modelId
stringlengths 5
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| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-02 06:30:45
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 533
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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huggingtweets/albertletranger
|
huggingtweets
| 2021-05-21T18:01:16Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/albertletranger/1616779907134/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1148966885024837635/8ihdfQKv_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Albert 🤖 AI Bot </div>
<div style="font-size: 15px">@albertletranger bot</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on [@albertletranger's tweets](https://twitter.com/albertletranger).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3230 |
| Retweets | 1299 |
| Short tweets | 362 |
| Tweets kept | 1569 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/x4s90a6l/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @albertletranger's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/10wrv1a0) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/10wrv1a0/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/albertletranger')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/ahmedallibhoy
|
huggingtweets
| 2021-05-21T17:53:42Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/ahmedallibhoy/1616643813999/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1297351407809380352/gW1wWpRv_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Ahmed 🤖 AI Bot </div>
<div style="font-size: 15px">@ahmedallibhoy bot</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on [@ahmedallibhoy's tweets](https://twitter.com/ahmedallibhoy).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 226 |
| Retweets | 82 |
| Short tweets | 1 |
| Tweets kept | 143 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/6cjgzd9a/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ahmedallibhoy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3g9v31lb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3g9v31lb/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ahmedallibhoy')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/afinchwrites
|
huggingtweets
| 2021-05-21T17:47:53Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/afinchwrites/1617758836679/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1250126825109544960/8ndvxL2E_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Ashley Finch 🔞 🤖 AI Bot </div>
<div style="font-size: 15px">@afinchwrites bot</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on [@afinchwrites's tweets](https://twitter.com/afinchwrites).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3214 |
| Retweets | 1236 |
| Short tweets | 265 |
| Tweets kept | 1713 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1bwfztuv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @afinchwrites's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/39vriclf) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/39vriclf/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/afinchwrites')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/ae333mage
|
huggingtweets
| 2021-05-21T17:46:25Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/ae333mage/1619282749797/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1385948567622324230/XKbD4BWp_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">aeva 🤖 AI Bot </div>
<div style="font-size: 15px">@ae333mage bot</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on [@ae333mage's tweets](https://twitter.com/ae333mage).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3169 |
| Retweets | 1776 |
| Short tweets | 592 |
| Tweets kept | 801 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/31fkltap/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ae333mage's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/jg2xbkk5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/jg2xbkk5/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ae333mage')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/adrienna_w
|
huggingtweets
| 2021-05-21T17:45:21Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/adrienna_w/1610164811243/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1267145246359474176/OtRIrSIL_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Adrienna Wong 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@adrienna_w bot</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on [@adrienna_w's tweets](https://twitter.com/adrienna_w).
<table style='border-width:0'>
<thead style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
<th style='border-width:0'>Data</th>
<th style='border-width:0'>Quantity</th>
</tr>
</thead>
<tbody style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Tweets downloaded</td>
<td style='border-width:0'>2000</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Retweets</td>
<td style='border-width:0'>1570</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Short tweets</td>
<td style='border-width:0'>46</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>384</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3r42s34p/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @adrienna_w's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3n5znqzh) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3n5znqzh/artifacts) is logged and versioned.
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for text generation:
<pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline
generator = pipeline(<span style="color:#FF9800">'text-generation'</span>,
model=<span style="color:#FF9800">'huggingtweets/adrienna_w'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
</section>
[](https://twitter.com/intent/follow?screen_name=borisdayma)
<section class='prose'>
For more details, visit the project repository.
</section>
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/adjacentgrace
|
huggingtweets
| 2021-05-21T17:42:06Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/adjacentgrace/1616623328480/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1366296275302248448/ZQk6DPNb_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Grace 🤖 AI Bot </div>
<div style="font-size: 15px">@adjacentgrace 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 [@adjacentgrace's tweets](https://twitter.com/adjacentgrace).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 561 |
| Retweets | 155 |
| Short tweets | 61 |
| Tweets kept | 345 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1xjsh2v0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @adjacentgrace's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/14n88k4d) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/14n88k4d/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/adjacentgrace')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/adiaeu
|
huggingtweets
| 2021-05-21T17:40:56Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/adiaeu/1608391370887/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1324015708104089600/ZrXV0rUp_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">ًenhypen debut 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@adiaeu 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 [@adiaeu's tweets](https://twitter.com/adiaeu).
