).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3243 |
| Retweets | 144 |
| Short tweets | 90 |
| Tweets kept | 3009 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/36rnr3vs/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @javierito321's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/bj56pvfw) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/bj56pvfw/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/javierito321')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/javierito321/1617016242704/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/javierito321
|
[
"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
|
Javierhalamadrid AI Bot
@javierito321 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 @javierito321'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 @javierito321'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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</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/922569683420794880/bk2ERDe2_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Gabor Javorszky 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@javorszky 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 [@javorszky's tweets](https://twitter.com/javorszky).
<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'>3137</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'>2139</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'>67</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>931</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/1cyr2cuz/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @javorszky's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/2sa503ur) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/2sa503ur/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/javorszky'</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/javorszky/1602234108282/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/javorszky
|
[
"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">Gabor Javorszky AI Bot </div>
<div style="font-size: 15px; color: #657786">@javorszky 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 @javorszky'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'>3137</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'>2139</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'>67</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>931</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 @javorszky'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/javorszky'</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 @javorszky'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'>3137</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'>2139</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'>67</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>931</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 @javorszky'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/javorszky'</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 @javorszky'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'>3137</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'>2139</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'>67</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>931</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 @javorszky'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/javorszky'</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
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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/1325460517922729984/xDO9dBt-_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">Jay Alammar</div>
<div style="text-align: center; font-size: 14px;">@jayalammar</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 Jay Alammar.
| Data | Jay Alammar |
| --- | --- |
| Tweets downloaded | 692 |
| Retweets | 198 |
| Short tweets | 35 |
| Tweets kept | 459 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1wf3zug3/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jayalammar's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/hq8g8xlh) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/hq8g8xlh/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jayalammar')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jayalammar/1638460288971/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jayalammar
|
[
"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
Jay Alammar
@jayalammar
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 Jay Alammar.
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 @jayalammar'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/1345079087337938944/tUHfuOi2_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Jasmine Persephone ☭ Black Podcast Revolution 🤖 AI Bot </div>
<div style="font-size: 15px">@jazzpomegranate 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 [@jazzpomegranate's tweets](https://twitter.com/jazzpomegranate).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3208 |
| Retweets | 184 |
| Short tweets | 720 |
| Tweets kept | 2304 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/312m9owm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jazzpomegranate's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/jvni6p8a) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/jvni6p8a/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jazzpomegranate')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jazzpomegranate/1614106581220/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jazzpomegranate
|
[
"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
|
Jasmine Persephone Black Podcast Revolution AI Bot
@jazzpomegranate 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 @jazzpomegranate'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 @jazzpomegranate'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/63826202/jon_buffalo_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Jon Beasley-Murray 🤖 AI Bot </div>
<div style="font-size: 15px">@jbmurray 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 [@jbmurray's tweets](https://twitter.com/jbmurray).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2861 |
| Retweets | 364 |
| Short tweets | 260 |
| Tweets kept | 2237 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1yppksvx/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jbmurray's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/dqw1zvsq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/dqw1zvsq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jbmurray')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jbmurray/1617246417542/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jbmurray
|
[
"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
|
Jon Beasley-Murray AI Bot
@jbmurray 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 @jbmurray'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 @jbmurray'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/948678870990954496/5moZ7K0__400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Jordan Peterson Quotes 🤖 AI Bot </div>
<div style="font-size: 15px">@jbpetersonquote 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 [@jbpetersonquote's tweets](https://twitter.com/jbpetersonquote).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1983 |
| Retweets | 605 |
| Short tweets | 47 |
| Tweets kept | 1331 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1n1ihdfe/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jbpetersonquote's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1qijh16v) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1qijh16v/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jbpetersonquote')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-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/jbpetersonquote/1620104584619/predictions.png", "widget": [{"text": "My dream is"}]}
|
text-generation
|
huggingtweets/jbpetersonquote
|
[
"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
|
Jordan Peterson Quotes AI Bot
@jbpetersonquote 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 @jbpetersonquote'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 @jbpetersonquote'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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