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text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/975180581440053249/yaM9x-Lq_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">William Blake</div>
<div style="text-align: center; font-size: 14px;">@williamblakebot</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 William Blake.
| Data | William Blake |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 0 |
| Short tweets | 0 |
| Tweets kept | 3250 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2lyz5wo1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @williamblakebot's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3hz2kxqg) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3hz2kxqg/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/williamblakebot')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/williamblakebot/1629577022887/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/williamblakebot
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
William Blake
@williamblakebot
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 William Blake.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @williamblakebot's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1374605323881779203/c5kjnLQp_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">🔆¯\_(ツ)_/¯❓ 🤖 AI Bot </div>
<div style="font-size: 15px">@williamgrobman 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 [@williamgrobman's tweets](https://twitter.com/williamgrobman).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3248 |
| Retweets | 51 |
| Short tweets | 223 |
| Tweets kept | 2974 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2wiaiqgc/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @williamgrobman's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/phcsbki0) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/phcsbki0/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/williamgrobman')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/williamgrobman/1616735910839/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/williamgrobman
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
¯\\_(ツ)\_/¯ AI Bot
@williamgrobman 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 @williamgrobman'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 @williamgrobman's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1485887832346357760/JhrQ8-73_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">Quinn Wilton</div>
<div style="text-align: center; font-size: 14px;">@wilton_quinn</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 Quinn Wilton.
| Data | Quinn Wilton |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 72 |
| Short tweets | 135 |
| Tweets kept | 3038 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1789r49b/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wilton_quinn's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/31qiamqk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/31qiamqk/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/wilton_quinn')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/wilton_quinn
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Quinn Wilton
@wilton\_quinn
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 Quinn Wilton.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @wilton\_quinn's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1228050699348561920/YvWAQD2L_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">WIRED 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@wired 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 [@wired's tweets](https://twitter.com/wired).
<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'>3240</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'>353</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'>21</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2866</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/35181tay/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wired's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/13lg2287) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/13lg2287/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/wired'</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/wired/1601263431883/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/wired
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">WIRED AI Bot </div>
<div style="font-size: 15px; color: #657786">@wired 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 @wired'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'>3240</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'>353</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'>21</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2866</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 @wired's tweets.
Hyperparameters and metrics are 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/wired'</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 @wired'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'>3240</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'>353</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'>21</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2866</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 @wired'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/wired'</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 @wired'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'>3240</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'>353</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'>21</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2866</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 @wired'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/wired'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1184495597967106049/BPXCzXkd_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Chaotic Maelstrom of Willing Hands 🤖 AI Bot </div>
<div style="font-size: 15px">@witchdagguh 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 [@witchdagguh's tweets](https://twitter.com/witchdagguh).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3046 |
| Retweets | 1626 |
| Short tweets | 135 |
| Tweets kept | 1285 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1tqwxgqa/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @witchdagguh's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ng9fmxq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ng9fmxq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/witchdagguh')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/witchdagguh/1614138864003/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/witchdagguh
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Chaotic Maelstrom of Willing Hands AI Bot
@witchdagguh 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 @witchdagguh'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 @witchdagguh's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1458448689568092161/qliCU5-F_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">VacuumF</div>
<div style="text-align: center; font-size: 14px;">@witheredstrings</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 VacuumF.
| Data | VacuumF |
| --- | --- |
| Tweets downloaded | 311 |
| Retweets | 10 |
| Short tweets | 15 |
| Tweets kept | 286 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3jei3sh4/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @witheredstrings's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1wwm26hx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1wwm26hx/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/witheredstrings')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/witheredstrings/1636894685017/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/witheredstrings
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
VacuumF
@witheredstrings
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 VacuumF.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @witheredstrings's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('http://pbs.twimg.com/profile_images/1264681056256565268/lrwZRqIv_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Edward Witten 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@witten271 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 [@witten271's tweets](https://twitter.com/witten271).
<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'>1337</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'>761</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'>32</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>544</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/i5o4s13a/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @witten271's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/35w4smrg) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/35w4smrg/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/witten271'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
</section>
[](https://twitter.com/intent/follow?screen_name=borisdayma)
<section class='prose'>
For more details, visit the project repository.
</section>
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/witten271
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Edward Witten AI Bot </div>
<div style="font-size: 15px; color: #657786">@witten271 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 @witten271'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'>1337</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'>761</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'>32</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>544</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 @witten271's tweets.
Hyperparameters and metrics are 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/witten271'</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 @witten271'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'>1337</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'>761</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'>32</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>544</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 @witten271'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/witten271'</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 @witten271'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'>1337</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'>761</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'>32</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>544</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 @witten271'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/witten271'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1453179488569802752/LsB82o0-_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">wihrel</div>
<div style="text-align: center; font-size: 14px;">@wmascen</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 wihrel.
| Data | wihrel |
| --- | --- |
| Tweets downloaded | 2900 |
| Retweets | 203 |
| Short tweets | 236 |
| Tweets kept | 2461 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/bsbw98xm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wmascen's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3pwlitks) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3pwlitks/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/wmascen')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/wmascen/1642567908765/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/wmascen
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
wihrel
@wmascen
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 wihrel.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @wmascen's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1509990164415893517/qIuzsMq6_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">Adrian Wojnarowski</div>
<div style="text-align: center; font-size: 14px;">@wojespn</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Adrian Wojnarowski.
| Data | Adrian Wojnarowski |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 699 |
| Short tweets | 46 |
| Tweets kept | 2505 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3kc1af3t/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wojespn's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3d9r0f0h) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3d9r0f0h/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/wojespn')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/wojespn/1651603295184/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/wojespn
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Adrian Wojnarowski
@wojespn
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
The model was trained on tweets from Adrian Wojnarowski.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @wojespn's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1334420408490057729/BoIR414f_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">Wokal Distance</div>
<div style="text-align: center; font-size: 14px;">@wokal_distance</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 Wokal Distance.
| Data | Wokal Distance |
| --- | --- |
| Tweets downloaded | 3242 |
| Retweets | 1382 |
| Short tweets | 145 |
| Tweets kept | 1715 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1udsr72i/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wokal_distance's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1pi9x5ai) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1pi9x5ai/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/wokal_distance')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/wokal_distance
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Wokal Distance
@wokal\_distance
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 Wokal Distance.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @wokal\_distance's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1363814877119336451/DtC1OuMG_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">woketopus is grooving 🤖 AI Bot </div>
<div style="font-size: 15px">@woketopus 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 [@woketopus's tweets](https://twitter.com/woketopus).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3219 |
| Retweets | 217 |
| Short tweets | 646 |
| Tweets kept | 2356 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3t0s1gfu/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @woketopus's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1s4xkpp2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1s4xkpp2/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/woketopus')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/woketopus/1614149251542/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/woketopus
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
woketopus is grooving AI Bot
@woketopus 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 @woketopus'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 @woketopus's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1253094103413395456/1OXl60FT_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Josh Wolfe 🤖 AI Bot </div>
<div style="font-size: 15px">@wolfejosh 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 [@wolfejosh's tweets](https://twitter.com/wolfejosh).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 838 |
| Short tweets | 374 |
| Tweets kept | 2033 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/121x3hw4/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wolfejosh's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2poyfrja) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2poyfrja/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/wolfejosh')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/wolfejosh/1616623620980/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/wolfejosh
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Josh Wolfe AI Bot
@wolfejosh 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 @wolfejosh'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 @wolfejosh's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1357846937840586753/vte8QVom_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Evelyn fra den øya✴⬡✴🦎✴🇬🇧🇳🇴 🤖 AI Bot </div>
<div style="font-size: 15px">@wolfniya 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 [@wolfniya's tweets](https://twitter.com/wolfniya).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3243 |
| Retweets | 198 |
| Short tweets | 331 |
| Tweets kept | 2714 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2e1doaxh/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wolfniya's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2iyc29lq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2iyc29lq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/wolfniya')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/wolfniya/1617790877098/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/wolfniya
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Evelyn fra den øya⬡🇬🇧🇳🇴 AI Bot
@wolfniya 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 @wolfniya'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 @wolfniya's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1213358797038739456/P4RT8ilj_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Wonkhe 🤖 AI Bot </div>
<div style="font-size: 15px">@wonkhe 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 [@wonkhe's tweets](https://twitter.com/wonkhe).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 82 |
| Short tweets | 0 |
| Tweets kept | 3168 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3thzvsno/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wonkhe's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/23efmlg0) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/23efmlg0/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/wonkhe')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/wonkhe/1617269501722/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/wonkhe
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Wonkhe AI Bot
@wonkhe 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 @wonkhe'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 @wonkhe's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1383116056726736906/9efycXzG_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">rhys cake</div>
<div style="text-align: center; font-size: 14px;">@wormonnastring</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 rhys cake.
| Data | rhys cake |
| --- | --- |
| Tweets downloaded | 3139 |
| Retweets | 841 |
| Short tweets | 260 |
| Tweets kept | 2038 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1f327axi/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wormonnastring's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3gmcf7lk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3gmcf7lk/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/wormonnastring')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/wormonnastring/1628103109378/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/wormonnastring
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
rhys cake
@wormonnastring
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 rhys cake.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @wormonnastring's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1370683624324956162/R6cB17BK_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">21st Century Schizoid Ben 🤖 AI Bot </div>
<div style="font-size: 15px">@worrski_ 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 [@worrski_'s tweets](https://twitter.com/worrski_).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3190 |
| Retweets | 531 |
| Short tweets | 856 |
| Tweets kept | 1803 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1g9vvlkk/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @worrski_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/17oq1dis) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/17oq1dis/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/worrski_')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/worrski_/1616131395706/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/worrski_
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
21st Century Schizoid Ben AI Bot
@worrski\_ 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 @worrski\_'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 @worrski\_'s tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1292884837468999686/9yJgjLUo_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">turnip 🤖 AI Bot </div>
<div style="font-size: 15px">@wortelsoup 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 [@wortelsoup's tweets](https://twitter.com/wortelsoup).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3203 |
| Retweets | 280 |
| Short tweets | 492 |
| Tweets kept | 2431 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3lswi6sv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wortelsoup's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/15hy9x0d) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/15hy9x0d/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/wortelsoup')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/wortelsoup/1617787975493/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/wortelsoup
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
turnip AI Bot
@wortelsoup 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 @wortelsoup'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 @wortelsoup's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1474540394956394498/6o13ikqu_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">Woxxy</div>
<div style="text-align: center; font-size: 14px;">@woxxy</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 Woxxy.
| Data | Woxxy |
| --- | --- |
| Tweets downloaded | 3239 |
| Retweets | 308 |
| Short tweets | 374 |
| Tweets kept | 2557 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ekkjj88/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @woxxy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3aueqdru) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3aueqdru/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/woxxy')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/woxxy/1653081762754/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/woxxy
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Woxxy
@woxxy
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 Woxxy.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @woxxy's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/939424471353475072/fB-3BRin_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Wrath Of Gnon 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@wrathofgnon 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 [@wrathofgnon's tweets](https://twitter.com/wrathofgnon).
<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'>3227</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'>499</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'>116</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2612</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/x0tn1ht9/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wrathofgnon's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/39eutbnd) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/39eutbnd/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/wrathofgnon'</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/wrathofgnon/1603923813760/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/wrathofgnon
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Wrath Of Gnon AI Bot </div>
<div style="font-size: 15px; color: #657786">@wrathofgnon 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 @wrathofgnon'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'>3227</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'>499</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'>116</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2612</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 @wrathofgnon's tweets.
Hyperparameters and metrics are 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/wrathofgnon'</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 @wrathofgnon'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'>3227</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'>499</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'>116</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2612</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 @wrathofgnon'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/wrathofgnon'</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 @wrathofgnon'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'>3227</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'>499</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'>116</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2612</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 @wrathofgnon'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/wrathofgnon'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1565906625994854400/IjFhPHZI_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">wretched worm</div>
<div style="text-align: center; font-size: 14px;">@wretched_worm</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 wretched worm.
| Data | wretched worm |
| --- | --- |
| Tweets downloaded | 3185 |
| Retweets | 425 |
| Short tweets | 489 |
| Tweets kept | 2271 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/glfr41fg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wretched_worm's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ejn1zlbd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ejn1zlbd/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/wretched_worm')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/wretched_worm
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
wretched worm
@wretched\_worm
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 wretched worm.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @wretched\_worm's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1213979995342684160/gsKEissy_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">write lefty 🤖 AI Bot </div>
<div style="font-size: 15px">@writinglefty 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 [@writinglefty's tweets](https://twitter.com/writinglefty).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 98 |
| Short tweets | 572 |
| Tweets kept | 2577 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/dllzz3mr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @writinglefty's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1pxddijl) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1pxddijl/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/writinglefty')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/writinglefty/1616644119366/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/writinglefty
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
write lefty AI Bot
@writinglefty 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 @writinglefty'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 @writinglefty's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/971415515754266624/zCX0q9d5_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">The Wall Street Journal 🤖 AI Bot </div>
<div style="font-size: 15px">@wsj 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 [@wsj's tweets](https://twitter.com/wsj).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 26 |
| Short tweets | 0 |
| Tweets kept | 3224 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1zzpode1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wsj's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2gwhambd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2gwhambd/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/wsj')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/wsj/1617668423306/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/wsj
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
The Wall Street Journal AI Bot
@wsj 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 @wsj'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 @wsj's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/68000547/1863715-big_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">William Shakespeare 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@wwm_shakespeare 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 [@wwm_shakespeare's tweets](https://twitter.com/wwm_shakespeare).
<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'>3234</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'>18</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'>196</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>3020</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/27cac1ob/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wwm_shakespeare's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1qqhve6t) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1qqhve6t/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/wwm_shakespeare'</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/wwm_shakespeare/1610567717562/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/wwm_shakespeare
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">William Shakespeare AI Bot </div>
<div style="font-size: 15px; color: #657786">@wwm_shakespeare 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 @wwm_shakespeare'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'>3234</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'>18</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'>196</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>3020</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 @wwm_shakespeare's tweets.
Hyperparameters and metrics are 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/wwm_shakespeare'</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 @wwm_shakespeare'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'>3234</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'>18</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'>196</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>3020</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 @wwm_shakespeare'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/wwm_shakespeare'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on @wwm_shakespeare'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'>3234</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'>18</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'>196</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>3020</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 @wwm_shakespeare'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/wwm_shakespeare'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/804696255150587904/sDGQpDwM_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">warrior cop</div>
<div style="text-align: center; font-size: 14px;">@wyatt_privilege</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 warrior cop.
| Data | warrior cop |
| --- | --- |
| Tweets downloaded | 3216 |
| Retweets | 586 |
| Short tweets | 435 |
| Tweets kept | 2195 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2bj8kaj1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wyatt_privilege's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1sa8rdju) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1sa8rdju/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/wyatt_privilege')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/wyatt_privilege
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
warrior cop
@wyatt\_privilege
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 warrior cop.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @wyatt\_privilege's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1340068830673027074/VVV2NNgn_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">C a r p 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@wyattpuppers 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 [@wyattpuppers's tweets](https://twitter.com/wyattpuppers).
<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'>3190</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'>478</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'>481</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2231</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1bl7smzv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wyattpuppers's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ld6fkx1k) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ld6fkx1k/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/wyattpuppers'</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/wyattpuppers/1609011422698/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/wyattpuppers
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">C a r p AI Bot </div>
<div style="font-size: 15px; color: #657786">@wyattpuppers 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 @wyattpuppers'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'>3190</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'>478</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'>481</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2231</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 @wyattpuppers's tweets.
Hyperparameters and metrics are 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/wyattpuppers'</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 @wyattpuppers'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'>3190</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'>478</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'>481</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2231</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 @wyattpuppers'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/wyattpuppers'</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 @wyattpuppers'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'>3190</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'>478</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'>481</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2231</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 @wyattpuppers'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/wyattpuppers'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1375865466095239170/D5rWgwpw_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">xane 🤖 AI Bot </div>
<div style="font-size: 15px">@xaneowski 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 [@xaneowski's tweets](https://twitter.com/xaneowski).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1672 |
| Retweets | 18 |
| Short tweets | 208 |
| Tweets kept | 1446 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2d0e2uns/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @xaneowski's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/27c0d7fs) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/27c0d7fs/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/xaneowski')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/xaneowski/1616904322673/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/xaneowski
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
xane AI Bot
@xaneowski 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 @xaneowski'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 @xaneowski's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1322124437039165442/wNDVA07K_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">xean 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@xescobin 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 [@xescobin's tweets](https://twitter.com/xescobin).
<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'>848</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'>51</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'>213</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>584</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3tc2mjf0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @xescobin's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2gvtor1n) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2gvtor1n/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/xescobin'</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/xescobin/1608382856568/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/xescobin
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">xean AI Bot </div>
<div style="font-size: 15px; color: #657786">@xescobin 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 @xescobin'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'>848</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'>51</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'>213</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>584</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 @xescobin's tweets.
Hyperparameters and metrics are 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/xescobin'</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 @xescobin'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'>848</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'>51</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'>213</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>584</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 @xescobin'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/xescobin'</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 @xescobin'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'>848</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'>51</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'>213</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>584</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 @xescobin'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/xescobin'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1246621358458433536/JlSVXK2__400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Xiaomi 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@xiaomi 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 [@xiaomi's tweets](https://twitter.com/xiaomi).