<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'>3167</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'>285</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'>598</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2284</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2mizccrh/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @adiaeu's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1jcjoc84) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1jcjoc84/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/adiaeu'</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)
|
huggingtweets/adhitadselvaraj
|
huggingtweets
| 2021-05-21T17:39:36Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo_share.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('http://pbs.twimg.com/profile_images/1295249742801203206/F3Wl-EIy_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Adhita 😷 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@adhitadselvaraj 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 [@adhitadselvaraj's tweets](https://twitter.com/adhitadselvaraj).
<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'>3209</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'>649</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'>521</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2039</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/27pmox1m/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @adhitadselvaraj's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/3m21mvy6) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/3m21mvy6/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/adhitadselvaraj'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
</section>
[](https://twitter.com/borisdayma)
<section class='prose'>
For more details, visit the project repository.
</section>
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/adhib
|
huggingtweets
| 2021-05-21T17:38:33Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/adhib/1617472294749/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1259773709210079234/zy8BML5a_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Adam Hibbert 🤖 AI Bot </div>
<div style="font-size: 15px">@adhib 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 [@adhib's tweets](https://twitter.com/adhib).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 89 |
| Short tweets | 509 |
| Tweets kept | 2649 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3vmd854v/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @adhib's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/okvjl3od) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/okvjl3od/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/adhib')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/adderallia
|
huggingtweets
| 2021-05-21T17:37:15Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1371446905604087813/2FxI9YMM_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">macy 🤖 AI Bot </div>
<div style="font-size: 15px">@adderallia 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 [@adderallia's tweets](https://twitter.com/adderallia).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 295 |
| Retweets | 71 |
| Short tweets | 5 |
| Tweets kept | 219 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/jjeo4uw5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @adderallia's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/g3f6vfg5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/g3f6vfg5/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/adderallia')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/actiongeologist
|
huggingtweets
| 2021-05-21T17:32:02Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/actiongeologist/1617468825652/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1322985945960902656/2dAh5NDP_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Lydia 🤖 AI Bot </div>
<div style="font-size: 15px">@actiongeologist 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 [@actiongeologist's tweets](https://twitter.com/actiongeologist).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1062 |
| Retweets | 31 |
| Short tweets | 81 |
| Tweets kept | 950 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/b7gw8mp3/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @actiongeologist's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/327hbgyu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/327hbgyu/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/actiongeologist')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/acephallus
|
huggingtweets
| 2021-05-21T17:29:14Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/acephallus/1617764407342/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1377643728123404290/yXoDgE8c_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">annie halation! 🌺 🤖 AI Bot </div>
<div style="font-size: 15px">@acephallus 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 [@acephallus's tweets](https://twitter.com/acephallus).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3227 |
| Retweets | 223 |
| Short tweets | 613 |
| Tweets kept | 2391 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/gd83k7hw/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @acephallus's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/32kzz65f) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/32kzz65f/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/acephallus')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/abupepeofficial
|
huggingtweets
| 2021-05-21T17:27:52Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1364172631923191818/EBpmjPVo_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Abu Pepe 🤖 AI Bot </div>
<div style="font-size: 15px">@abupepeofficial 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 [@abupepeofficial's tweets](https://twitter.com/abupepeofficial).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3214 |
| Retweets | 829 |
| Short tweets | 692 |
| Tweets kept | 1693 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2g5r3yyi/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @abupepeofficial's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/avhc3qdq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/avhc3qdq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/abupepeofficial')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/abelaer
|
huggingtweets
| 2021-05-21T17:25:22Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/abelaer/1616682063676/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1085138421599870976/y1VodNUp_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Abel Jansma 🤖 AI Bot </div>
<div style="font-size: 15px">@abelaer 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 [@abelaer's tweets](https://twitter.com/abelaer).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 231 |
| Retweets | 15 |
| Short tweets | 14 |