<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'>273</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'>99</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2853</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3jsfmwvr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @xiaomi's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/16pogxna) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/16pogxna/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/xiaomi'</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/xiaomi/1609716260950/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/xiaomi
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Xiaomi AI Bot </div>
<div style="font-size: 15px; color: #657786">@xiaomi 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 @xiaomi'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'>273</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'>99</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2853</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 @xiaomi's tweets.
Hyperparameters and metrics are 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/xiaomi'</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 @xiaomi'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'>273</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'>99</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2853</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 @xiaomi'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/xiaomi'</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 @xiaomi'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'>273</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'>99</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2853</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 @xiaomi'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/xiaomi'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1185460679550984192/jCxIECDA_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Xinqi Su 蘇昕琪 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@xinqisu 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 [@xinqisu's tweets](https://twitter.com/xinqisu).
<table style='border-width:0'>
<thead style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
<th style='border-width:0'>Data</th>
<th style='border-width:0'>Quantity</th>
</tr>
</thead>
<tbody style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Tweets downloaded</td>
<td style='border-width:0'>3248</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Retweets</td>
<td style='border-width:0'>174</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'>39</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>3035</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1l67ih2t/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @xinqisu's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2m0wt4pe) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2m0wt4pe/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/xinqisu'</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/xinqisu/1607803999992/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/xinqisu
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Xinqi Su 蘇昕琪 AI Bot </div>
<div style="font-size: 15px; color: #657786">@xinqisu 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 @xinqisu's tweets.
<table style='border-width:0'>
<thead style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
<th style='border-width:0'>Data</th>
<th style='border-width:0'>Quantity</th>
</tr>
</thead>
<tbody style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Tweets downloaded</td>
<td style='border-width:0'>3248</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Retweets</td>
<td style='border-width:0'>174</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'>39</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>3035</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 @xinqisu's tweets.
Hyperparameters and metrics are 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/xinqisu'</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 @xinqisu's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3248</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>174</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'>39</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>3035</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 @xinqisu'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/xinqisu'</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 @xinqisu's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3248</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>174</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'>39</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>3035</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 @xinqisu'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/xinqisu'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1366426861299920900/A1ynltRo_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">𝙁𝙀𝙈𝘽𝙊𝙔 𝙏𝙍𝘼𝙉𝙎𝙄𝙏𝙄𝙊𝙉𝙄𝙉𝙂 🤖 AI Bot </div>
<div style="font-size: 15px">@xwylraz0rbl4d3x 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 [@xwylraz0rbl4d3x's tweets](https://twitter.com/xwylraz0rbl4d3x).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3152 |
| Retweets | 682 |
| Short tweets | 631 |
| Tweets kept | 1839 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/36q46b23/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @xwylraz0rbl4d3x's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3a0sqgtk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3a0sqgtk/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/xwylraz0rbl4d3x')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/xwylraz0rbl4d3x/1617889284619/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/xwylraz0rbl4d3x
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
𝙁𝙀𝙈𝘽𝙊𝙔 𝙏𝙍𝘼𝙉𝙎𝙄𝙏𝙄𝙊𝙉𝙄𝙉𝙂 AI Bot
@xwylraz0rbl4d3x 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 @xwylraz0rbl4d3x'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 @xwylraz0rbl4d3x's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1596905325273161729/EF7pPdba_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">poolboy 𝖀𝖓𝖆𝕱𝖚𝖙𝖚𝖗𝖊 Iɳɳҽɾɳҽƚƚҽ</div>
<div style="text-align: center; font-size: 14px;">@xxinnernettexx</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 poolboy 𝖀𝖓𝖆𝕱𝖚𝖙𝖚𝖗𝖊 Iɳɳҽɾɳҽƚƚҽ.
| Data | poolboy 𝖀𝖓𝖆𝕱𝖚𝖙𝖚𝖗𝖊 Iɳɳҽɾɳҽƚƚҽ |
| --- | --- |
| Tweets downloaded | 1873 |
| Retweets | 481 |
| Short tweets | 254 |
| Tweets kept | 1138 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/x8bo3dly/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @xxinnernettexx's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/h50tqo56) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/h50tqo56/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/xxinnernettexx')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/xxinnernettexx/1673814262588/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/xxinnernettexx
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
poolboy 𝖀𝖓𝖆𝕱𝖚𝖙𝖚𝖗𝖊 Iɳɳҽɾɳҽƚƚҽ
@xxinnernettexx
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 poolboy 𝖀𝖓𝖆𝕱𝖚𝖙𝖚𝖗𝖊 Iɳɳҽɾɳҽƚƚҽ.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @xxinnernettexx's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/978499874923556866/8vBDa_LU_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Nicolas Bray 🤖 AI Bot </div>
<div style="font-size: 15px">@yarbsalocin 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 [@yarbsalocin's tweets](https://twitter.com/yarbsalocin).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3243 |
| Retweets | 135 |
| Short tweets | 190 |
| Tweets kept | 2918 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ps5uhut/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @yarbsalocin's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/tsu7r7s9) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/tsu7r7s9/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/yarbsalocin')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/yarbsalocin/1616647825805/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/yarbsalocin
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Nicolas Bray AI Bot
@yarbsalocin 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 @yarbsalocin'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 @yarbsalocin's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/716641091311697920/hFFVBhFe_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Y Combinator 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@ycombinator 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 [@ycombinator's tweets](https://twitter.com/ycombinator).
<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'>3220</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'>726</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'>14</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2480</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/256at4s3/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ycombinator's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/31k6k27j) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/31k6k27j/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/ycombinator'</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/ycombinator/1603447091260/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/ycombinator
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Y Combinator AI Bot </div>
<div style="font-size: 15px; color: #657786">@ycombinator 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 @ycombinator'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'>3220</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'>726</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'>14</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2480</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 @ycombinator's tweets.
Hyperparameters and metrics are 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/ycombinator'</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 @ycombinator'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'>3220</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'>726</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'>14</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2480</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 @ycombinator'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/ycombinator'</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 @ycombinator'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'>3220</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'>726</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'>14</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2480</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 @ycombinator'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/ycombinator'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/647352395622653952/WEuxh8dK_400x400.png')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">evilyeahyeahyens 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@yeahyeahyens 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 [@yeahyeahyens's tweets](https://twitter.com/yeahyeahyens).
<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'>3244</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'>492</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'>327</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2425</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3udu67c3/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @yeahyeahyens's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2lmppjy9) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2lmppjy9/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/yeahyeahyens'</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/yeahyeahyens/1612175424382/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/yeahyeahyens
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">evilyeahyeahyens AI Bot </div>
<div style="font-size: 15px; color: #657786">@yeahyeahyens 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 @yeahyeahyens'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'>3244</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'>492</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'>327</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2425</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 @yeahyeahyens's tweets.
Hyperparameters and metrics are 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/yeahyeahyens'</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 @yeahyeahyens'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'>3244</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'>492</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'>327</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2425</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 @yeahyeahyens'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/yeahyeahyens'</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 @yeahyeahyens'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'>3244</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'>492</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'>327</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2425</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 @yeahyeahyens'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/yeahyeahyens'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1198233166399647744/6UjqTBIT_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">Ludwig Yeetgenstein</div>
<div style="text-align: center; font-size: 14px;">@yeetgenstein</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 Ludwig Yeetgenstein.
| Data | Ludwig Yeetgenstein |
| --- | --- |
| Tweets downloaded | 3237 |
| Retweets | 218 |
| Short tweets | 367 |
| Tweets kept | 2652 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/27hq0lqc/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @yeetgenstein's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1aykas3j) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1aykas3j/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/yeetgenstein')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/yeetgenstein/1639413081074/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/yeetgenstein
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Ludwig Yeetgenstein
@yeetgenstein
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 Ludwig Yeetgenstein.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @yeetgenstein's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1374515470498373639/dygzoNob_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Toby (Inactive) 🗳️🥑⚙️🚊🌃🚀🌐 🤖 AI Bot </div>
<div style="font-size: 15px">@yellowdogedem 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 [@yellowdogedem's tweets](https://twitter.com/yellowdogedem).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3233 |
| Retweets | 494 |
| Short tweets | 788 |
| Tweets kept | 1951 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ln60y7n/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @yellowdogedem's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2vj8xb46) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2vj8xb46/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/yellowdogedem')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/yellowdogedem/1617172947443/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/yellowdogedem
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Toby (Inactive) ️️ AI Bot
@yellowdogedem 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 @yellowdogedem'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 @yellowdogedem's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1400613553070018571/4_Sit9I4_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">🏳🌈🌸 Pride Huwu-Yenny 🌸🏳🌈</div>
<div style="text-align: center; font-size: 14px;">@yennyowo</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 🏳🌈🌸 Pride Huwu-Yenny 🌸🏳🌈.
| Data | 🏳🌈🌸 Pride Huwu-Yenny 🌸🏳🌈 |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 50 |
| Short tweets | 1015 |
| Tweets kept | 2185 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1e63a0zl/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @yennyowo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2rh23jhk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2rh23jhk/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/yennyowo')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/yennyowo/1623067005926/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/yennyowo
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Pride Huwu-Yenny
@yennyowo
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 Pride Huwu-Yenny .
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @yennyowo's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
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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/1339194351298072577/tfJheAeM_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Yieee Nagi Taco (Sky) 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@yieee_nagitaco 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 [@yieee_nagitaco's tweets](https://twitter.com/yieee_nagitaco).
<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'>3032</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'>789</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'>623</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1620</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1g8y06jc/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @yieee_nagitaco's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1tjubyxk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1tjubyxk/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/yieee_nagitaco'</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/yieee_nagitaco/1608381956095/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/yieee_nagitaco
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Yieee Nagi Taco (Sky) AI Bot </div>
<div style="font-size: 15px; color: #657786">@yieee_nagitaco 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 @yieee_nagitaco'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'>3032</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'>789</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'>623</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1620</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 @yieee_nagitaco's tweets.
Hyperparameters and metrics are 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/yieee_nagitaco'</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 @yieee_nagitaco'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'>3032</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'>789</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'>623</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1620</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 @yieee_nagitaco'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/yieee_nagitaco'</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 @yieee_nagitaco'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'>3032</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'>789</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'>623</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1620</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 @yieee_nagitaco'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/yieee_nagitaco'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1428772517347479552/fT9QUaOy_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">Erpan Pardon</div>
<div style="text-align: center; font-size: 14px;">@yierpaen</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 Erpan Pardon.
| Data | Erpan Pardon |
| --- | --- |
| Tweets downloaded | 3025 |
| Retweets | 2613 |
| Short tweets | 106 |
| Tweets kept | 306 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2jk3rfqi/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @yierpaen's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/y2mm5kxj) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/y2mm5kxj/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/yierpaen')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/yierpaen/1635516027908/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/yierpaen
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Erpan Pardon
@yierpaen
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 Erpan Pardon.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @yierpaen's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1407084026507182089/ywRe7M0Z_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">yiğit</div>
<div style="text-align: center; font-size: 14px;">@yigitckahyaoglu</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 yiğit.
| Data | yiğit |
| --- | --- |
| Tweets downloaded | 1671 |
| Retweets | 165 |
| Short tweets | 64 |
| Tweets kept | 1442 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2cqhj21l/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @yigitckahyaoglu's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2k3eal89) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2k3eal89/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/yigitckahyaoglu')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/yigitckahyaoglu/1626750011426/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/yigitckahyaoglu
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
yiğit
@yigitckahyaoglu
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 yiğit.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @yigitckahyaoglu's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1483577865056702469/rWA-3_T7_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">Yann LeCun</div>
<div style="text-align: center; font-size: 14px;">@ylecun</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 Yann LeCun.
| Data | Yann LeCun |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 573 |
| Short tweets | 231 |
| Tweets kept | 2445 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/t83vleac/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ylecun's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/97id902s) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/97id902s/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ylecun')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/ylecun
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Yann LeCun
@ylecun
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 Yann LeCun.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @ylecun's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1334724928059240448/hrHRlrJF_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">YouCleanItUp</div>
<div style="text-align: center; font-size: 14px;">@youcleanitup1</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 YouCleanItUp.
| Data | YouCleanItUp |
| --- | --- |
| Tweets downloaded | 3027 |
| Retweets | 131 |
| Short tweets | 111 |
| Tweets kept | 2785 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/18s0ux8o/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @youcleanitup1's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1g9i1qvf) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1g9i1qvf/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/youcleanitup1')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/youcleanitup1/1627704616740/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/youcleanitup1
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
YouCleanItUp
@youcleanitup1
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 YouCleanItUp.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @youcleanitup1's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1423284698046865415/vfSSZ3t9_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">🥴</div>
<div style="text-align: center; font-size: 14px;">@yourfavhwhw</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 🥴.
| Data | 🥴 |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 57 |
| Short tweets | 525 |
| Tweets kept | 2664 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/18wxe7tu/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @yourfavhwhw's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/imwcf0iy) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/imwcf0iy/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/yourfavhwhw')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/yourfavhwhw/1629984367533/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/yourfavhwhw
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
@yourfavhwhw
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 .
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @yourfavhwhw's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1184826580910125057/gqE8fCKg_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Internet Dad 🤖 AI Bot </div>
<div style="font-size: 15px">@youronlinedad 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 [@youronlinedad's tweets](https://twitter.com/youronlinedad).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3201 |
| Retweets | 41 |
| Short tweets | 508 |
| Tweets kept | 2652 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3g7jg14o/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @youronlinedad's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2t2wy77n) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2t2wy77n/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/youronlinedad')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/youronlinedad/1614100614383/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/youronlinedad
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Internet Dad AI Bot
@youronlinedad 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 @youronlinedad'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 @youronlinedad's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1476997379857723392/L6czpqmI_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">ゆう🇲🇾英語を軸に人生に革新を🔥</div>
<div style="text-align: center; font-size: 14px;">@yu_kisub21</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 ゆう🇲🇾英語を軸に人生に革新を🔥.
| Data | ゆう🇲🇾英語を軸に人生に革新を🔥 |
| --- | --- |
| Tweets downloaded | 1580 |
| Retweets | 366 |
| Short tweets | 1137 |
| Tweets kept | 77 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1fswx6qh/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @yu_kisub21's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/35tec8b2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/35tec8b2/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/yu_kisub21')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/yu_kisub21/1643037750346/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/yu_kisub21
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
ゆう🇲🇾英語を軸に人生に革新を
@yu\_kisub21
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 ゆう🇲🇾英語を軸に人生に革新を.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @yu\_kisub21's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1308291810830086144/t4CsThlT_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">Yujacha차차</div>
<div style="text-align: center; font-size: 14px;">@yujachachacha</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 Yujacha차차.
| Data | Yujacha차차 |
| --- | --- |
| Tweets downloaded | 3241 |
| Retweets | 2281 |
| Short tweets | 42 |
| Tweets kept | 918 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/u9lcz1jg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @yujachachacha's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3nhqqjka) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3nhqqjka/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/yujachachacha')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/yujachachacha/1631662527114/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/yujachachacha
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Yujacha차차
@yujachachacha
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 Yujacha차차.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @yujachachacha's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1350388215975456768/hyUBufqg_400x400.png')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Yujiri (my likes don't always work) 🤖 AI Bot </div>
<div style="font-size: 15px">@yujiri3 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 [@yujiri3's tweets](https://twitter.com/yujiri3).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3182 |
| Retweets | 773 |
| Short tweets | 318 |
| Tweets kept | 2091 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/353ok7f3/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @yujiri3's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/sqw7eetr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/sqw7eetr/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/yujiri3')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/yujiri3/1616651697285/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/yujiri3
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Yujiri (my likes don't always work) AI Bot
@yujiri3 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 @yujiri3'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 @yujiri3's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1359285189482934280/-NJ6GZ4M_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Brandon Macdonald 🤖 AI Bot </div>
<div style="font-size: 15px">@yukonbrandon 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 [@yukonbrandon's tweets](https://twitter.com/yukonbrandon).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 105 |
| Short tweets | 128 |
| Tweets kept | 3016 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/36sc6qhf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @yukonbrandon's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/8xctkx0v) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/8xctkx0v/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/yukonbrandon')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/yukonbrandon/1617458018352/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/yukonbrandon
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Brandon Macdonald AI Bot
@yukonbrandon 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 @yukonbrandon'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 @yukonbrandon's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1358907764374970368/tJHY7eRK_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Certified Hairbrush Licker 🤖 AI Bot </div>
<div style="font-size: 15px">@yung_caribou 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 [@yung_caribou's tweets](https://twitter.com/yung_caribou).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 772 |
| Retweets | 52 |
| Short tweets | 158 |
| Tweets kept | 562 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1c7fnh7f/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @yung_caribou's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2palqva5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2palqva5/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/yung_caribou')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/yung_caribou/1614136835166/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/yung_caribou
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Certified Hairbrush Licker AI Bot
@yung\_caribou 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 @yung\_caribou'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 @yung\_caribou's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1305960885987532802/6TzwD8_B_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Astronaut Cum Evangelist 🇬🇷 🤖 AI Bot </div>
<div style="font-size: 15px">@yungparenti 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 [@yungparenti's tweets](https://twitter.com/yungparenti).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3232 |
| Retweets | 211 |
| Short tweets | 507 |
| Tweets kept | 2514 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/20pdwcql/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @yungparenti's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/oi3awf8d) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/oi3awf8d/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/yungparenti')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/yungparenti/1619285093850/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/yungparenti
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Astronaut Cum Evangelist 🇬🇷 AI Bot
@yungparenti 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 @yungparenti'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 @yungparenti's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1343286808755499008/5MMYpRNN_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">yuureimi🌸 🤖 AI Bot </div>
<div style="font-size: 15px">@yuureimi 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 [@yuureimi's tweets](https://twitter.com/yuureimi).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2885 |
| Retweets | 2570 |
| Short tweets | 53 |
| Tweets kept | 262 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/21dq7xjn/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @yuureimi's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2hunxb6z) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2hunxb6z/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/yuureimi')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/yuureimi/1614217561886/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/yuureimi
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
yuureimi AI Bot
@yuureimi 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 @yuureimi'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 @yuureimi's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1369145347155591168/eRKzbkIx_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Yiyang Chen 🤖 AI Bot </div>
<div style="font-size: 15px">@yybbhn 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 [@yybbhn's tweets](https://twitter.com/yybbhn).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 107 |
| Retweets | 25 |
| Short tweets | 9 |
| Tweets kept | 73 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/16g99wak/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @yybbhn's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1pj7k13c) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1pj7k13c/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/yybbhn')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/yybbhn
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Yiyang Chen AI Bot
@yybbhn 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 @yybbhn'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 @yybbhn's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1336547084372234241/ZthomfiN_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Zachary 💻🖱️ 🤖 AI Bot </div>
<div style="font-size: 15px">@zacharyhundley 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 [@zacharyhundley's tweets](https://twitter.com/zacharyhundley).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 260 |
| Short tweets | 797 |
| Tweets kept | 2189 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2d5adsoe/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zacharyhundley's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ni6yl621) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ni6yl621/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/zacharyhundley')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/zacharyhundley/1616620657215/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/zacharyhundley
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Zachary ️ AI Bot
@zacharyhundley 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 @zacharyhundley'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 @zacharyhundley's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1142440824766193664/6NK0B-Gr_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Zach Fox 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@zachfox 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 [@zachfox's tweets](https://twitter.com/zachfox).