| Tweets kept | 202 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2fgjwxfv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @abelaer's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/39ffistv) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/39ffistv/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/abelaer')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/abcdentminded
|
huggingtweets
| 2021-05-21T17:23:05Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1296648016875548673/0RDPcPIT_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">⊖┤abcdentminded├⊕ 🤖 AI Bot </div>
<div style="font-size: 15px">@abcdentminded 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 [@abcdentminded's tweets](https://twitter.com/abcdentminded).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 88 |
| Short tweets | 529 |
| Tweets kept | 2630 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2isrhdw8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @abcdentminded's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2u366z3l) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2u366z3l/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/abcdentminded')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/_tinyflower
|
huggingtweets
| 2021-05-21T17:17:43Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1322810236500025348/n4DEuDvs_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">flappy fawn 🌸🍼 🤖 AI Bot </div>
<div style="font-size: 15px">@_tinyflower 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 [@_tinyflower's tweets](https://twitter.com/_tinyflower).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3185 |
| Retweets | 2019 |
| Short tweets | 181 |
| Tweets kept | 985 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/4osh65pp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @_tinyflower's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3237hlmg) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3237hlmg/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/_tinyflower')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/_sydkit_
|
huggingtweets
| 2021-05-21T17:16:16Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/_sydkit_/1614138714911/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1256014674799321088/BVrvm4TY_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">sydkit 🤖 AI Bot </div>
<div style="font-size: 15px">@_sydkit_ 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 [@_sydkit_'s tweets](https://twitter.com/_sydkit_).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 160 |
| Retweets | 22 |
| Short tweets | 13 |
| Tweets kept | 125 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/b48v48wr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @_sydkit_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/31cgxgnq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/31cgxgnq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/_sydkit_')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/_srhiggins
|
huggingtweets
| 2021-05-21T17:12:59Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1246772902864646150/Ifl8SpYc_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Sebastian from the Order of Moonlit Knight 🤖 AI Bot </div>
<div style="font-size: 15px">@_srhiggins 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 [@_srhiggins's tweets](https://twitter.com/_srhiggins).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3172 |
| Retweets | 767 |
| Short tweets | 680 |
| Tweets kept | 1725 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2jr6qa4l/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @_srhiggins's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1xfxz8ne) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1xfxz8ne/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/_srhiggins')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/_rdo
|
huggingtweets
| 2021-05-21T17:11:57Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/_rdo/1602271625590/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/664614733476175873/Mk9AdCB3_400x400.png')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Rodrigo Ricárdez 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@_rdo 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 [@_rdo's tweets](https://twitter.com/_rdo).
<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'>3176</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'>1144</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'>310</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1722</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/1raumdp7/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @_rdo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/2pxbgnfy) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/2pxbgnfy/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/_rdo'</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 -->
|
huggingtweets/_phr3nzy
|
huggingtweets
| 2021-05-21T17:10:49Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/_phr3nzy/1607057644985/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1289641403207819265/Oo8L2MDk_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">osama 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@_phr3nzy 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 [@_phr3nzy's tweets](https://twitter.com/_phr3nzy).
<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'>2236</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'>1135</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'>155</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>946</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2cywo7wq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @_phr3nzy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ijwy08u) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ijwy08u/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/_phr3nzy'</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 -->
|
huggingtweets/_marfii
|
huggingtweets
| 2021-05-21T17:07:37Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/_marfii/1616720799370/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1370199330112557057/IHw8xP8m_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">marf 🤖 AI Bot </div>
<div style="font-size: 15px">@_marfii 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 [@_marfii's tweets](https://twitter.com/_marfii).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3248 |
| Retweets | 144 |
| Short tweets | 872 |
| Tweets kept | 2232 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/15dkgbba/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @_marfii's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3bq89iuq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3bq89iuq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/_marfii')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/_alexhirsch
|
huggingtweets