<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'>3168</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'>547</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'>415</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2206</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/21svlsaa/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zachfox's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/21o6mb7e) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/21o6mb7e/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/zachfox'</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/zachfox/1603321719549/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/zachfox
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Zach Fox AI Bot </div>
<div style="font-size: 15px; color: #657786">@zachfox 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 @zachfox'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'>3168</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'>547</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'>415</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2206</td>
</tr>
</tbody>
</table>
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
## Training procedure
The model is based on a pre-trained GPT-2 which is fine-tuned on @zachfox's tweets.
Hyperparameters and metrics are 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/zachfox'</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 @zachfox'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'>3168</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'>547</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'>415</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2206</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @zachfox'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/zachfox'</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 @zachfox'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'>3168</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'>547</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'>415</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2206</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @zachfox'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/zachfox'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1512157386441695245/YdLmcC-0_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">Zack Fox</div>
<div style="text-align: center; font-size: 14px;">@zackfox</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 Zack Fox.
| Data | Zack Fox |
| --- | --- |
| Tweets downloaded | 1237 |
| Retweets | 197 |
| Short tweets | 405 |
| Tweets kept | 635 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/9m88vwnn/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zackfox's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/x5x1e1wv) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/x5x1e1wv/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/zackfox')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}
|
huggingtweets/zackfox
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Zack Fox
@zackfox
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 Zack Fox.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @zackfox's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1058563940223868928/08EbRbwT_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Zack M. Davis 🤖 AI Bot </div>
<div style="font-size: 15px">@zackmdavis 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 [@zackmdavis's tweets](https://twitter.com/zackmdavis).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3178 |
| Retweets | 1350 |
| Short tweets | 111 |
| Tweets kept | 1717 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1uu0syjb/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zackmdavis's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2x5kqcep) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2x5kqcep/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/zackmdavis')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/zackmdavis/1617765418297/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/zackmdavis
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Zack M. Davis AI Bot
@zackmdavis 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 @zackmdavis'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 @zackmdavis's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1286629921037508608/cC3hHmm7_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Zash Skoe 🚀 🤖 AI Bot </div>
<div style="font-size: 15px">@zashskoe 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 [@zashskoe's tweets](https://twitter.com/zashskoe).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3248 |
| Retweets | 35 |
| Short tweets | 555 |
| Tweets kept | 2658 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/8qwi2vtm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zashskoe's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2e7v0bbr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2e7v0bbr/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/zashskoe')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/zashskoe/1617795305571/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/zashskoe
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Zash Skoe AI Bot
@zashskoe 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 @zashskoe'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 @zashskoe's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1602021424021004289/rjN3u-qE_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">Quixotesco</div>
<div style="text-align: center; font-size: 14px;">@zavaralho</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 Quixotesco.
| Data | Quixotesco |
| --- | --- |
| Tweets downloaded | 3188 |
| Retweets | 438 |
| Short tweets | 780 |
| Tweets kept | 1970 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/nzapag8i/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zavaralho's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/5i429j1j) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/5i429j1j/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/zavaralho')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/zavaralho/1670952738869/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/zavaralho
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Quixotesco
@zavaralho
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 Quixotesco.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @zavaralho's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1103665627183472642/OVXzwAk7_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">Shreya Mukherjee 💀🌻</div>
<div style="text-align: center; font-size: 14px;">@zeebeecat01</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 Shreya Mukherjee 💀🌻.
| Data | Shreya Mukherjee 💀🌻 |
| --- | --- |
| Tweets downloaded | 731 |
| Retweets | 552 |
| Short tweets | 33 |
| Tweets kept | 146 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/kz1pvshu/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zeebeecat01's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3btkttwk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3btkttwk/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/zeebeecat01')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/zeebeecat01/1645914254405/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/zeebeecat01
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Shreya Mukherjee
@zeebeecat01
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 Shreya Mukherjee .
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @zeebeecat01's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1465637600060919808/8nQIBTEI_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">Eric Zemmour</div>
<div style="text-align: center; font-size: 14px;">@zemmoureric</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 Eric Zemmour.
| Data | Eric Zemmour |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 546 |
| Short tweets | 175 |
| Tweets kept | 2523 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/24afk8k1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zemmoureric's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2dz80r2t) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2dz80r2t/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/zemmoureric')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/zemmoureric/1638897923015/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/zemmoureric
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Eric Zemmour
@zemmoureric
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 Eric Zemmour.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @zemmoureric's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1362563256230031366/ujesNtUk_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">MEMENTO BUNI 🤖 AI Bot </div>
<div style="font-size: 15px">@zetsubunny 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 [@zetsubunny's tweets](https://twitter.com/zetsubunny).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3230 |
| Retweets | 1122 |
| Short tweets | 264 |
| Tweets kept | 1844 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3u5onhoi/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zetsubunny's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/tv6yd107) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/tv6yd107/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/zetsubunny')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/zetsubunny/1617772715416/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/zetsubunny
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
MEMENTO BUNI AI Bot
@zetsubunny 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 @zetsubunny'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 @zetsubunny's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1354239840305471491/qYoJiz8K_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">zeynep tufekci</div>
<div style="text-align: center; font-size: 14px;">@zeynep</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 zeynep tufekci.
| Data | zeynep tufekci |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 384 |
| Short tweets | 339 |
| Tweets kept | 2522 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3v1ciuhl/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zeynep's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/144e2xer) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/144e2xer/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/zeynep')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/zeynep/1624880317549/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/zeynep
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
zeynep tufekci
@zeynep
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 zeynep tufekci.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @zeynep's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1033823587071737856/pDlHy2Sh_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">Joshua Herman</div>
<div style="text-align: center; font-size: 14px;">@zitterbewegung</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 Joshua Herman.
| Data | Joshua Herman |
| --- | --- |
| Tweets downloaded | 3223 |
| Retweets | 1452 |
| Short tweets | 141 |
| Tweets kept | 1630 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1rvhbbck/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zitterbewegung's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2qc7e3m1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2qc7e3m1/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/zitterbewegung')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/zitterbewegung/1633265493095/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/zitterbewegung
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Joshua Herman
@zitterbewegung
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 Joshua Herman.
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @zitterbewegung's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
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<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
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</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Zev Karlin-Neumann 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@zkarlinn 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 [@zkarlinn's tweets](https://twitter.com/zkarlinn).
<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'>2237</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'>801</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/260jmvfw/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zkarlinn's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/67rfsha0) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/67rfsha0/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/zkarlinn'</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/zkarlinn/1612670051245/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/zkarlinn
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Zev Karlin-Neumann AI Bot </div>
<div style="font-size: 15px; color: #657786">@zkarlinn 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 @zkarlinn'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'>2237</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'>801</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 @zkarlinn's tweets.
Hyperparameters and metrics are 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/zkarlinn'</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 @zkarlinn'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'>2237</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'>801</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 @zkarlinn'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/zkarlinn'</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 @zkarlinn'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'>2237</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'>801</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 @zkarlinn'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/zkarlinn'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/933754221257723904/OPVfNgZG_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Tauhid R. Zaman 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@zlisto 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 [@zlisto's tweets](https://twitter.com/zlisto).
<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'>3157</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'>1202</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'>117</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1838</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3p8ylpjm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zlisto's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/220gmo20) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/220gmo20/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/zlisto'</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/zlisto/1611290409885/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/zlisto
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Tauhid R. Zaman AI Bot </div>
<div style="font-size: 15px; color: #657786">@zlisto 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 @zlisto'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'>3157</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'>1202</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'>117</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1838</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 @zlisto's tweets.
Hyperparameters and metrics are 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/zlisto'</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 @zlisto'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'>3157</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'>1202</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'>117</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1838</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 @zlisto'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/zlisto'</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 @zlisto'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'>3157</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'>1202</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'>117</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1838</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 @zlisto'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/zlisto'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1330687610625220611/RGVkpYG-_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">ZOEOZONE 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@zoebot_zoe 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 [@zoebot_zoe's tweets](https://twitter.com/zoebot_zoe).
<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'>3196</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'>597</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Short tweets</td>
<td style='border-width:0'>410</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2189</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1cddeevp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zoebot_zoe's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1kaybw72) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1kaybw72/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/zoebot_zoe'</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/zoebot_zoe/1607527750457/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/zoebot_zoe
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">ZOEOZONE AI Bot </div>
<div style="font-size: 15px; color: #657786">@zoebot_zoe 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 @zoebot_zoe'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'>3196</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'>597</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Short tweets</td>
<td style='border-width:0'>410</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2189</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 @zoebot_zoe's tweets.
Hyperparameters and metrics are 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/zoebot_zoe'</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 @zoebot_zoe'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'>3196</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'>597</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>410</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2189</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 @zoebot_zoe'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/zoebot_zoe'</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 @zoebot_zoe'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'>3196</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'>597</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>410</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2189</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 @zoebot_zoe'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/zoebot_zoe'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>",
"### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.",
"## About\n\n*Built by Boris Dayma*\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL"
] |
text-generation
|
transformers
|
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1332547297914175494/RAz44L4J_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">object-level jail 🚨 🤖 AI Bot </div>
<div style="font-size: 15px">@zrkrlc 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 [@zrkrlc's tweets](https://twitter.com/zrkrlc).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2241 |
| Retweets | 228 |
| Short tweets | 204 |
| Tweets kept | 1809 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2g51am53/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zrkrlc's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/23unb7ar) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/23unb7ar/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/zrkrlc')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/zrkrlc/1616626091555/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/zrkrlc
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
object-level jail AI Bot
@zrkrlc 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 @zrkrlc'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 @zrkrlc's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1486464160422670337/mEvWu0_w_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">Alex Becker 🍊🏆🥇</div>
<div style="text-align: center; font-size: 14px;">@zssbecker</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [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 Alex Becker 🍊🏆🥇.
| Data | Alex Becker 🍊🏆🥇 |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 53 |
| Short tweets | 453 |
| Tweets kept | 2743 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2j8o1cf7/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zssbecker's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1qe600k1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1qe600k1/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/zssbecker')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://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/zssbecker/1643661417699/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/zssbecker
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Alex Becker
@zssbecker
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model 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 Alex Becker .
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
------------------
The model is based on a pre-trained GPT-2 which is fine-tuned on @zssbecker's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
How to use
----------
You can use this model directly with a pipeline for text generation:
Limitations and bias
--------------------
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
About
-----
*Built by Boris Dayma*
![Follow](URL
For more details, visit the project repository.
![GitHub stars](URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/793249343713243137/L-ZrfLj5_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Zvi S. Rosen 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@zvisrosen 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 [@zvisrosen's tweets](https://twitter.com/zvisrosen).
<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'>3232</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'>225</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'>85</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2922</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3awttigi/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zvisrosen's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3rths1wy) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3rths1wy/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/zvisrosen'</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/zvisrosen/1607051627200/predictions.png", "widget": [{"text": "My dream is"}]}
|
huggingtweets/zvisrosen
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<link rel="stylesheet" href="URL
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Zvi S. Rosen AI Bot </div>
<div style="font-size: 15px; color: #657786">@zvisrosen 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 @zvisrosen'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'>3232</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'>225</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'>85</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2922</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 @zvisrosen's tweets.
Hyperparameters and metrics are 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/zvisrosen'</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 @zvisrosen'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'>3232</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'>225</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'>85</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2922</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 @zvisrosen'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/zvisrosen'</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 @zvisrosen'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'>3232</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'>225</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'>85</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2922</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 @zvisrosen'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/zvisrosen'</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"
] |
sentence-similarity
|
sentence-transformers
|
# hugorosen/flaubert_base_uncased-xnli-sts
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["Ceci est une phrase d'exemple", "Chaque phrase est convertie"]
model = SentenceTransformer('hugorosen/flaubert_base_uncased-xnli-sts')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ["Un avion est en train de décoller.",
"Un homme joue d'une grande flûte.",
"Un homme étale du fromage râpé sur une pizza.",
"Une personne jette un chat au plafond.",
"Une personne est en train de plier un morceau de papier.",
]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('hugorosen/flaubert_base_uncased-xnli-sts')
model = AutoModel.from_pretrained('hugorosen/flaubert_base_uncased-xnli-sts')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
This model scores 76.9% on STS test (french)
## Training
### Pre-training
We use the pre-trained [flaubert/flaubert_base_uncased](https://huggingface.co/flaubert/flaubert_base_cased). Please refer to the model card for more detailed information about the pre-training procedure.
### Fine-tuning
we fine-tune the model using a `CosineSimilarityLoss` on XNLI and STS dataset (french).
Parameters of the fit()-Method:
```
{
"epochs": 4,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 144,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: FlaubertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
Fine-tuned for semantic similarity by Hugo Rosenkranz-costa.
Based on FlauBERT:
```
@InProceedings{le2020flaubert,
author = {Le, Hang and Vial, Lo\"{i}c and Frej, Jibril and Segonne, Vincent and Coavoux, Maximin and Lecouteux, Benjamin and Allauzen, Alexandre and Crabb\'{e}, Beno\^{i}t and Besacier, Laurent and Schwab, Didier},
title = {FlauBERT: Unsupervised Language Model Pre-training for French},
booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference},
month = {May},
year = {2020},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {2479--2490},
url = {https://www.aclweb.org/anthology/2020.lrec-1.302}
}
```
|
{"language": "fr", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "fr"], "datasets": ["xnli", "stsb_multi_mt"], "pipeline_tag": "sentence-similarity"}
|
hugorosen/flaubert_base_uncased-xnli-sts
| null |
[
"sentence-transformers",
"pytorch",
"flaubert",
"feature-extraction",
"sentence-similarity",
"transformers",
"fr",
"dataset:xnli",
"dataset:stsb_multi_mt",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"fr"
] |
TAGS
#sentence-transformers #pytorch #flaubert #feature-extraction #sentence-similarity #transformers #fr #dataset-xnli #dataset-stsb_multi_mt #endpoints_compatible #region-us
|
# hugorosen/flaubert_base_uncased-xnli-sts
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
## Evaluation Results
This model scores 76.9% on STS test (french)
## Training
### Pre-training
We use the pre-trained flaubert/flaubert_base_uncased. Please refer to the model card for more detailed information about the pre-training procedure.
### Fine-tuning
we fine-tune the model using a 'CosineSimilarityLoss' on XNLI and STS dataset (french).
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
Fine-tuned for semantic similarity by Hugo Rosenkranz-costa.