| 2021-05-21T16:53:35Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/_alexhirsch/1616542840091/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/661330385465245696/3rnsJokZ_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Alex Hirsch 🤖 AI Bot </div>
<div style="font-size: 15px">@_alexhirsch 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 [@_alexhirsch's tweets](https://twitter.com/_alexhirsch).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3187 |
| Retweets | 240 |
| Short tweets | 450 |
| Tweets kept | 2497 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1go2kut1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @_alexhirsch's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1pe6iqi8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1pe6iqi8/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/_alexhirsch')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/__stillpoint
|
huggingtweets
| 2021-05-21T16:50:56Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/__stillpoint/1616690567975/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1276525118454468608/dT_52Uvg_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Dan Bennett 🤖 AI Bot </div>
<div style="font-size: 15px">@__stillpoint 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 [@__stillpoint's tweets](https://twitter.com/__stillpoint).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3237 |
| Retweets | 853 |
| Short tweets | 160 |
| Tweets kept | 2224 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3iopt694/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @__stillpoint's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/27mgd31u) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/27mgd31u/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/__stillpoint')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/__solnychko
|
huggingtweets
| 2021-05-21T16:49:49Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/__solnychko/1616680322908/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1360367235408224263/AwK6rgAZ_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Sophia 🤖 AI Bot </div>
<div style="font-size: 15px">@__solnychko 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 [@__solnychko's tweets](https://twitter.com/__solnychko).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3206 |
| Retweets | 1278 |
| Short tweets | 208 |
| Tweets kept | 1720 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3aglnv5r/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @__solnychko's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/z5yw4btx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/z5yw4btx/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/__solnychko')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/____devii
|
huggingtweets
| 2021-05-21T16:46:07Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/____devii/1614135668330/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1345142719124021248/z5rYY2JE_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Devii 🤖 AI Bot </div>
<div style="font-size: 15px">@____devii 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 [@____devii's tweets](https://twitter.com/____devii).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3084 |
| Retweets | 1521 |
| Short tweets | 288 |
| Tweets kept | 1275 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ffmdq5y/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @____devii's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3bjpt7k3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3bjpt7k3/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/____devii')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/Question
|
huggingtweets
| 2021-05-21T16:44:50Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1282836681914085378/PGX9pn9g_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Milanote 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@milanoteapp 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 [@milanoteapp's tweets](https://twitter.com/milanoteapp).
<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'>831</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'>63</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'>28</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>740</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/15katy2a/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @milanoteapp's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1y624qmh) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1y624qmh/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/milanoteapp'</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 -->
|
huggingtweets/666ouz666
|
huggingtweets
| 2021-05-21T16:43:13Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/666ouz666/1606428014311/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1328826930049789953/EWpTLaQR_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Oğuzhan 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@666ouz666 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 [@666ouz666's tweets](https://twitter.com/666ouz666).
<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'>2816</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'>63</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'>389</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2364</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3e6nphcq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @666ouz666's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/5hsj1s8v) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/5hsj1s8v/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/666ouz666'</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 -->
|
huggingtweets/4pfviolet
|
huggingtweets
| 2021-05-21T16:40:36Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/4pfviolet/1618177760300/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1369684381183451136/khVtCHFo_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">violet 🤖 AI Bot </div>
<div style="font-size: 15px">@4pfviolet 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 [@4pfviolet's tweets](https://twitter.com/4pfviolet).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3234 |
| Retweets | 491 |
| Short tweets | 903 |
| Tweets kept | 1840 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ekxu80u/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @4pfviolet's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1dlks43a) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1dlks43a/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/4pfviolet')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/423zb
|
huggingtweets
| 2021-05-21T16:38:25Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/423zb/1612221398403/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1277051021064392706/wuQS0nyO_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">423ZB 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@423zb 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 [@423zb's tweets](https://twitter.com/423zb).