Based on FlauBERT:
|
[
"# hugorosen/flaubert_base_uncased-xnli-sts\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nThis model scores 76.9% on STS test (french)",
"## Training",
"### Pre-training\n\nWe use the pre-trained flaubert/flaubert_base_uncased. Please refer to the model card for more detailed information about the pre-training procedure.",
"### Fine-tuning\n\nwe fine-tune the model using a 'CosineSimilarityLoss' on XNLI and STS dataset (french).\n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors\n\nFine-tuned for semantic similarity by Hugo Rosenkranz-costa.\n\nBased on FlauBERT:"
] |
[
"TAGS\n#sentence-transformers #pytorch #flaubert #feature-extraction #sentence-similarity #transformers #fr #dataset-xnli #dataset-stsb_multi_mt #endpoints_compatible #region-us \n",
"# hugorosen/flaubert_base_uncased-xnli-sts\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nThis model scores 76.9% on STS test (french)",
"## Training",
"### Pre-training\n\nWe use the pre-trained flaubert/flaubert_base_uncased. Please refer to the model card for more detailed information about the pre-training procedure.",
"### Fine-tuning\n\nwe fine-tune the model using a 'CosineSimilarityLoss' on XNLI and STS dataset (french).\n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors\n\nFine-tuned for semantic similarity by Hugo Rosenkranz-costa.\n\nBased on FlauBERT:"
] |
token-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLM-R-fine-tuned-for-ner
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5679
- F1: 0.8378
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.4202 | 1.0 | 2500 | 0.3449 | 0.7963 |
| 0.2887 | 2.0 | 5000 | 0.2756 | 0.8057 |
| 0.2309 | 3.0 | 7500 | 0.2971 | 0.8040 |
| 0.1832 | 4.0 | 10000 | 0.3319 | 0.8167 |
| 0.1461 | 5.0 | 12500 | 0.3958 | 0.8350 |
| 0.114 | 6.0 | 15000 | 0.4087 | 0.8316 |
| 0.0833 | 7.0 | 17500 | 0.4320 | 0.8361 |
| 0.0614 | 8.0 | 20000 | 0.4885 | 0.8353 |
| 0.039 | 9.0 | 22500 | 0.5408 | 0.8390 |
| 0.0251 | 10.0 | 25000 | 0.5679 | 0.8378 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 1.18.3
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "XLM-R-fine-tuned-for-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "metrics": [{"type": "f1", "value": 0.8377982238973259, "name": "F1"}]}]}]}
|
hugsao123/XLM-R-fine-tuned-for-ner
| null |
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
XLM-R-fine-tuned-for-ner
========================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5679
* F1: 0.8378
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.9.1
* Datasets 1.18.3
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.9.1\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.9.1\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] |
token-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3903
- F1: 0.7590
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0489 | 1.0 | 50 | 0.5561 | 0.6565 |
| 0.4953 | 2.0 | 100 | 0.4385 | 0.7189 |
| 0.35 | 3.0 | 150 | 0.3903 | 0.7590 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.8.2+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "metrics": [{"type": "f1", "value": 0.7589617892939993, "name": "F1"}]}]}]}
|
hugsao123/xlm-roberta-base-finetuned-panx-de
| null |
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3903
* F1: 0.7590
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 24
* eval\_batch\_size: 24
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.8.2+cu111
* Datasets 1.18.3
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.8.2+cu111\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.8.2+cu111\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] |
summarization
|
transformers
|
### PEGASUS for Financial Summarization
This model was fine-tuned on a novel financial news dataset, which consists of 2K articles from [Bloomberg](https://www.bloomberg.com/europe), on topics such as stock, markets, currencies, rate and cryptocurrencies.
It is based on the [PEGASUS](https://huggingface.co/transformers/model_doc/pegasus.html) model and in particular PEGASUS fine-tuned on the Extreme Summarization (XSum) dataset: [google/pegasus-xsum model](https://huggingface.co/google/pegasus-xsum). PEGASUS was originally proposed by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu in [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/pdf/1912.08777.pdf).
### How to use
We provide a simple snippet of how to use this model for the task of financial summarization in PyTorch.
```Python
from transformers import PegasusTokenizer, PegasusForConditionalGeneration, TFPegasusForConditionalGeneration
# Let's load the model and the tokenizer
model_name = "human-centered-summarization/financial-summarization-pegasus"
tokenizer = PegasusTokenizer.from_pretrained(model_name)
model = PegasusForConditionalGeneration.from_pretrained(model_name) # If you want to use the Tensorflow model
# just replace with TFPegasusForConditionalGeneration
# Some text to summarize here
text_to_summarize = "National Commercial Bank (NCB), Saudi Arabia’s largest lender by assets, agreed to buy rival Samba Financial Group for $15 billion in the biggest banking takeover this year.NCB will pay 28.45 riyals ($7.58) for each Samba share, according to a statement on Sunday, valuing it at about 55.7 billion riyals. NCB will offer 0.739 new shares for each Samba share, at the lower end of the 0.736-0.787 ratio the banks set when they signed an initial framework agreement in June.The offer is a 3.5% premium to Samba’s Oct. 8 closing price of 27.50 riyals and about 24% higher than the level the shares traded at before the talks were made public. Bloomberg News first reported the merger discussions.The new bank will have total assets of more than $220 billion, creating the Gulf region’s third-largest lender. The entity’s $46 billion market capitalization nearly matches that of Qatar National Bank QPSC, which is still the Middle East’s biggest lender with about $268 billion of assets."
# Tokenize our text
# If you want to run the code in Tensorflow, please remember to return the particular tensors as simply as using return_tensors = 'tf'
input_ids = tokenizer(text_to_summarize, return_tensors="pt").input_ids
# Generate the output (Here, we use beam search but you can also use any other strategy you like)
output = model.generate(
input_ids,
max_length=32,
num_beams=5,
early_stopping=True
)
# Finally, we can print the generated summary
print(tokenizer.decode(output[0], skip_special_tokens=True))
# Generated Output: Saudi bank to pay a 3.5% premium to Samba share price. Gulf region’s third-largest lender will have total assets of $220 billion
```
## Evaluation Results
The results before and after the fine-tuning on our dataset are shown below:
| Fine-tuning | R-1 | R-2 | R-L | R-S |
|:-----------:|:-----:|:-----:|:------:|:-----:|
| Yes | 23.55 | 6.99 | 18.14 | 21.36 |
| No | 13.8 | 2.4 | 10.63 | 12.03 |
## Citation
You can find more details about this work in the following workshop paper. If you use our model in your research, please consider citing our paper:
> T. Passali, A. Gidiotis, E. Chatzikyriakidis and G. Tsoumakas. 2021.
> Towards Human-Centered Summarization: A Case Study on Financial News.
> In Proceedings of the First Workshop on Bridging Human-Computer Interaction and Natural Language Processing(pp. 21–27). Association for Computational Linguistics.
BibTeX entry:
```
@inproceedings{passali-etal-2021-towards,
title = "Towards Human-Centered Summarization: A Case Study on Financial News",
author = "Passali, Tatiana and Gidiotis, Alexios and Chatzikyriakidis, Efstathios and Tsoumakas, Grigorios",
booktitle = "Proceedings of the First Workshop on Bridging Human{--}Computer Interaction and Natural Language Processing",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.hcinlp-1.4",
pages = "21--27",
}
```
## Support
Contact us at [info@medoid.ai](mailto:info@medoid.ai) if you are interested in a more sophisticated version of the model, trained on more articles and adapted to your needs!
More information about Medoid AI:
- Website: [https://www.medoid.ai](https://www.medoid.ai)
- LinkedIn: [https://www.linkedin.com/company/medoid-ai/](https://www.linkedin.com/company/medoid-ai/)
|
{"language": ["en"], "tags": ["summarization"], "datasets": ["xsum"], "metrics": ["rouge"], "widget": [{"text": "National Commercial Bank (NCB), Saudi Arabia\u2019s largest lender by assets, agreed to buy rival Samba Financial Group for $15 billion in the biggest banking takeover this year.NCB will pay 28.45 riyals ($7.58) for each Samba share, according to a statement on Sunday, valuing it at about 55.7 billion riyals. NCB will offer 0.739 new shares for each Samba share, at the lower end of the 0.736-0.787 ratio the banks set when they signed an initial framework agreement in June.The offer is a 3.5% premium to Samba\u2019s Oct. 8 closing price of 27.50 riyals and about 24% higher than the level the shares traded at before the talks were made public. Bloomberg News first reported the merger discussions.The new bank will have total assets of more than $220 billion, creating the Gulf region\u2019s third-largest lender. The entity\u2019s $46 billion market capitalization nearly matches that of Qatar National Bank QPSC, which is still the Middle East\u2019s biggest lender with about $268 billion of assets."}], "model-index": [{"name": "human-centered-summarization/financial-summarization-pegasus", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "xsum", "type": "xsum", "config": "default", "split": "test"}, "metrics": [{"type": "rouge", "value": 35.2055, "name": "ROUGE-1", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTA5OTZkY2YxMDU1YzE3NGJlMmE1OTg1NjlmNzcxOTg4YzY2OThlOTlkNGFhMGFjZWY4YjdiMjU5NDdmMWYzNSIsInZlcnNpb24iOjF9.ufBRoV2JoX4UlEfAUOYq7F3tZougwngdpKlnaC37tYXJU3omsR5hTsWM69hSdYO-k0cKUbAWCAMzjmoGwIaPAw"}, {"type": "rouge", "value": 16.5689, "name": "ROUGE-2", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWQwMmM2NjJjNzM1N2Y3NjZmMmE5NzNlNjRjNjEwNzNhNjcyZTRiMGRlODY3NWUyMGQ0YzZmMGFhODYzOTRmOSIsInZlcnNpb24iOjF9.AZZkbaYBZG6rw6-QHYjRlSl-p0gBT2EtJxwjIP7QYH5XIQjeoiQsTnDPIq25dSMDbmQLSZnpHC104ZctX0f_Dg"}, {"type": "rouge", "value": 30.1285, "name": "ROUGE-L", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTRjYThlMTllZjI4MGFiMDZhZTVkYmRjMTNhZDUzNTQ0OWQyNDQxMmQ5ODJiMmJiNGI3OTAzYjhiMzc2MTI4NCIsInZlcnNpb24iOjF9.zTHd3F4ZlgS-azl-ZVjOckcTrtrJmDOGWVaC3qQsvvn2UW9TnseNkmo7KBc3DJU7_NmlxWZArl1BdSetED0NCg"}, {"type": "rouge", "value": 30.1706, "name": "ROUGE-LSUM", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGMzZGFjNzVkYWI0NTJkMmZjZDQ0YjhiYjIxN2VkNmJjMTgwZTk1NjFlOGU2NjNjM2VjYTNlYTBhNTQ5MGZkNSIsInZlcnNpb24iOjF9.xQ2LoI3PwlEiXo1OT2o4Pq9o2thYCd9lSCKCWlLmZdxI5GxdsjcASBKmHKopzUcwCGBPR7zF95MHSAPyszOODA"}, {"type": "loss", "value": 2.7092134952545166, "name": "loss", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzQzODE0NDc5YTYzYjJlMWU2YTVjOGRjN2JmYWVkOWNkNTRlMTZlOWIyN2NiODJkMDljMjI3YzZmYzM3N2JjYSIsInZlcnNpb24iOjF9.Vv_pdeFuRMoKK3cPr5P6n7D6_18ChJX-2qcT0y4is3XX3mS98fk3U1AYEuy9nBHOwYR3o0U8WBgQ-Ya_FqefBg"}, {"type": "gen_len", "value": 15.1414, "name": "gen_len", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjk5OTk3NWRiNjZlZmQzMmYwOTU2MmQwOWE1MDNlNTg3YWVkOTgwOTc2ZTQ0MTBiZjliOWMyZTYwMDI2MDUzYiIsInZlcnNpb24iOjF9.Zvj84JzIhM50rWTQ2GrEeOU7HrS8KsILH-8ApTcSWSI6kVnucY0MyW2ODxvRAa_zHeCygFW6Q13TFGrT5kLNAA"}]}]}]}
|
human-centered-summarization/financial-summarization-pegasus
| null |
[
"transformers",
"pytorch",
"tf",
"safetensors",
"pegasus",
"text2text-generation",
"summarization",
"en",
"dataset:xsum",
"arxiv:1912.08777",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[
"1912.08777"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #safetensors #pegasus #text2text-generation #summarization #en #dataset-xsum #arxiv-1912.08777 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
### PEGASUS for Financial Summarization
This model was fine-tuned on a novel financial news dataset, which consists of 2K articles from Bloomberg, on topics such as stock, markets, currencies, rate and cryptocurrencies.
It is based on the PEGASUS model and in particular PEGASUS fine-tuned on the Extreme Summarization (XSum) dataset: google/pegasus-xsum model. PEGASUS was originally proposed by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu in PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization.
### How to use
We provide a simple snippet of how to use this model for the task of financial summarization in PyTorch.
Evaluation Results
------------------
The results before and after the fine-tuning on our dataset are shown below:
You can find more details about this work in the following workshop paper. If you use our model in your research, please consider citing our paper:
>
> T. Passali, A. Gidiotis, E. Chatzikyriakidis and G. Tsoumakas. 2021.
> Towards Human-Centered Summarization: A Case Study on Financial News.
> In Proceedings of the First Workshop on Bridging Human-Computer Interaction and Natural Language Processing(pp. 21–27). Association for Computational Linguistics.
>
>
>
BibTeX entry:
Support
-------
Contact us at info@URL if you are interested in a more sophisticated version of the model, trained on more articles and adapted to your needs!
More information about Medoid AI:
* Website: URL
* LinkedIn: URL
|
[
"### PEGASUS for Financial Summarization\n\n\nThis model was fine-tuned on a novel financial news dataset, which consists of 2K articles from Bloomberg, on topics such as stock, markets, currencies, rate and cryptocurrencies.\n\n\nIt is based on the PEGASUS model and in particular PEGASUS fine-tuned on the Extreme Summarization (XSum) dataset: google/pegasus-xsum model. PEGASUS was originally proposed by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu in PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization.",
"### How to use\n\n\nWe provide a simple snippet of how to use this model for the task of financial summarization in PyTorch.\n\n\nEvaluation Results\n------------------\n\n\nThe results before and after the fine-tuning on our dataset are shown below:\n\n\n\nYou can find more details about this work in the following workshop paper. If you use our model in your research, please consider citing our paper:\n\n\n\n> \n> T. Passali, A. Gidiotis, E. Chatzikyriakidis and G. Tsoumakas. 2021.\n> Towards Human-Centered Summarization: A Case Study on Financial News.\n> In Proceedings of the First Workshop on Bridging Human-Computer Interaction and Natural Language Processing(pp. 21–27). Association for Computational Linguistics.\n> \n> \n> \n\n\nBibTeX entry:\n\n\nSupport\n-------\n\n\nContact us at info@URL if you are interested in a more sophisticated version of the model, trained on more articles and adapted to your needs!\n\n\nMore information about Medoid AI:\n\n\n* Website: URL\n* LinkedIn: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #safetensors #pegasus #text2text-generation #summarization #en #dataset-xsum #arxiv-1912.08777 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### PEGASUS for Financial Summarization\n\n\nThis model was fine-tuned on a novel financial news dataset, which consists of 2K articles from Bloomberg, on topics such as stock, markets, currencies, rate and cryptocurrencies.\n\n\nIt is based on the PEGASUS model and in particular PEGASUS fine-tuned on the Extreme Summarization (XSum) dataset: google/pegasus-xsum model. PEGASUS was originally proposed by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu in PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization.",
"### How to use\n\n\nWe provide a simple snippet of how to use this model for the task of financial summarization in PyTorch.\n\n\nEvaluation Results\n------------------\n\n\nThe results before and after the fine-tuning on our dataset are shown below:\n\n\n\nYou can find more details about this work in the following workshop paper. If you use our model in your research, please consider citing our paper:\n\n\n\n> \n> T. Passali, A. Gidiotis, E. Chatzikyriakidis and G. Tsoumakas. 2021.\n> Towards Human-Centered Summarization: A Case Study on Financial News.\n> In Proceedings of the First Workshop on Bridging Human-Computer Interaction and Natural Language Processing(pp. 21–27). Association for Computational Linguistics.\n> \n> \n> \n\n\nBibTeX entry:\n\n\nSupport\n-------\n\n\nContact us at info@URL if you are interested in a more sophisticated version of the model, trained on more articles and adapted to your needs!\n\n\nMore information about Medoid AI:\n\n\n* Website: URL\n* LinkedIn: URL"
] |
question-answering
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3
This model is a fine-tuned version of [dbmdz/bert-base-turkish-128k-cased](https://huggingface.co/dbmdz/bert-base-turkish-128k-cased) on the turkish squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4724
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3911 | 1.0 | 1281 | 1.4900 |
| 0.9058 | 2.0 | 2562 | 1.3471 |
| 0.6747 | 3.0 | 3843 | 1.4724 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3", "results": []}]}
|
husnu/bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us
|
bert-base-turkish-128k-cased-finetuned\_lr-2e-05\_epochs-3
==========================================================
This model is a fine-tuned version of dbmdz/bert-base-turkish-128k-cased on the turkish squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4724
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
question-answering
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3TQUAD2-finetuned_lr-2e-05_epochs-1
This model is a fine-tuned version of [husnu/bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3](https://huggingface.co/husnu/bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4196
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5885 | 1.0 | 2245 | 1.4196 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3TQUAD2-finetuned_lr-2e-05_epochs-1", "results": []}]}
|
husnu/bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3TQUAD2-finetuned_lr-2e-05_epochs-1
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us
|
bert-base-turkish-128k-cased-finetuned\_lr-2e-05\_epochs-3TQUAD2-finetuned\_lr-2e-05\_epochs-1
==============================================================================================
This model is a fine-tuned version of husnu/bert-base-turkish-128k-cased-finetuned\_lr-2e-05\_epochs-3 on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4196
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
question-answering
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3TQUAD2-finetuned_lr-2e-05_epochs-3
This model is a fine-tuned version of [husnu/bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3](https://huggingface.co/husnu/bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3) on the turkish squad2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9011
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6404 | 1.0 | 2245 | 1.4524 |
| 0.403 | 2.0 | 4490 | 1.5638 |
| 0.2355 | 3.0 | 6735 | 1.9011 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["turkish squad v2"], "model-index": [{"name": "bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3TQUAD2-finetuned_lr-2e-05_epochs-3", "results": []}]}
|
husnu/bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3TQUAD2-finetuned_lr-2e-05_epochs-3
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
|
bert-base-turkish-128k-cased-finetuned\_lr-2e-05\_epochs-3TQUAD2-finetuned\_lr-2e-05\_epochs-3
==============================================================================================
This model is a fine-tuned version of husnu/bert-base-turkish-128k-cased-finetuned\_lr-2e-05\_epochs-3 on the turkish squad2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9011
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
question-answering
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electra-small-turkish-uncased-discriminator-finetuned_lr-2e-05_epochs-3
This model is a fine-tuned version of [loodos/electra-small-turkish-uncased-discriminator](https://huggingface.co/loodos/electra-small-turkish-uncased-discriminator) on the Turkish squad dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1669
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.5305 | 1.0 | 5818 | 2.4024 |
| 2.3264 | 2.0 | 11636 | 2.2298 |
| 2.1762 | 3.0 | 17454 | 2.1669 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "electra-small-turkish-uncased-discriminator-finetuned_lr-2e-05_epochs-3", "results": []}]}
|
husnu/electra-small-turkish-uncased-discriminator-finetuned_lr-2e-05_epochs-3
| null |
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us
|
electra-small-turkish-uncased-discriminator-finetuned\_lr-2e-05\_epochs-3
=========================================================================
This model is a fine-tuned version of loodos/electra-small-turkish-uncased-discriminator on the Turkish squad dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1669
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
question-answering
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electra-small-turkish-uncased-discriminator-finetuned_lr-2e-05_epochs-6
This model is a fine-tuned version of [loodos/electra-small-turkish-uncased-discriminator](https://huggingface.co/loodos/electra-small-turkish-uncased-discriminator) on the turkish squad dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0379
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.128 | 1.0 | 722 | 2.7187 |
| 3.0376 | 2.0 | 1444 | 2.4486 |
| 2.5304 | 3.0 | 2166 | 2.3485 |
| 2.4214 | 4.0 | 2888 | 2.0450 |
| 2.1568 | 5.0 | 3610 | 2.0576 |
| 2.0752 | 6.0 | 4332 | 2.0379 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["tsquad"], "model-index": [{"name": "electra-small-turkish-uncased-discriminator-finetuned_lr-2e-05_epochs-6", "results": []}]}
|
husnu/electra-small-turkish-uncased-discriminator-finetuned_lr-2e-05_epochs-6
| null |
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:tsquad",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-tsquad #endpoints_compatible #region-us
|
electra-small-turkish-uncased-discriminator-finetuned\_lr-2e-05\_epochs-6
=========================================================================
This model is a fine-tuned version of loodos/electra-small-turkish-uncased-discriminator on the turkish squad dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0379
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-tsquad #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
question-answering
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
This model is a fine-tuned version of [loodos/electra-small-turkish-uncased-discriminator](https://huggingface.co/loodos/electra-small-turkish-uncased-discriminator) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 5.9506
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.2
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.951 | 1.0 | 5818 | 5.9506 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "ft_electra-small-turkish-uncased-discriminator_lr-2e-1_epochs-1", "results": []}]}
|
husnu/electra-small-turkish-uncased-discriminator
| null |
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us
|
This model is a fine-tuned version of loodos/electra-small-turkish-uncased-discriminator on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 5.9506
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.2
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.2\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.2\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
question-answering
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-3
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the turkish squad dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6510
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.0113 | 1.0 | 1050 | 2.7529 |
| 2.838 | 2.0 | 2100 | 2.6510 |
| 2.8168 | 3.0 | 3150 | 2.6510 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-3", "results": []}]}
|
husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-3
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
|
xtremedistil-l6-h256-uncased-TQUAD-finetuned\_lr-2e-05\_epochs-3
================================================================
This model is a fine-tuned version of microsoft/xtremedistil-l6-h256-uncased on the turkish squad dataset.
It achieves the following results on the evaluation set:
* Loss: 2.6510
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
question-answering
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the Turkish squad dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8135
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 350 | 3.8389 |
| 4.4474 | 2.0 | 700 | 3.3748 |
| 3.512 | 3.0 | 1050 | 3.0657 |
| 3.512 | 4.0 | 1400 | 2.9219 |
| 3.1526 | 5.0 | 1750 | 2.8517 |
| 2.9972 | 6.0 | 2100 | 2.8135 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6", "results": []}]}
|
husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-6
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
|
xtremedistil-l6-h256-uncased-TQUAD-finetuned\_lr-2e-05\_epochs-6
================================================================
This model is a fine-tuned version of microsoft/xtremedistil-l6-h256-uncased on the Turkish squad dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8135
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 48
* eval\_batch\_size: 48
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
question-answering
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-9
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the Turkish squad dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2340
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.5236 | 1.0 | 1050 | 3.0042 |
| 2.8489 | 2.0 | 2100 | 2.5866 |
| 2.5485 | 3.0 | 3150 | 2.3526 |
| 2.4067 | 4.0 | 4200 | 2.3535 |
| 2.3091 | 5.0 | 5250 | 2.2862 |
| 2.2401 | 6.0 | 6300 | 2.3989 |
| 2.1715 | 7.0 | 7350 | 2.2284 |
| 2.1414 | 8.0 | 8400 | 2.2298 |
| 2.1221 | 9.0 | 9450 | 2.2340 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-9", "results": []}]}
|
husnu/xtremedistil-l6-h256-uncased-TQUAD-finetuned_lr-2e-05_epochs-9
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
|
xtremedistil-l6-h256-uncased-TQUAD-finetuned\_lr-2e-05\_epochs-9
================================================================
This model is a fine-tuned version of microsoft/xtremedistil-l6-h256-uncased on the Turkish squad dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2340
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 9
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 9",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 9",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
question-answering
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xtremedistil-l6-h256-uncased-finetuned_lr-2e-05_epochs-3
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2864
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.6088 | 1.0 | 5533 | 1.4429 |
| 1.3928 | 2.0 | 11066 | 1.3183 |
| 1.3059 | 3.0 | 16599 | 1.2864 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "xtremedistil-l6-h256-uncased-finetuned_lr-2e-05_epochs-3", "results": []}]}
|
husnu/xtremedistil-l6-h256-uncased-finetuned_lr-2e-05_epochs-3
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
|
xtremedistil-l6-h256-uncased-finetuned\_lr-2e-05\_epochs-3
==========================================================
This model is a fine-tuned version of microsoft/xtremedistil-l6-h256-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2864
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
question-answering
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xtremedistil-l6-h256-uncased-finetuned_lr-2e-05_epochs-6
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2578
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.3828 | 1.0 | 1845 | 1.7946 |
| 1.5827 | 2.0 | 3690 | 1.4123 |
| 1.404 | 3.0 | 5535 | 1.3142 |
| 1.346 | 4.0 | 7380 | 1.2819 |
| 1.2871 | 5.0 | 9225 | 1.2630 |
| 1.2538 | 6.0 | 11070 | 1.2578 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "xtremedistil-l6-h256-uncased-finetuned_lr-2e-05_epochs-6", "results": []}]}
|
husnu/xtremedistil-l6-h256-uncased-finetuned_lr-2e-05_epochs-6
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
|
xtremedistil-l6-h256-uncased-finetuned\_lr-2e-05\_epochs-6
==========================================================
This model is a fine-tuned version of microsoft/xtremedistil-l6-h256-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2578
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 48
* eval\_batch\_size: 48
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
token-classification
|
spacy
|
Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morphologizer, lemmatizer, parser, ner
| Feature | Description |
| --- | --- |
| **Name** | `hu_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `trainable_lemmatizer`, `parser`, `ner` |
| **Components** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `trainable_lemmatizer`, `parser`, `ner` |
| **Vectors** | -1 keys, 200000 unique vectors (300 dimensions) |
| **Sources** | [UD Hungarian Szeged](https://universaldependencies.org/treebanks/hu_szeged/index.html) (Richárd Farkas, Katalin Simkó, Zsolt Szántó, Viktor Varga, Veronika Vincze (MTA-SZTE Research Group on Artificial Intelligence))<br>[NYTK-NerKor Corpus](https://github.com/nytud/NYTK-NerKor) (Eszter Simon, Noémi Vadász (Department of Language Technology and Applied Linguistics))<br>[Szeged NER Corpus](https://rgai.inf.u-szeged.hu/node/130) (György Szarvas, Richárd Farkas, László Felföldi, András Kocsor, János Csirik (MTA-SZTE Research Group on Artificial Intelligence))<br>[Hungarian lg Floret vectors](https://huggingface.co/huspacy/hu_vectors_web_lg) (Szeged AI) |
| **License** | `cc-by-sa-4.0` |
| **Author** | [SzegedAI, MILAB](https://github.com/huspacy/huspacy) |
### Label Scheme
<details>
<summary>View label scheme (1209 labels for 4 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `ADJ`, `ADP`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `SYM`, `VERB`, `X` |
| **`morphologizer`** | `Definite=Def\|POS=DET\|PronType=Art`, `Case=Ine\|Number=Sing\|POS=NOUN`, `POS=ADV`, `Case=Nom\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Nom\|Number=Sing\|POS=ADJ\|VerbForm=PartPres`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=NOUN`, `Definite=Ind\|POS=DET\|PronType=Tot`, `Case=Ade\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `POS=PUNCT`, `Case=Nom\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Definite=Ind\|POS=DET\|PronType=Ind`, `Definite=Def\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=ADP`, `POS=CCONJ`, `Case=Del\|Number=Sing\|POS=NOUN`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Sbl\|Number=Sing\|POS=NOUN`, `Case=Nom\|Number=Sing\|POS=ADJ\|VerbForm=PartPast`, `Case=Del\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Nom\|Number=Sing\|POS=PROPN`, `Definite=Ind\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Number=Sing\|POS=NOUN`, `Case=Sup\|Number=Sing\|POS=PROPN`, `Case=Ess\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Ine\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Sup\|Number=Plur\|POS=NOUN`, `Degree=Pos\|POS=ADV`, `Case=Sup\|Number=Sing\|POS=NOUN`, `Definite=Ind\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Cau\|Number=Plur\|POS=NOUN`, `Case=Cau\|Number=Sing\|POS=NOUN`, `Case=Gen\|Number=Sing\|POS=NOUN`, `Definite=Ind\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Definite=Def\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Tra\|Number=Sing\|POS=ADJ\|VerbForm=PartPres`, `Case=Nom\|Number=Plur\|POS=NOUN`, `Case=Cau\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Def\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ins\|Number=Sing\|POS=NOUN`, `POS=ADV\|PronType=Neg`, `Case=Ine\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `POS=SCONJ`, `Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Definite=Def\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|NumType=Frac\|Number=Sing\|POS=NUM`, `Case=Sbl\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Abl\|Number=Sing\|POS=NOUN`, `Case=Dat\|Number=Sing\|POS=NOUN`, `Definite=Ind\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|Voice=Act`, `POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Definite=Ind\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Degree=Sup\|Number=Sing\|POS=ADJ`, `POS=ADV\|PronType=Dem`, `Case=Ins\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Ade\|Degree=Pos\|Number=Sing\|POS=ADJ`, `POS=ADV\|PronType=Int`, `Case=Tra\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Sbl\|Number=Sing\|POS=PROPN`, `Case=Sbl\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=All\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Definite=Ind\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=PART`, `Case=Sup\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `POS=ADV\|PronType=Tot`, `Case=Ill\|Definite=Ind\|POS=DET\|PronType=Ind`, `Number=Sing\|POS=VERB\|Person=3\|VerbForm=Inf\|Voice=Act`, `Case=Ill\|Number=Sing\|POS=NOUN`, `Definite=Ind\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Sbl\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Definite=Def\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|Voice=Act`, `Definite=Ind\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Definite=Def\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Sup\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Definite=Ind\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Ade\|Number=Sing\|POS=ADJ\|VerbForm=PartPast`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Ess\|Number=Sing\|POS=ADJ\|VerbForm=PartPres`, `Case=Acc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Number=Sing\|POS=ADJ\|VerbForm=PartFut`, `Case=Ine\|NumType=Card\|Number=Sing\|POS=NUM`, `Definite=Ind\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Number=Plur\|POS=NOUN`, `Case=Del\|Number=Plur\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Tra\|Number=Sing\|POS=NOUN`, `Case=Sup\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Definite=Ind\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Definite=Ind\|POS=DET\|PronType=Art`, `Case=Dat\|Number=Plur\|POS=NOUN`, `Case=Ins\|Number=Plur\|POS=NOUN`, `Case=Sbl\|Number=Plur\|POS=NOUN`, `Case=Ela\|Number=Sing\|POS=NOUN`, `Definite=Ind\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=All\|Number=Sing\|POS=NOUN`, `Case=Ine\|Number=Plur\|POS=NOUN`, `Case=Dat\|Number=Plur\|POS=ADJ\|VerbForm=PartPres`, `Case=Ela\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Abl\|Number=Sing\|POS=PROPN`, `Case=Cau\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Number=Sing\|POS=PROPN`, `Case=Ess\|Number=Sing\|POS=ADJ\|VerbForm=PartPast`, `Number=Plur\|POS=VERB\|Person=3\|VerbForm=Inf\|Voice=Act`, `Case=Sbl\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=All\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Abl\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Definite=Def\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=ADJ`, `POS=ADV\|PronType=Rel`, `Definite=Def\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Cau`, `Case=Del\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Gen\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Ill\|Number=Plur\|POS=NOUN`, `Case=Ela\|Number=Plur\|POS=NOUN`, `Case=Ill\|Number=Sing\|POS=PROPN`, `Case=Ela\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Definite=Def\|POS=DET\|PronType=Ind`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Definite=Ind\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rcp`, `Case=Ine\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=All\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ter\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `POS=ADV\|VerbForm=Conv`, `Definite=Def\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Sup\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Aspect=Iter\|Definite=Def\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Iter\|Definite=Def\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Dis\|Number=Sing\|POS=NOUN`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Ade\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=All\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=ADJ\|VerbForm=PartPast`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Nom\|Number=Plur\|POS=PROPN`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Cau\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Dat\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Ine\|Number=Sing\|POS=PROPN`, `Definite=Def\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Cau`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Definite=Ind\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Cau`, `Case=Abs\|Number=Sing\|POS=NOUN`, `Case=Ade\|Number=Sing\|POS=PROPN`, `Case=Ins\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Sup\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Sbl\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Number=Sing\|POS=PROPN`, `Case=Del\|Number=Sing\|POS=PROPN`, `Case=Sbl\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Loc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|POS=DET\|PronType=Ind`, `Case=Ill\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rcp`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Definite=Ind\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Del\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Definite=Ind\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|Voice=Act`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Ter\|Number=Sing\|POS=NOUN`, `Case=Ela\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `POS=X`, `Definite=Def\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Definite=Ind\|Mood=Imp\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Del\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Neg`, `Case=Tra\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Degree=Pos\|POS=ADV\|PronType=Dem`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|Reflex=Yes`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Del\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Definite=Ind\|Mood=Cnd,Pot\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ade\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Ill\|Number=Sing\|POS=PRON\|Person=3\|PronType=Neg`, `Definite=Def\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Iter\|Definite=Def\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Ine\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Definite=Def\|Mood=Cnd,Pot\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ine\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Sbl\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Definite=Ind\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=All\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ess\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Dat\|Number=Sing\|POS=PROPN`, `Case=Nom\|Number=Sing\|Number[psed]=Sing\|POS=NOUN`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=ADJ\|VerbForm=PartPres`, `Case=Sbl\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Ess\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3\|VerbForm=PartPast`, `Definite=Def\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Cau`, `Definite=Ind\|POS=DET\|PronType=Neg`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ter\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Sup\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Def\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Del\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Definite=Def\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=3`, `Case=Nom\|Number=Plur\|POS=ADJ\|VerbForm=PartPast`, `Case=Cau\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Definite=Ind\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Cau`, `Case=Acc\|Number=Sing\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Number=Plur\|POS=ADJ\|VerbForm=PartPast`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Cau\|Number=Sing\|POS=PROPN`, `Case=Abs\|Number=Sing\|POS=ADJ\|VerbForm=PartPres`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rcp`, `Case=Ine\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Definite=Def\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Cau`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Ess\|Number=Sing\|POS=NOUN`, `Case=Ter\|Number=Plur\|POS=NOUN`, `Case=Tem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=INTJ`, `Case=Ine\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Number=Plur\|POS=VERB\|Person=1\|VerbForm=Inf\|Voice=Act`, `Case=Sbl\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Definite=Ind\|Mood=Pot\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=All\|Number=Sing\|POS=PROPN`, `Case=Ter\|Number=Sing\|POS=PROPN`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Definite=Ind\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Sbl\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Ind`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Definite=Ind\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|Voice=Act`, `Case=Abl\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Sup\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Ela\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Neg`, `Case=Sbl\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Ind\|Mood=Imp\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Cau`, `Definite=Ind\|Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Definite=Ind\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Sbl\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Definite=Def\|POS=DET\|PronType=Prs`, `Case=Ill\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ine\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Ill\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Del\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=3`, `Case=Acc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Definite=Def\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Definite=Ind\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Cau`, `Definite=Ind\|Mood=Imp,Pot\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Def\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Number=Sing\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Sbl\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Def\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ine\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ine\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Ind\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Cau\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ins\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Ins\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Definite=2\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Sbl\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Definite=Ind\|Mood=Pot\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=All\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=All\|Number=Plur\|POS=NOUN`, `Case=Ela\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Abs\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ine\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Ine\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `POS=AUX\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Definite=Def\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ela\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=All\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Aspect=Iter\|Definite=Ind\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=3`, `Case=Ter\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Number=Plur\|POS=NOUN`, `Case=Tem\|Number=Sing\|POS=NOUN`, `Case=Del\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Ade\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ins\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `POS=ADV\|PronType=Ind`, `Definite=Ind\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Definite=Def\|POS=DET\|PronType=Int`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Ind\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Abs\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Sing\|Number[psed]=Sing\|POS=PROPN`, `Case=Del\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ela\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc\|Number=Plur\|POS=PROPN`, `Case=Abl\|NumType=Card\|Number=Sing\|POS=NUM`, `Definite=Def\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=3`, `Case=Abs\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=3`, `Definite=Def\|Mood=Pot\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Abl\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Ela\|Number=Sing\|POS=PROPN`, `Case=Ade\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Ela\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Sbl\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Definite=Def\|Mood=Imp,Pot\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|POS=DET\|PronType=Tot`, `Definite=Def\|POS=DET\|PronType=Neg`, `Case=Ins\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Ine\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Ind`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rcp`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Sup\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Sbl\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ill\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Definite=Def\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|Voice=Act`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Case=Abl\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Definite=Ind\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Cau`, `Case=Sbl\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Tra\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Abl\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ill\|Number=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Case=Del\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Ess\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Ess\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Sup\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Acc\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=ADJ\|VerbForm=PartPres`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=ADJ\|VerbForm=PartPast`, `Case=Ess\|Degree=Pos\|Number=Sing\|POS=ADJ\|VerbForm=PartPres`, `Case=All\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Degree=Cmp\|POS=ADV`, `Definite=Def\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=All\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ela\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Cau\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Ins\|NumType=Card\|Number=Sing\|POS=NUM`, `Definite=Ind\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Number=Sing\|POS=ADJ\|VerbForm=PartFut`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=ADJ\|VerbForm=PartPast`, `Degree=Sup\|POS=ADV`, `Case=Acc\|NumType=Card\|Number=Sing\|POS=NUM`, `Definite=Def\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ine\|NumType=Frac\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Ade\|Number=Plur\|POS=NOUN`, `Case=Acc\|NumType=Frac\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Tra\|Degree=Pos\|Number=Sing\|POS=ADJ\|VerbForm=PartPres`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ\|VerbForm=PartPres`, `Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Inf\|Voice=Act`, `Case=All\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Cau\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Degree=Pos\|Number=Sing\|Number[psed]=Sing\|POS=ADJ`, `Case=Nom\|NumType=Frac\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Ela\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Definite=Def\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Cau`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Ine\