<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'>3166</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'>2425</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'>144</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>597</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/jnwkepoo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @423zb's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/29x1ggo7) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/29x1ggo7/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/423zb'</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)
|
huggingtweets/3thyr3al
|
huggingtweets
| 2021-05-21T16:37:14Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/3thyr3al/1617942034431/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1362160113247793153/VEYzwQTI_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">ethy (3thyreඞl)🏺 🤖 AI Bot </div>
<div style="font-size: 15px">@3thyr3al 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 [@3thyr3al's tweets](https://twitter.com/3thyr3al).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 1727 |
| Retweets | 360 |
| Short tweets | 539 |
| Tweets kept | 828 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2tr059nk/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @3thyr3al's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/m9xvw9pq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/m9xvw9pq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/3thyr3al')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/3rbunn1nja
|
huggingtweets
| 2021-05-21T16:32:07Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/3rbunn1nja/1616808238654/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1371476407767957505/xfhZ00Hv_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Jeremy Spradlin 🤖 AI Bot </div>
<div style="font-size: 15px">@3rbunn1nja 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 [@3rbunn1nja's tweets](https://twitter.com/3rbunn1nja).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3251 |
| Retweets | 121 |
| Short tweets | 252 |
| Tweets kept | 2878 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2fqh91fk/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @3rbunn1nja's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3lk04zqn) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3lk04zqn/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/3rbunn1nja')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/14werewolfvevo
|
huggingtweets
| 2021-05-21T16:28:48Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/14werewolfvevo/1617769919321/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1343113335882063873/mITxI5OI_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">SIKA MODE | BLM 🤖 AI Bot </div>
<div style="font-size: 15px">@14werewolfvevo 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 [@14werewolfvevo's tweets](https://twitter.com/14werewolfvevo).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3229 |
| Retweets | 170 |
| Short tweets | 798 |
| Tweets kept | 2261 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ymsdw3a/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @14werewolfvevo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1iypm80s) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1iypm80s/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/14werewolfvevo')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/12123i123i12345
|
huggingtweets
| 2021-05-21T16:22:22Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/12123i123i12345/1617760753400/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1377780722883174400/4gq8ntlP_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">parallellax 🤖 AI Bot </div>
<div style="font-size: 15px">@12123i123i12345 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 [@12123i123i12345's tweets](https://twitter.com/12123i123i12345).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2362 |
| Retweets | 310 |
| Short tweets | 283 |
| Tweets kept | 1769 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/e91cv8fo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @12123i123i12345's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ncn8t24f) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ncn8t24f/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/12123i123i12345')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/09indierock
|
huggingtweets
| 2021-05-21T16:21:05Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/09indierock/1616791178582/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1363688455352553473/nfQUoTBH_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">kn 🤖 AI Bot </div>
<div style="font-size: 15px">@09indierock 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 [@09indierock's tweets](https://twitter.com/09indierock).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3126 |
| Retweets | 1094 |
| Short tweets | 428 |
| Tweets kept | 1604 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/39findw6/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @09indierock's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/33xy9nxb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/33xy9nxb/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/09indierock')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
gaochangkuan/model_dir
|
gaochangkuan
| 2021-05-21T16:10:50Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
## Generating Chinese poetry by topic.
```python
from transformers import *
tokenizer = BertTokenizer.from_pretrained("gaochangkuan/model_dir")
model = AutoModelWithLMHead.from_pretrained("gaochangkuan/model_dir")
prompt= '''<s>田园躬耕'''
length= 84
stop_token='</s>'
temperature = 1.2
repetition_penalty=1.3
k= 30
p= 0.95
device ='cuda'
seed=2020
no_cuda=False
prompt_text = prompt if prompt else input("Model prompt >>> ")
encoded_prompt = tokenizer.encode(
'<s>'+prompt_text+'<sep>',
add_special_tokens=False,
return_tensors="pt"
)
encoded_prompt = encoded_prompt.to(device)
output_sequences = model.generate(
input_ids=encoded_prompt,
max_length=length,
min_length=10,
do_sample=True,
early_stopping=True,
num_beams=10,
temperature=temperature,
top_k=k,
top_p=p,
repetition_penalty=repetition_penalty,
bad_words_ids=None,
bos_token_id=tokenizer.bos_token_id,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
length_penalty=1.2,
no_repeat_ngram_size=2,
num_return_sequences=1,
attention_mask=None,
decoder_start_token_id=tokenizer.bos_token_id,)
generated_sequence = output_sequences[0].tolist()
text = tokenizer.decode(generated_sequence)
text = text[: text.find(stop_token) if stop_token else None]
print(''.join(text).replace(' ','').replace('<pad>','').replace('<s>',''))
```
|
gagan3012/rap-writer
|
gagan3012
| 2021-05-21T16:09:53Z | 8 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
# Generating Rap song Lyrics like Eminem Using GPT2
### I have built a custom model for it using data from Kaggle
Creating a new finetuned model using data lyrics from leading hip-hop stars
### My model can be accessed at: gagan3012/rap-writer
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gagan3012/rap-writer")
model = AutoModelWithLMHead.from_pretrained("gagan3012/rap-writer")
```
|
gagan3012/project-code-py
|
gagan3012
| 2021-05-21T16:08:09Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
# Leetcode using AI :robot:
GPT-2 Model for Leetcode Questions in python
**Note**: the Answers might not make sense in some cases because of the bias in GPT-2
**Contribtuions:** If you would like to make the model better contributions are welcome Check out [CONTRIBUTIONS.md](https://github.com/gagan3012/project-code-py/blob/master/CONTRIBUTIONS.md)
### 📢 Favour:
It would be highly motivating, if you can STAR⭐ this repo if you find it helpful.