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Mood=Pot\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Ela\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ade\|Number=Sing\|Number[psed]=Sing\|POS=NOUN`, `Definite=Def\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Cau`, `Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Inf\|Voice=Act`, `Case=Ela\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Sbl\|NumType=Frac\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Definite=Ind\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Cau`, `Case=Ade\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=ADJ\|VerbForm=PartPres`, `Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Inf\|Voice=Act`, `Case=Ine\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Number=Plur\|POS=ADV\|Person=1\|PronType=PrsPron`, `POS=ADV\|PronType=v`, `Definite=Ind\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Abl\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Number=Sing\|POS=ADV\|Person=3\|PronType=PrsPron`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|NumType[sem]=Time\|Number=Sing\|POS=NUM`, `Case=Tem\|NumType[sem]=Time\|Number=Sing\|POS=NUM`, `Case=Abl\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Number=Sing\|POS=ADV\|Person=1\|PronType=PrsPron`, `Case=Ter\|NumType[sem]=Time\|Number=Sing\|POS=NUM`, `Case=Ill\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Number=Sing\|POS=VERB\|Person=1\|VerbForm=Inf\|Voice=Act`, `Case=Ine\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1\|Poss=Yes\|PronType=Prs`, `Case=Ine\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=ADV\|Person=3\|PronType=PrsPron`, `Case=Ins\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Ela\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ter\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Case=Sbl\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Cas=6\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Ela\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Sup\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Tot`, `Case=Ade\|Number=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Definite=Ind\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Sup\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Sbl\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Del\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Abl\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Nom\|NumType=Dist\|Number=Sing\|POS=NUM`, `Case=Sup\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Sbl\|Number=Sing\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Sbl\|Number=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Definite=Ind\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Sbl\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|Voice=Act`, `Definite=Def\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|Voice=Act`, `Case=Acc\|NumType=Card\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person[psor]=1`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1\|Poss=Yes\|PronType=Prs`, `Case=Abl\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Plur\|POS=ADV\|Person=2\|PronType=PrsPron`, `Case=Ine\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Definite=Ind\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|Voice=Act`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=All\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Dat\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ill\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Sbl\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Definite=Ind\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Sup\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Gen\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rcp`, `Case=Del\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Del\|Number=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=1\|Poss=Yes\|PronType=Prs`, `Case=Ela\|Number=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Case=Nom\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Definite=Ind\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=2`, `Case=All\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Abl\|Number=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Case=All\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Abl\|Number=Plur\|POS=NOUN`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ade\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Cas=6\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ess\|Number=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Case=Abl\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rcp`, `Case=Sbl\|NumType[sem]=Time\|Number=Sing\|POS=NUM`, `Case=All\|Number=Plur\|POS=PROPN`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=1\|PronType=Ind`, `Case=Ine\|Number=Sing\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Acc\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Ine\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=3`, `Case=Ade\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Definite=Def\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Ins\|Number=Plur\|POS=PROPN`, `Case=Nom\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Ela\|Number=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Ine\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Sup\|Number=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Definite=Def\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=All\|Number=Sing\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Nom\|NumType[sem]=Dot\|Number=Sing\|POS=NUM`, `Case=Sup\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Degree=Pos\|POS=ADV\|PronType=Ind`, `Case=Ela\|Number=Sing\|Number[psed]=Sing\|POS=NOUN`, `Definite=Def\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|Voice=Act`, `Case=Ade\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Sup\|NumType[sem]=Time\|Number=Sing\|POS=NUM`, `Case=Gen\|Number=Plur\|POS=PROPN`, `Case=All\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rcp`, `Case=Ins\|Number=Sing\|Number[psed]=Sing\|POS=NOUN`, `Case=Ill\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Sbl\|Number=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Case=Del\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Number=Sing\|POS=ADV\|Person=2\|PronType=PrsPron`, `Case=Sbl\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Ill\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Degree=Cmp\|POS=ADV\|PronType=Dem`, `Case=Ins\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Sup\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Ins\|NumType=Card\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Ill\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Ill\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Dat\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=All\|Number=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Abl\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=All\|Number=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Definite=Def\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Del\|Number=Sing\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Ind`, `Case=All\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Tot`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Acc\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=1`, `Case=Sbl\|NumType=Frac\|Number=Sing\|POS=NUM`, `Case=Tem\|NumType=Frac\|Number=Sing\|POS=NUM`, `Case=Tem\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Nom\|NumType[sem]=Result\|Number=Sing\|POS=NUM`, `Case=Del\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rcp`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Acc\|Number=Plur\|Number[psed]=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Cau\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Ade\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Ine\|Number=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=1\|PronType=Tot`, `Case=Sbl\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Ela\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Definite=Ind\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Sup\|Number=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Ind`, `Case=Del\|Number=Plur\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Del\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Dat\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Definite=2\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Com\|Number=Sing\|POS=NOUN`, `Case=Tra\|Number=Plur\|POS=NOUN`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Tot`, `Case=Ade\|Number=Plur\|POS=PROPN`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Ess\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Dat\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Definite=Def\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Sbl\|NumType[sem]=Quotient\|Number=Sing\|POS=NUM`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int`, `Case=Del\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Del\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Sup\|Number=Sing\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Ade\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ins\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Ess\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Del\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Cau\|Number=Plur\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Sbl\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rcp`, `Case=Tem\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Ill\|NumType=Frac\|Number=Sing\|POS=NUM`, `Case=Ill\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Degree=Pos\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person[psor]=1`, `Case=Del\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Del\|Number=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Case=Gen\|NumType=Card\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person[psor]=1`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Ind`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Ela\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Ins\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Dat\|NumType=Card\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person[psor]=1`, `Case=Sbl\|Number=Plur\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Ill\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Acc\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Cau\|Number=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=2`, `Definite=Def\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Sbl\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Sup\|Number=Plur\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Tem\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Ins\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Abl\|Number=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Tra\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Abs\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=All\|Number=Plur\|POS=PRON\|Person=1\|PronType=Tot`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=1\|PronType=Ind`, `Case=Ine\|Number=Plur\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Sup\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Definite=Def\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=All\|Number=Plur\|POS=PRON\|Person=1\|Reflexive=Yes`, `Cas=1\|Number=Sing\|POS=PROPN`, `Definite=Ind\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|Voice=Act`, `Case=Ela\|Number=Sing\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Sbl\|Number=Sing\|POS=PRON\|Person=2\|Reflexive=Yes`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|Reflexive=Yes`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=1`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Sbl\|NumType[sem]=Result\|Number=Sing\|POS=NUM`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=2`, `Case=Nom\|Number=Sing\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Nom\|Number=Sing\|Number[psed]=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Sbl\|Number=Sing\|Number[psed]=Sing\|POS=NOUN`, `Case=Nom\|Number=Sing\|Number[psed]=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Definite=Ind\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|Voice=Act`, `Case=Ill\|Number=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Ine\|Number=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=3`, `Case=Ela\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat\|NumType=Card\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person[psor]=3`, `Case=Ine\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rcp`, `Case=Nom\|Number=Sing\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Tot`, `Definite=Ind\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Cau\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Acc\|Number=Sing\|Number[psed]=Sing\|POS=PROPN`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1\|PronType=Tot`, `Case=Abl\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Tra\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Cau\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Sup\|Number=Plur\|POS=PROPN`, `Case=Ess\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Definite=Def\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dis\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Ill\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=3`, `Case=All\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Tot`, `Case=Nom\|NumType=Card\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person[psor]=1`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|Reflexive=Yes`, `Cas=6\|Number=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int`, `Case=Sup\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Sbl\|Number=Sing\|Number[psed]=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Sup\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Abl\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Abs\|Number=Plur\|POS=NOUN`, `Case=Sup\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=1\|PronType=Tot`, `Case=Ine\|Number=Sing\|Number[psed]=Sing\|POS=NOUN`, `Case=Tra\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Sbl\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Ins\|Degree=Cmp\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Ade\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Sbl\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=All\|Number=Sing\|Number[psed]=Sing\|POS=NOUN`, `Case=Abl\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=PROPN\|Person[psor]=1`, `Case=Nom\|Number=Sing\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Del\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Del\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=All\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Ade\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Ter\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|NumType=Card\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person[psor]=3`, `Case=Abl\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|Reflexive=Yes`, `Case=Dat\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=3\|PronType=Tot`, `Case=Sup\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Ela\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ine\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc\|Number=Sing\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Sbl\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Definite=Ind\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ill\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|NumType=Frac\|Number=Sing\|POS=NUM`, `Case=Cau\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Gen\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ade\|Number=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=All\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ill\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=1\|Poss=Yes\|PronType=Prs`, `Definite=2\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Cas=6\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Cau\|Number=Plur\|POS=PRON\|Person=3\|PronType=Tot`, `Case=Abl\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Abs\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Acc\|Number=Plur\|Number[psed]=Sing\|POS=NOUN`, `Case=Nom\|NumType=Card\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Cau\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=All\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Sup\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rcp`, `Case=Ade\|Number=Plur\|Number[psed]=Sing\|POS=NOUN`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Ill\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Del\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|Number[psed]=Sing\|POS=NOUN`, `Case=Cau\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Cau\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rcp`, `Number=Sing\|POS=VERB\|Person=2\|VerbForm=Inf\|Voice=Act`, `Case=Ine\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Cau\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Ine\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=1\|Poss=Yes\|PronType=Prs`, `Case=Ela\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Sup\|Number=Plur\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Cau\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Sbl\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Ter\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Tra\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Cau\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Ind\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Tot`, `Case=Acc\|Number=Sing\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Dat\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Dat\|Number=Sing\|Number[psed]=Sing\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Abl\|Number=Plur\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Tem\|Number=Plur\|POS=NOUN`, `Case=Abs\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Sbl\|Number=Plur\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Ins\|Number=Sing\|Number[psed]=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=All\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Abl\|Number=Sing\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Acc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=All\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Plur\|Number[psed]=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Ind`, `Case=Ill\|Number=Sing\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Ade\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ade\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=2\|PronType=Tot`, `Case=Abl\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Cau\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Del\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Cau\|Number=Plur\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Ill\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Ade\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ine\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Nom\|Number=Sing\|Number[psed]=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Tot`, `Case=Ade\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|Reflexive=Yes`, `Case=Ins\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=All\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ess\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Cau\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Cas=6\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Tra\|NumType=Card\|Number=Sing\|POS=NUM`, `Number=Plur\|POS=VERB\|Person=2\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Degree=Pos\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Cas=6\|Number=Sing\|POS=NOUN`, `Case=Ins\|Number=Sing\|Number[psed]=Sing\|POS=PROPN`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Ind`, `Case=Ela\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Sup\|NumType=Card\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person[psor]=3`, `Case=Abl\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=1\|PronType=Int`, `Case=Ill\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=2`, `Case=Del\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Tra\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Sbl\|NumType=Card\|Number=Plur\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Acc\|NumType=Card\|Number=Plur\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Int`, `Case=Nom\|Number=Plur\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Ine\|Number=Sing\|POS=PRON\|Person=2\|Reflexive=Yes`, `Case=Abl\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Gen\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Gen\|NumType=Card\|Number=Plur\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|NumType=Card\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person[psor]=3`, `Case=All\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Acc\|NumType=Card\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person[psor]=3`, `Case=Tra\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Sbl\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Ins\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=2`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1\|PronType=Dem`, `Case=Nom\|Degree=Cmp\|Number=Plur\|Number[psed]=Sing\|POS=ADJ`, `Case=Acc\|Number=Sing\|Number[psed]=Sing\|POS=NOUN`, `Case=Cau\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=2\|PronType=Ind`, `Case=All\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=All\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Tem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ill\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Abl\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Cau\|Number=Plur\|POS=PROPN`, `Case=Ade\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Ade\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Ind`, `Case=All\|Number=Plur\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Del\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Definite=Ind\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|Voice=Act`, `Definite=Def\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|Voice=Act`, `Case=Sup\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=2`, `Case=Tra\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Sup\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Ind`, `Case=Nom\|NumType=Card\|Number=Plur\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Sbl\|Number=Sing\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Nom\|Number=Plur\|Number[psed]=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=All\|Number=Plur\|Number[psed]=Sing\|POS=NOUN`, `Case=Nom\|Number=Sing\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ill\|Number=Plur\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Ela\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Ind`, `Case=Ela\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Del\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Sup\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Tra\|Number=Plur\|Number[psed]=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Ter\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Nom\|Number=Sing\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Tot`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Ind`, `Case=All\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1\|Poss=Yes\|PronType=Prs`, `Case=Ter\