## Model
Two models have been developed for different use cases and they can be found at https://huggingface.co/gagan3012
The model weights can be found here: [GPT-2](https://huggingface.co/gagan3012/project-code-py) and [DistilGPT-2](https://huggingface.co/gagan3012/project-code-py-small)
### Example usage:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gagan3012/project-code-py")
model = AutoModelWithLMHead.from_pretrained("gagan3012/project-code-py")
```
## Demo
[](https://share.streamlit.io/gagan3012/project-code-py/app.py)
A streamlit webapp has been setup to use the model: https://share.streamlit.io/gagan3012/project-code-py/app.py
## Example results:
### Question:
```
Write a function to delete a node in a singly-linked list. You will not be given access to the head of the list, instead you will be given access to the node to be deleted directly. It is guaranteed that the node to be deleted is not a tail node in the list.
```
### Answer:
```python
""" Write a function to delete a node in a singly-linked list. You will not be given access to the head of the list, instead you will be given access to the node to be deleted directly. It is guaranteed that the node to be deleted is not a tail node in the list.
For example,
a = 1->2->3
b = 3->1->2
t = ListNode(-1, 1)
Note: The lexicographic ordering of the nodes in a tree matters. Do not assign values to nodes in a tree.
Example 1:
Input: [1,2,3]
Output: 1->2->5
Explanation: 1->2->3->3->4, then 1->2->5[2] and then 5->1->3->4.
Note:
The length of a linked list will be in the range [1, 1000].
Node.val must be a valid LinkedListNode type.
Both the length and the value of the nodes in a linked list will be in the range [-1000, 1000].
All nodes are distinct.
"""
# Definition for singly-linked list.
# class ListNode:
# def __init__(self, x):
# self.val = x
# self.next = None
class Solution:
def deleteNode(self, head: ListNode, val: int) -> None:
"""
BFS
Linked List
:param head: ListNode
:param val: int
:return: ListNode
"""
if head is not None:
return head
dummy = ListNode(-1, 1)
dummy.next = head
dummy.next.val = val
dummy.next.next = head
dummy.val = ""
s1 = Solution()
print(s1.deleteNode(head))
print(s1.deleteNode(-1))
print(s1.deleteNode(-1))
```
|
gagan3012/Fox-News-Generator
|
gagan3012
| 2021-05-21T16:03:28Z | 7 | 3 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
# Generating Right Wing News Using GPT2
### I have built a custom model for it using data from Kaggle
Creating a new finetuned model using data from FOX news
### My model can be accessed at gagan3012/Fox-News-Generator
Check the [BenchmarkTest](https://github.com/gagan3012/Fox-News-Generator/blob/master/BenchmarkTest.ipynb) notebook for results
Find the model at [gagan3012/Fox-News-Generator](https://huggingface.co/gagan3012/Fox-News-Generator)
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gagan3012/Fox-News-Generator")
model = AutoModelWithLMHead.from_pretrained("gagan3012/Fox-News-Generator")
```
|
erikinfo/gpt2TEDlectures
|
erikinfo
| 2021-05-21T16:00:10Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
# GPT2 Keyword Based Lecture Generator
## Model description
GPT2 fine-tuned on the TED Talks Dataset (published under the Creative Commons BY-NC-ND license).
## Intended uses
Used to generate spoken-word lectures.
### How to use
Input text:
<BOS> title <|SEP|> Some keywords <|SEP|>
Keyword Format: "Main Topic"."Subtopic1","Subtopic2","Subtopic3"
Code Example:
```
prompt = <BOS> + title + \\
<|SEP|> + keywords + <|SEP|>
generated = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0)
model.eval();
```
|
DebateLabKIT/cript
|
DebateLabKIT
| 2021-05-21T15:40:52Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"arxiv:2009.07185",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- gpt2
---
# CRiPT Model (Critical Thinking Intermediarily Pretrained Transformer)
Small version of the trained model (`SYL01-2020-10-24-72K/gpt2-small-train03-72K`) presented in the paper "Critical Thinking for Language Models" (Betz, Voigt and Richardson 2020). See also:
* [blog entry](https://debatelab.github.io/journal/critical-thinking-language-models.html)
* [GitHub repo](https://github.com/debatelab/aacorpus)
* [paper](https://arxiv.org/pdf/2009.07185)
|
DebateLabKIT/cript-medium
|
DebateLabKIT
| 2021-05-21T15:39:12Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"arxiv:2009.07185",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- gpt2
---
# CRiPT Model Medium (Critical Thinking Intermediarily Pretrained Transformer)
Medium version of the trained model (`SYL01-2020-10-24-72K/gpt2-medium-train03-72K`) presented in the paper "Critical Thinking for Language Models" (Betz, Voigt and Richardson 2020). See also:
* [blog entry](https://debatelab.github.io/journal/critical-thinking-language-models.html)
* [GitHub repo](https://github.com/debatelab/aacorpus)
* [paper](https://arxiv.org/pdf/2009.07185)
|
bolbolzaban/gpt2-persian
|
bolbolzaban
| 2021-05-21T14:23:14Z | 883 | 27 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"farsi",
"persian",
"fa",
"doi:10.57967/hf/1207",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: fa
license: apache-2.0
tags:
- farsi
- persian
---
# GPT2-Persian
bolbolzaban/gpt2-persian is gpt2 language model that is trained with hyper parameters similar to standard gpt2-medium with following differences:
1. The context size is reduced from 1024 to 256 sub words in order to make the training affordable
2. Instead of BPE, google sentence piece tokenizor is used for tokenization.
3. The training dataset only include Persian text. All non-persian characters are replaced with especial tokens (e.g [LAT], [URL], [NUM])
Please refer to this [blog post](https://medium.com/@khashei/a-not-so-dangerous-ai-in-the-persian-language-39172a641c84) for further detail.
Also try the model [here](https://huggingface.co/bolbolzaban/gpt2-persian?text=%D8%AF%D8%B1+%DB%8C%DA%A9+%D8%A7%D8%AA%D9%81%D8%A7%D9%82+%D8%B4%DA%AF%D9%81%D8%AA+%D8%A7%D9%86%DA%AF%DB%8C%D8%B2%D8%8C+%D9%BE%DA%98%D9%88%D9%87%D8%B4%DA%AF%D8%B1%D8%A7%D9%86) or on [Bolbolzaban.com](http://www.bolbolzaban.com/text).
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline, AutoTokenizer, GPT2LMHeadModel
tokenizer = AutoTokenizer.from_pretrained('bolbolzaban/gpt2-persian')
model = GPT2LMHeadModel.from_pretrained('bolbolzaban/gpt2-persian')
generator = pipeline('text-generation', model, tokenizer=tokenizer, config={'max_length':256})
sample = generator('در یک اتفاق شگفت انگیز، پژوهشگران')
```
If you are using Tensorflow import TFGPT2LMHeadModel instead of GPT2LMHeadModel.
## Fine-tuning
Find a basic fine-tuning example on this [Github Repo](https://github.com/khashei/bolbolzaban-gpt2-persian).
## Special Tokens
gpt-persian is trained for the purpose of research on Persian poetry. Because of that all english words and numbers are replaced with special tokens and only standard Persian alphabet is used as part of input text. Here is one example:
Original text: اگر آیفون یا آیپد شما دارای سیستم عامل iOS 14.3 یا iPadOS 14.3 یا نسخههای جدیدتر باشد
Text used in training: اگر آیفون یا آیپد شما دارای سیستم عامل [LAT] [NUM] یا [LAT] [NUM] یا نسخههای جدیدتر باشد
Please consider normalizing your input text using [Hazm](https://github.com/sobhe/hazm) or similar libraries and ensure only Persian characters are provided as input.