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=All\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|Voice=Act`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Cau\|Number=Plur\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Ins\|Number=Plur\|Number[psed]=Sing\|POS=NOUN`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|Poss=Yes\|PronType=Prs`, `Case=Del\|Number=Plur\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Tem\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Del\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Sup\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Ter\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Sup\|Number=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Ine\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Abs\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=All\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=1\|Poss=Yes\|PronType=Prs`, `Case=Sup\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Cau\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Sup\|NumType=Ord\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Sup\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=3`, `Case=Abl\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Int`, `Case=Ela\|Number=Plur\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Dat\|NumType=Ord\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Ill\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=All\|Number=Sing\|Number[psed]=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=1`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=1`, `Case=Ine\|NumType=Card\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person[psor]=1`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=1`, `Case=Ela\|Number=Sing\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Case=Ade\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Ill\|Number=Sing\|Number[psor]=Plur\|POS=PROPN\|Person[psor]=1`, `Case=Ade\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1\|Poss=Yes\|PronType=Prs`, `Case=All\|Number=Sing\|Number[psed]=Sing\|POS=PROPN`, `Case=Acc\|Degree=Pos\|Number=Plur\|Number[psor]=Plur\|POS=ADJ\|Person[psor]=3`, `Case=Dat\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Nom\|Number=Sing\|POS=SYM\|Type=w`, `Case=Gen\|Number=Sing\|POS=SYM\|Type=w`, `Case=Abl\|Number=Sing\|POS=SYM\|Type=w`, `Case=Acc\|Number=Sing\|POS=SYM\|Type=w`, `Case=Ade\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=All\|Number=Sing\|POS=SYM\|Type=w`, `Case=Tra\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Ins\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Abl\|Number=Sing\|Number[psed]=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Number=Plur\|Number[psed]=Sing\|POS=ADJ`, `Case=Sup\|Number=Sing\|Number[psed]=Sing\|POS=NOUN`, `Case=Sup\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Nom\|NumType[sem]=Quotient\|Number=Sing\|POS=NUM`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=PROPN\|Person[psor]=1`, `Case=Ins\|Number=Sing\|Number[psed]=Plur\|POS=NOUN`, `Case=Gen\|Number=Sing\|Number[psed]=Sing\|POS=NOUN`, `Case=Ine\|Degree=Pos\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person[psor]=3`, `Case=Abs\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ela\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Dat\|Number=Sing\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Number=Plur\|Number[psed]=Sing\|POS=NOUN`, `Case=Nom\|Number=Sing\|POS=SYM\|Type=o`, `Case=Gen\|NumType=Frac\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Sup\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|NumType[sem]=Signed\|Number=Sing\|POS=NUM`, `Case=Com\|Number=Sing\|POS=PROPN`, `Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Tot`, `Case=Ins\|Number=Sing\|Number[psed]=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ill\|Number=Plur\|POS=PRON\|Person=1\|Reflexive=Yes`, `Case=Nom\|Number=Plur\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Ins\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Gen\|NumType=Dist\|Number=Sing\|POS=NUM`, `Case=Nom\|NumType[sem]=Formula\|Number=Sing\|POS=NUM`, `Case=Del\|Number=Sing\|POS=SYM\|Type=w`, `Case=Ade\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Rel`, `Case=Ine\|Number=Plur\|Number[psed]=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Number=Sing\|Number[psor]=Plur\|POS=ADJ\|Person[psor]=3`, `Case=Ade\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc\|Number=Sing\|POS=SYM\|Type=o`, `Case=Ins\|NumType=Frac\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Ela\|Number=Sing\|POS=SYM\|Type=o`, `Case=Dat\|Degree=Pos\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Case=All\|Number=Plur\|Number[psed]=Sing\|POS=SYM\|Type=w`, `Case=Ade\|Number=Sing\|POS=SYM\|Type=w`, `Case=Sbl\|Number=Sing\|POS=SYM\|Type=w`, `Case=Ade\|NumType=Card\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Ill\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Ind`, `Case=Ine\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=3`, `Case=Acc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Ill\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Sup\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Sup\|NumType=Card\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Dat\|Number=Sing\|Number[psed]=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Ine\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Tot`, `Case=Ill\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=3`, `Case=Sup\|Number=Sing\|POS=SYM\|Type=w`, `Case=Ine\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Ins\|NumType=Frac\|Number=Sing\|POS=NUM`, `Case=Ela\|Number=Sing\|POS=SYM\|Type=w`, `Case=Sbl\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Ind`, `Case=Nom\|Number=Sing\|POS=SYM\|Type=p`, `Case=Abl\|Number=Plur\|Number[psed]=Sing\|POS=NOUN`, `Case=Nom\|NumType[sem]=Measure\|Number=Sing\|POS=NUM`, `Case=Abs\|Number=Sing\|POS=PROPN`, `Case=Ins\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Ind`, `Case=Nom\|Number=Sing\|Number[psed]=Plur\|POS=NOUN`, `Case=Nom\|Number=Sing\|POS=SYM\|Type=m`, `Case=Acc\|Number=Sing\|POS=SYM\|Type=m`, `Case=Sup\|Number=Sing\|Number[psed]=Sing\|POS=PROPN`, `Case=Ine\|Number=Plur\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Nom\|Number=Sing\|Number[psed]=Sing\|POS=SYM\|Type=o`, `Case=Ins\|Number=Sing\|POS=SYM\|Type=o`, `Case=Ins\|Number=Sing\|POS=SYM\|Type=w`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int`, `Case=Acc\|Number=Sing\|Number[psed]=Plur\|POS=NOUN`, `Case=Gen\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Sbl\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int`, `Case=Abl\|Number=Sing\|Number[psed]=Sing\|POS=PROPN`, `Case=Acc\|Degree=Pos\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Abs\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Ind`, `Case=Ill\|Number=Sing\|POS=SYM\|Type=w`, `Case=Ela\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Abl\|NumType[sem]=Time\|Number=Sing\|POS=NUM`, `Case=Gen\|Degree=Sup\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Abs\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3\|Poss=Yes\|PronType=Prs`, `Case=Sup\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Gen\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Tot`, `Case=Ins\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Tot`, `Case=Sup\|NumType=Frac\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Abs\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Acc\|Degree=Pos\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Acc\|NumType[sem]=Percent\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Ter\|NumType[sem]=Percent\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Dat\|Number=Sing\|Number[psed]=Sing\|POS=NOUN`, `Case=Nom\|NumType[sem]=Percent\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Acc\|NumType[sem]=Percent\|Number=Sing\|POS=NUM`, `Case=Ter\|NumType=Frac\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Ade\|NumType[sem]=Percent\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Ins\|NumType[sem]=Percent\|Number=Sing\|POS=NUM`, `Case=Ins\|NumType[sem]=Percent\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Gen\|NumType[sem]=Percent\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Dat\|NumType[sem]=Percent\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Sbl\|NumType[sem]=Percent\|Number=Sing\|POS=NUM`, `Case=Ine\|NumType[sem]=Percent\|Number=Sing\|POS=NUM`, `Case=All\|NumType=Frac\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Ade\|NumType=Frac\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Nom\|NumType[sem]=Percent\|Number=Sing\|POS=NUM`, `Case=All\|NumType[sem]=Percent\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Abl\|NumType=Card\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Ter\|NumType=Card\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Acc\|NumType=Card\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Ter\|NumType[sem]=Formula\|Number=Sing\|POS=NUM`, `Case=Sbl\|NumType[sem]=Percent\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=All\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=3`, `Case=Del\|NumType=Frac\|Number=Sing\|POS=NUM`, `Case=Cau\|NumType=Frac\|Number=Sing\|POS=NUM`, `Case=Gen\|Number=Plur\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=3`, `Case=Ins\|NumType=Ord\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Ade\|NumType=Frac\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person[psor]=3`, `Case=Ine\|NumType=Frac\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person[psor]=3`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Ind`, `Case=Sup\|NumType=Card\|Number=Plur\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Tra\|Number=Sing\|Number[psed]=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Ine\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Number=Sing\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Tra\|Number=Sing\|Number[psed]=Sing\|POS=NOUN`, `Case=Gen\|Number=Sing\|Number[psed]=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Nom\|Number=Sing\|Number[psed]=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=3`, `Case=Gen\|Number=Sing\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Tem\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Dat\|NumType[sem]=Dot\|Number=Sing\|POS=NUM`, `Case=Sbl\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=All\|Number=Sing\|Number[psed]=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ine\|NumType=Frac\|Number=Sing\|POS=NUM`, `Case=All\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Sbl\|Number=Plur\|POS=PROPN`, `Case=Tra\|Number=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Case=Sup\|NumType=Frac\|Number=Sing\|POS=NUM`, `Case=Dat\|Number=Plur\|Number[psed]=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Dat\|Number=Sing\|POS=SYM\|Type=w`, `Case=Ill\|Number=Plur\|POS=PROPN`, `Case=Loc\|Number=Sing\|POS=PROPN`, `Case=Ess\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Acc\|Degree=Pos\|Number=Plur\|Number[psed]=Sing\|POS=ADJ`, `Case=Abl\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=All\|NumType=Frac\|Number=Sing\|POS=NUM`, `Case=Ade\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Ine\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Ine\|Number=Sing\|POS=SYM\|Type=w`, `Case=Cau\|NumType=Frac\|Number=Sing\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Ela\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=3`, `Case=Abs\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Sbl\|NumType[sem]=Dot\|Number=Sing\|POS=NUM`, `Case=Tem\|Number=Sing\|POS=PROPN`, `Case=Del\|NumType[sem]=Dot\|Number=Sing\|POS=NUM`, `Case=Ade\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=3`, `Case=Ine\|Number=Sing\|Number[psor]=Plur\|POS=PROPN\|Person[psor]=1`, `Case=Nom\|Number=Sing\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Case=Acc\|Degree=Sup\|Number=Plur\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Ade\|Number=Plur\|Number[psed]=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=3`, `Case=Ela\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Acc\|Number=Plur\|Number[psed]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Del\|Number=Plur\|Number[psed]=Sing\|POS=NOUN`, `Case=Nom\|Degree=Sup\|Number=Sing\|Number[psor]=Sing\|POS=ADJ\|Person[psor]=3`, `Case=Dat\|Number=Plur\|POS=PROPN`, `Case=Ill\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Sbl\|NumType=Card\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person[psor]=3`, `Case=Ins\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Plur\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Ter\|Number=Plur\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Ess\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Sup\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=2`, `Case=Acc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=2\|PronType=Tot`, `Case=Gen\|NumType=Card\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person[psor]=3`, `Case=Ine\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Int`, `Case=All\|Number=Sing\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=3`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=2`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rcp`, `Definite=Ind\|Mood=Imp\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|Voice=Act`, `Case=Tra\|Number=Sing\|Number[psor]=Plur\|POS=NOUN\|Person[psor]=1`, `Case=Ins\|NumType=Card\|Number=Plur\|Number[psor]=Sing\|POS=NUM\|Person[psor]=3`, `Case=Del\|Number=Sing\|POS=PRON\|Person=2\|Reflexive=Yes`, `Case=Sbl\|NumType=Card\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person[psor]=1`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=2\|PronType=Ind`, `Case=All\|Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Sbl\|Number=Sing\|Number[psed]=Sing\|POS=PROPN`, `Case=Ill\|Number=Sing\|Number[psed]=Sing\|POS=PROPN`, `Case=Ine\|NumType=Card\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person[psor]=3`, `Case=Del\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Number=Sing\|Number[psed]=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=2\|PronType=Tot`, `Case=Abl\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Int`, `Case=Ine\|Number=Sing\|Number[psed]=Sing\|POS=PROPN`, `Case=Cau\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Del\|Number=Plur\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Cau\|Number=Sing\|POS=PRON\|Person=3\|Reflexive=Yes`, `Case=Nom\|NumType=Card\|Number=Sing\|Number[psor]=Plur\|POS=NUM\|Person[psor]=2`, `Case=Abl\|Number=Sing\|Number[psor]=Sing\|POS=NOUN\|Person[psor]=2`, `Case=Ine\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Definite=2\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Tot`, `Case=Ela\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Acc\|Number=Sing\|POS=SYM\|Type=p`, `Case=Abl\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Acc\|Number=Plur\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Ine\|Number=Plur\|POS=PROPN`, `Case=Sbl\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Person[psor]=3\|PronType=Tot`, `Case=Ins\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=3\|Person[psor]=3\|Poss=Yes\|PronType=Prs`, `Case=Ter\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=PROPN\|Person[psor]=3`, `Case=All\|Number=Sing\|Number[psed]=Sing\|POS=PRON\|Person=3\|PronType=Tot` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `advmod:locy`, `advmod:mode`, `advmod:que`, `advmod:tfrom`, `advmod:tlocy`, `advmod:to`, `advmod:tto`, `amod:att`, `appos`, `aux`, `case`, `cc`, `ccomp`, `ccomp:obj`, `ccomp:obl`, `ccomp:pred`, `compound`, `compound:preverb`, `conj`, `cop`, `csubj`, `dep`, `det`, `flat:name`, `iobj`, `list`, `mark`, `nmod`, `nmod:att`, `nmod:obl`, `nsubj`, `nummod`, `obj`, `obj:lvc`, `obl`, `orphan`, `parataxis`, `punct`, `xcomp` |
| **`ner`** | `LOC`, `MISC`, `ORG`, `PER` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.99 |
| `TOKEN_P` | 99.86 |
| `TOKEN_R` | 99.93 |
| `TOKEN_F` | 99.89 |
| `SENTS_P` | 98.43 |
| `SENTS_R` | 98.00 |
| `SENTS_F` | 98.21 |
| `TAG_ACC` | 96.89 |
| `POS_ACC` | 96.70 |
| `MORPH_ACC` | 93.63 |
| `MORPH_MICRO_P` | 97.03 |
| `MORPH_MICRO_R` | 95.98 |
| `MORPH_MICRO_F` | 96.50 |
| `LEMMA_ACC` | 97.60 |
| `DEP_UAS` | 83.32 |
| `DEP_LAS` | 76.92 |
| `ENTS_P` | 87.15 |
| `ENTS_R` | 85.94 |
| `ENTS_F` | 86.54 |
|
{"language": ["hu"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]}
|
huspacy/hu_core_news_lg
| null |
[
"spacy",
"token-classification",
"hu",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"hu"
] |
TAGS
#spacy #token-classification #hu #license-cc-by-sa-4.0 #model-index #region-us
|
Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morphologizer, lemmatizer, parser, ner
### Label Scheme
View label scheme (1209 labels for 4 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (1209 labels for 4 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #hu #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1209 labels for 4 components)",
"### Accuracy"
] |
token-classification
|
transformers
|
Model trained for PhoNER_COVID19 dataset
|
{}
|
huyenbui117/Covid19_phoNER
| null |
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #safetensors #roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
|
Model trained for PhoNER_COVID19 dataset
|
[] |
[
"TAGS\n#transformers #pytorch #safetensors #roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null |
test
|
{}
|
huyongquan/m1
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
test
|
[] |
[
"TAGS\n#region-us \n"
] |
null | null |
m4
|
{}
|
huyongquan/m4
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
m4
|
[] |
[
"TAGS\n#region-us \n"
] |
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-cynthia-tedlium-2500-v2
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6425
- Wer: 0.2033
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1196 | 6.58 | 500 | 0.6498 | 0.2103 |
| 0.1176 | 13.16 | 1000 | 0.6490 | 0.2169 |
| 0.1227 | 19.73 | 1500 | 0.6241 | 0.2127 |
| 0.1078 | 26.31 | 2000 | 0.6359 | 0.2118 |
| 0.0956 | 32.89 | 2500 | 0.6330 | 0.2073 |
| 0.1008 | 39.47 | 3000 | 0.6816 | 0.2036 |
| 0.09 | 46.05 | 3500 | 0.6425 | 0.2033 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.13.3
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-cynthia-tedlium-2500-v2", "results": []}]}
|
huyue012/wav2vec2-base-cynthia-tedlium-2500-v2
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-base-cynthia-tedlium-2500-v2
=====================================
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6425
* Wer: 0.2033
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 50
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.0+cu113
* Datasets 1.13.3
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu113\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu113\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-cynthia-tedlium-2500
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4356
- Wer: 0.1796
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.2811 | 6.58 | 500 | 3.1455 | 0.9934 |
| 3.0533 | 13.16 | 1000 | 2.9568 | 0.9934 |
| 1.8269 | 19.73 | 1500 | 0.7595 | 0.2484 |
| 0.5103 | 26.31 | 2000 | 0.4356 | 0.1796 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.13.3
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-cynthia-tedlium-2500", "results": []}]}
|
huyue012/wav2vec2-base-cynthia-tedlium-2500
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-base-cynthia-tedlium-2500
==================================
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4356
* Wer: 0.1796
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.0+cu113
* Datasets 1.13.3
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu113\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu113\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-cynthia-tedlium
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-cynthia-tedlium", "results": []}]}
|
huyue012/wav2vec2-base-cynthia-tedlium
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-cynthia-tedlium
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.15.1
- Tokenizers 0.10.3
|
[
"# wav2vec2-base-cynthia-tedlium\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu113\n- Datasets 1.15.1\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-cynthia-tedlium\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu113\n- Datasets 1.15.1\n- Tokenizers 0.10.3"
] |
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-cynthia-timit
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4888
- Wer: 0.3315
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.7674 | 1.0 | 500 | 2.8994 | 1.0 |
| 1.3538 | 2.01 | 1000 | 0.5623 | 0.5630 |
| 0.5416 | 3.01 | 1500 | 0.4595 | 0.4765 |
| 0.3563 | 4.02 | 2000 | 0.4435 | 0.4328 |
| 0.2869 | 5.02 | 2500 | 0.4035 | 0.4145 |
| 0.2536 | 6.02 | 3000 | 0.4090 | 0.3945 |
| 0.2072 | 7.03 | 3500 | 0.4188 | 0.3809 |
| 0.1825 | 8.03 | 4000 | 0.4139 | 0.3865 |
| 0.1754 | 9.04 | 4500 | 0.4320 | 0.3763 |
| 0.1477 | 10.04 | 5000 | 0.4668 | 0.3699 |
| 0.1418 | 11.04 | 5500 | 0.4439 | 0.3683 |
| 0.1207 | 12.05 | 6000 | 0.4419 | 0.3678 |
| 0.115 | 13.05 | 6500 | 0.4606 | 0.3786 |
| 0.1022 | 14.06 | 7000 | 0.4403 | 0.3610 |
| 0.1019 | 15.06 | 7500 | 0.4966 | 0.3609 |
| 0.0898 | 16.06 | 8000 | 0.4675 | 0.3586 |
| 0.0824 | 17.07 | 8500 | 0.4844 | 0.3583 |
| 0.0737 | 18.07 | 9000 | 0.4801 | 0.3534 |
| 0.076 | 19.08 | 9500 | 0.4945 | 0.3529 |
| 0.0627 | 20.08 | 10000 | 0.4700 | 0.3417 |
| 0.0723 | 21.08 | 10500 | 0.4630 | 0.3449 |
| 0.0597 | 22.09 | 11000 | 0.5164 | 0.3456 |
| 0.0566 | 23.09 | 11500 | 0.4957 | 0.3401 |
| 0.0453 | 24.1 | 12000 | 0.5032 | 0.3419 |
| 0.0492 | 25.1 | 12500 | 0.5391 | 0.3387 |
| 0.0524 | 26.1 | 13000 | 0.5057 | 0.3348 |
| 0.0381 | 27.11 | 13500 | 0.5098 | 0.3331 |
| 0.0402 | 28.11 | 14000 | 0.5087 | 0.3353 |
| 0.0358 | 29.12 | 14500 | 0.4888 | 0.3315 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-cynthia-timit", "results": []}]}
|
huyue012/wav2vec2-base-cynthia-timit
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-base-cynthia-timit
===========================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4888
* Wer: 0.3315
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.13.3
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
token-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0617
- Precision: 0.9274
- Recall: 0.9397
- F1: 0.9335
- Accuracy: 0.9838
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2403 | 1.0 | 878 | 0.0714 | 0.9171 | 0.9216 | 0.9193 | 0.9805 |
| 0.0555 | 2.0 | 1756 | 0.0604 | 0.9206 | 0.9347 | 0.9276 | 0.9829 |
| 0.031 | 3.0 | 2634 | 0.0617 | 0.9274 | 0.9397 | 0.9335 | 0.9838 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.7.1
- Datasets 1.18.3
- Tokenizers 0.10.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.9273570324574961, "name": "Precision"}, {"type": "recall", "value": 0.9397024275646045, "name": "Recall"}, {"type": "f1", "value": 0.9334889148191365, "name": "F1"}, {"type": "accuracy", "value": 0.9837641190207635, "name": "Accuracy"}]}]}]}
|
hyerim/distilbert-base-uncased-finetuned-ner
| null |
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0617
* Precision: 0.9274
* Recall: 0.9397
* F1: 0.9335
* Accuracy: 0.9838
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.7.1
* Datasets 1.18.3
* Tokenizers 0.10.1
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.7.1\n* Datasets 1.18.3\n* Tokenizers 0.10.1"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.7.1\n* Datasets 1.18.3\n* Tokenizers 0.10.1"
] |
summarization
|
transformers
|
hello! this is the pretrained BART. The dataset used for pretraining is nonsense summary corpus with output as diff.
|
{"language": ["en"], "tags": ["summarization", "diff generation"], "datasets": ["nonsense corpus"], "metrics": ["rouge"]}
|
hyesunyun/NonsenseUpdateDiffIntBart
| null |
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"diff generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bart #text2text-generation #summarization #diff generation #en #autotrain_compatible #endpoints_compatible #region-us
|
hello! this is the pretrained BART. The dataset used for pretraining is nonsense summary corpus with output as diff.
|
[] |
[
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #diff generation #en #autotrain_compatible #endpoints_compatible #region-us \n"
] |
summarization
|
transformers
|
hello! this is the pretrained BART. The dataset used for pretraining is nonsense summary corpus with output as diff.
|
{"language": ["en"], "tags": ["summarization", "diff generation"], "datasets": ["nonsense corpus"], "metrics": ["rouge"]}
|
hyesunyun/NonsenseUpdateDiffStringBart
| null |
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"diff generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bart #text2text-generation #summarization #diff generation #en #autotrain_compatible #endpoints_compatible #region-us
|
hello! this is the pretrained BART. The dataset used for pretraining is nonsense summary corpus with output as diff.
|
[] |
[
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #diff generation #en #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation
|
transformers
|
# Update Summarization with BART Large and Longformer Encoder Decoder
## Model description
This model is a Transformer-based model that supports long document generative sequence-to-sequence.
Based on [BART Large](https://huggingface.co/transformers/model_doc/bart.html) with [Longformer Encode Decoder](https://huggingface.co/transformers/model_doc/led.html) to allow for longer inputs.
## Intended uses & limitations
#### How to use
Format your data so that each new article or evidence to add have `<EV>` token in front with each title prefixed by `<t>` and each abstract prefixed by `<abs>`. Please have the original summary also in the same format. You can have the list of articles and original summary concatenated in any order as long as they have the correct separator tokens.
```python
import torch
from transformers import LEDTokenizer, LEDForConditionalGeneration
tokenizer = LEDTokenizer.from_pretrained("hyesunyun/update-summarization-bart-large-longformer")
model = LEDForConditionalGeneration.from_pretrained("hyesunyun/update-summarization-bart-large-longformer")
input = "<EV> <t> Hypoglycemic effect of bitter melon compared with metformin in newly diagnosed type 2 diabetes patients. <abs> ETHNOPHARMACOLOGICAL RELEVANCE: Bitter melon (Momordica charantia L.) has been widely used as an traditional medicine treatment for diabetic patients in Asia. In vitro and animal studies suggested its hypoglycemic activity, but limited human studies are available to support its use. AIM OF STUDY: This study was conducted to assess the efficacy and safety of three doses of bitter melon compared with metformin. MATERIALS AND METHODS: This is a 4-week, multicenter, randomized, double-blind, active-control trial. Patients were randomized into 4 groups to receive bitter melon 500 mg/day, 1,000 mg/day, and 2,000 mg/day or metformin 1,000 mg/day. All patients were followed for 4 weeks. RESULTS: There was a significant decline in fructosamine at week 4 of the metformin group (-16.8; 95% CI, -31.2, -2.4 mumol/L) and the bitter melon 2,000 mg/day group (-10.2; 95% CI, -19.1, -1.3 mumol/L). Bitter melon 500 and 1,000 mg/day did not significantly decrease fructosamine levels (-3.5; 95% CI -11.7, 4.6 and -10.3; 95% CI -22.7, 2.2 mumol/L, respectively). CONCLUSIONS: Bitter melon had a modest hypoglycemic effect and significantly reduced fructosamine levels from baseline among patients with type 2 diabetes who received 2,000 mg/day. However, the hypoglycemic effect of bitter melon was less than metformin 1,000 mg/day. <EV> <t> Momordica charantia for type 2 diabetes mellitus. <abs> There is insufficient evidence to recommend momordica charantia for type 2 diabetes mellitus. Further studies are therefore required to address the issues of standardization and the quality control of preparations. For medical nutritional therapy, further observational trials evaluating the effects of momordica charantia are needed before RCTs are established to guide any recommendations in clinical practice."
inputs_dict = tokenizer(input, padding="max_length", max_length=10240, return_tensors="pt", truncation=True)
input_ids = inputs_dict.input_ids
attention_mask = inputs_dict.attention_mask
global_attention_mask = torch.zeros_like(attention_mask)
# put global attention on <s> token
global_attention_mask[:, 0] = 1
predicted_summary_ids = model.generate(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask)
print(tokenizer.batch_decode(predicted_summary_ids, skip_special_tokens=True))
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Used pre-trained [LED model](https://huggingface.co/transformers/model_doc/led.html) and fine-tuned using the dataset found in [this github repo](https://github.com/hyesunyun/update_summarization_data).
## Training procedure
Preprocessing, hardware used, hyperparameters...
## Eval results
### BibTeX entry and citation info
```bibtex
@inproceedings{...,
year={2021}
}
```
|
{"language": ["en"], "license": ["apache-2.0"], "tags": ["update summarization", "longformer", "transformers", "BART"], "metrics": ["edit distance", "ROUGE", "BertScore"]}
|
hyesunyun/update-summarization-bart-large-longformer
| null |
[
"transformers",
"pytorch",
"led",
"text2text-generation",
"update summarization",
"longformer",
"BART",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #led #text2text-generation #update summarization #longformer #BART #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Update Summarization with BART Large and Longformer Encoder Decoder
## Model description
This model is a Transformer-based model that supports long document generative sequence-to-sequence.
Based on BART Large with Longformer Encode Decoder to allow for longer inputs.
## Intended uses & limitations
#### How to use
Format your data so that each new article or evidence to add have '<EV>' token in front with each title prefixed by '<t>' and each abstract prefixed by '<abs>'. Please have the original summary also in the same format. You can have the list of articles and original summary concatenated in any order as long as they have the correct separator tokens.
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Used pre-trained LED model and fine-tuned using the dataset found in this github repo.
## Training procedure
Preprocessing, hardware used, hyperparameters...
## Eval results
### BibTeX entry and citation info
|
[
"# Update Summarization with BART Large and Longformer Encoder Decoder",
"## Model description\n\nThis model is a Transformer-based model that supports long document generative sequence-to-sequence.\n\nBased on BART Large with Longformer Encode Decoder to allow for longer inputs.",
"## Intended uses & limitations",
"#### How to use\n\nFormat your data so that each new article or evidence to add have '<EV>' token in front with each title prefixed by '<t>' and each abstract prefixed by '<abs>'. Please have the original summary also in the same format. You can have the list of articles and original summary concatenated in any order as long as they have the correct separator tokens.",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\nUsed pre-trained LED model and fine-tuned using the dataset found in this github repo.",
"## Training procedure\n\nPreprocessing, hardware used, hyperparameters...",
"## Eval results",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #led #text2text-generation #update summarization #longformer #BART #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Update Summarization with BART Large and Longformer Encoder Decoder",
"## Model description\n\nThis model is a Transformer-based model that supports long document generative sequence-to-sequence.\n\nBased on BART Large with Longformer Encode Decoder to allow for longer inputs.",
"## Intended uses & limitations",
"#### How to use\n\nFormat your data so that each new article or evidence to add have '<EV>' token in front with each title prefixed by '<t>' and each abstract prefixed by '<abs>'. Please have the original summary also in the same format. You can have the list of articles and original summary concatenated in any order as long as they have the correct separator tokens.",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\nUsed pre-trained LED model and fine-tuned using the dataset found in this github repo.",
"## Training procedure\n\nPreprocessing, hardware used, hyperparameters...",
"## Eval results",
"### BibTeX entry and citation info"
] |
null | null |
# Hyperion Toolkit Speaker Verification pre-trained Model
## Model Configuration
This model was trained using recipe [voxceleb/v1.1](https://github.com/hyperion-ml/hyperion/tree/master/egs/voxceleb/v1.1)
The configuration for this modeis is defined in
[config_fbank80_stmn_lresnet34_arcs30m0.3_adam_lr0.05_amp.v1.sh](https://github.com/hyperion-ml/hyperion/blob/master/egs/voxceleb/v1.1/global_conf/config_fbank80_stmn_lresnet34_arcs30m0.3_adam_lr0.05_amp.v1.sh)
This is an x-vector model with:
- 80 logMel filter-banks with short-time mean normalization.
- ThinResNet34 (aka Light ResNet34) encoder.
- Mean+Stddev pooling
- AAM-softmax loss (m=0.3, s=30)
- Mixed prec. training.
|
{"language": ["en"], "license": "apache-2.0", "tags": ["hyperion", "audio", "speech", "speaker-recognition", "x-vector", "thin-resnet34"], "datasets": ["voxceleb"], "metrics": ["eer", "min_dcf-p=0.05", "min_dcf-p=0.01"], "model-index": [{"name": "voxceleb-v1.1-fbank80_stmn_lresnet34_e256_arcs30m0.3_do0_adam_lr0.05_b512.v1", "results": [{"task": {"type": "speaker-verification", "name": "Speaker Verification"}, "dataset": {"name": "Voxceleb1", "type": "voxceleb1", "args": "Train on VoxCeleb2-dev"}, "metrics": [{"type": "eer", "value": 2.11, "name": "EER Vox1-O"}, {"type": "min_dcf-p=0.05", "value": 0.135, "name": "Minimum DCF Vox1-O prior=0.05"}, {"type": "act_dcf-p=0.01", "value": 0.208, "name": "Minimum DCF Vox1-O prior=0.01"}, {"type": "eer", "value": 1.93, "name": "EER Vox1-E"}, {"type": "min_dcf-p=0.05", "value": 0.121, "name": "Minimum DCF Vox1-E prior=0.05"}, {"type": "act_dcf-p=0.01", "value": 0.204, "name": "Minimum DCF Vox1-E Original prior=0.01"}, {"type": "eer", "value": 3.21, "name": "EER Vox1-H"}, {"type": "min_dcf-p=0.05", "value": 0.19, "name": "Minimum DCF Vox1-H prior=0.05"}, {"type": "act_dcf-p=0.01", "value": 0.298, "name": "Minimum DCF Vox1-H Original prior=0.01"}]}]}]}
|
hyperion-ml/voxceleb-v1.1-fbank80_stmn_lresnet34_e256_arcs30m0.3_do0_adam_lr0.05_b512.v1
| null |
[
"hyperion",
"audio",
"speech",
"speaker-recognition",
"x-vector",
"thin-resnet34",
"en",
"dataset:voxceleb",
"license:apache-2.0",
"model-index",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#hyperion #audio #speech #speaker-recognition #x-vector #thin-resnet34 #en #dataset-voxceleb #license-apache-2.0 #model-index #region-us
|
# Hyperion Toolkit Speaker Verification pre-trained Model
## Model Configuration
This model was trained using recipe voxceleb/v1.1
The configuration for this modeis is defined in
config_fbank80_stmn_lresnet34_arcs30m0.3_adam_lr0.05_amp.URL
This is an x-vector model with:
- 80 logMel filter-banks with short-time mean normalization.
- ThinResNet34 (aka Light ResNet34) encoder.
- Mean+Stddev pooling
- AAM-softmax loss (m=0.3, s=30)
- Mixed prec. training.
|
[
"# Hyperion Toolkit Speaker Verification pre-trained Model",
"## Model Configuration\r\nThis model was trained using recipe voxceleb/v1.1\r\n\r\nThe configuration for this modeis is defined in\r\nconfig_fbank80_stmn_lresnet34_arcs30m0.3_adam_lr0.05_amp.URL\r\n\r\nThis is an x-vector model with:\r\n - 80 logMel filter-banks with short-time mean normalization.\r\n - ThinResNet34 (aka Light ResNet34) encoder.\r\n - Mean+Stddev pooling\r\n - AAM-softmax loss (m=0.3, s=30)\r\n - Mixed prec. training."
] |
[
"TAGS\n#hyperion #audio #speech #speaker-recognition #x-vector #thin-resnet34 #en #dataset-voxceleb #license-apache-2.0 #model-index #region-us \n",
"# Hyperion Toolkit Speaker Verification pre-trained Model",
"## Model Configuration\r\nThis model was trained using recipe voxceleb/v1.1\r\n\r\nThe configuration for this modeis is defined in\r\nconfig_fbank80_stmn_lresnet34_arcs30m0.3_adam_lr0.05_amp.URL\r\n\r\nThis is an x-vector model with:\r\n - 80 logMel filter-banks with short-time mean normalization.\r\n - ThinResNet34 (aka Light ResNet34) encoder.\r\n - Mean+Stddev pooling\r\n - AAM-softmax loss (m=0.3, s=30)\r\n - Mixed prec. training."
] |
null | null |
# Yet-Another-Anime-Segmenter
- Repo: https://github.com/zymk9/Yet-Another-Anime-Segmenter
- https://drive.google.com/file/d/1-wFdQ4jwSTeJ7wGD3YKNJdcpSS5Ho8c9/view?usp=sharing
- https://raw.githubusercontent.com/zymk9/Yet-Another-Anime-Segmenter/main/configs/SOLOv2.yaml
|
{}
|
public-data/Yet-Another-Anime-Segmenter
| null |
[
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#has_space #region-us
|
# Yet-Another-Anime-Segmenter
- Repo: URL
- URL
- URL
|
[
"# Yet-Another-Anime-Segmenter\n\n- Repo: URL\n - URL\n - URL"
] |
[
"TAGS\n#has_space #region-us \n",
"# Yet-Another-Anime-Segmenter\n\n- Repo: URL\n - URL\n - URL"
] |
null | null |
# anime_face_landmark_detection
- Repo: https://github.com/kanosawa/anime_face_landmark_detection
- https://drive.google.com/open?id=1NckKw7elDjQTllRxttO87WY7cnQwdMqz
|
{}
|
public-data/anime_face_landmark_detection
| null |
[
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#has_space #region-us
|
# anime_face_landmark_detection
- Repo: URL
- URL
|
[
"# anime_face_landmark_detection\n\n- Repo: URL\n - URL"
] |
[
"TAGS\n#has_space #region-us \n",
"# anime_face_landmark_detection\n\n- Repo: URL\n - URL"
] |
null | null |
# bizarre-pose-estimator
- Repo: https://github.com/ShuhongChen/bizarre-pose-estimator
- https://drive.google.com/drive/folders/11bw47Vy-RPKjgd6yF0RzcXALvp7zB_wt
|
{}
|
public-data/bizarre-pose-estimator-models
| null |
[
"region:us",
"has_space"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us #has_space
|
# bizarre-pose-estimator
- Repo: URL
- URL
|
[
"# bizarre-pose-estimator\n\n- Repo: URL\n - URL"
] |
[
"TAGS\n#region-us #has_space \n",
"# bizarre-pose-estimator\n\n- Repo: URL\n - URL"
] |
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