If you want to use classical Persian poetry as input use [BOM] (begining of mesra) at the beginning of each verse (مصرع) followed by [EOS] (end of statement) at the end of each couplet (بیت).
See following links for example:
[[BOM] توانا بود](https://huggingface.co/bolbolzaban/gpt2-persian?text=%5BBOM%5D+%D8%AA%D9%88%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF)
[[BOM] توانا بود هر که دانا بود [BOM]](https://huggingface.co/bolbolzaban/gpt2-persian?text=%5BBOM%5D+%D8%AA%D9%88%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF+%D9%87%D8%B1+%DA%A9%D9%87+%D8%AF%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF+%5BBOM%5D)
[[BOM] توانا بود هر که دانا بود [BOM] ز دانش دل پیر](https://huggingface.co/bolbolzaban/gpt2-persian?text=%5BBOM%5D+%D8%AA%D9%88%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF+%D9%87%D8%B1+%DA%A9%D9%87+%D8%AF%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF+%5BBOM%5D+%D8%B2+%D8%AF%D8%A7%D9%86%D8%B4+%D8%AF%D9%84+%D9%BE%DB%8C%D8%B1)
[[BOM] توانا بود هر که دانا بود [BOM] ز دانش دل پیربرنا بود [EOS]](https://huggingface.co/bolbolzaban/gpt2-persian?text=%5BBOM%5D+%D8%AA%D9%88%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF+%D9%87%D8%B1+%DA%A9%D9%87+%D8%AF%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF+%5BBOM%5D+%D8%B2+%D8%AF%D8%A7%D9%86%D8%B4+%D8%AF%D9%84+%D9%BE%DB%8C%D8%B1%D8%A8%D8%B1%D9%86%D8%A7+%D8%A8%D9%88%D8%AF++%5BEOS%5D)
If you like to know about structure of classical Persian poetry refer to these [blog posts](https://medium.com/@khashei).
## Acknowledgment
This project is supported by Cloud TPUs from Google’s TensorFlow Research Cloud (TFRC).
## Citation and Reference
Please reference "bolbolzaban.com" website if you are using gpt2-persian in your research or commertial application.
## Contacts
Please reachout on [Linkedin](https://www.linkedin.com/in/khashei/) or [Telegram](https://t.me/khasheia) if you have any question or need any help to use the model.
Follow [Bolbolzaban](http://bolbolzaban.com/about) on [Twitter](https://twitter.com/bolbol_zaban), [Telegram](https://t.me/bolbol_zaban) or [Instagram](https://www.instagram.com/bolbolzaban/)
|
ainize/gpt2-spongebob-script-large
|
ainize
| 2021-05-21T12:18:42Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
### Model information
Fine tuning data: https://www.kaggle.com/mikhailgaerlan/spongebob-squarepants-completed-transcripts
License: CC-BY-SA
Base model: gpt-2 large
Epoch: 50
Train runtime: 14723.0716 secs
Loss: 0.0268
API page: [Ainize](https://ainize.ai/fpem123/GPT2-Spongebob?branch=master)
Demo page: [End-point](https://master-gpt2-spongebob-fpem123.endpoint.ainize.ai/)
### ===Teachable NLP=== ###
To train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the model here for free.
Teachable NLP: [Teachable NLP](https://ainize.ai/teachable-nlp)
Tutorial: [Tutorial](https://forum.ainetwork.ai/t/teachable-nlp-how-to-use-teachable-nlp/65?utm_source=community&utm_medium=huggingface&utm_campaign=model&utm_content=teachable%20nlp)
|
ainize/gpt2-rnm-with-spongebob
|
ainize
| 2021-05-21T12:09:02Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
### Model information
Fine tuning data 1: https://www.kaggle.com/andradaolteanu/rickmorty-scripts
Fine tuning data 2: https://www.kaggle.com/mikhailgaerlan/spongebob-squarepants-completed-transcripts
Base model: e-tony/gpt2-rnm
Epoch: 2
Train runtime: 790.0612 secs
Loss: 2.8569
API page: [Ainize](https://ainize.ai/fpem123/GPT2-Rick-N-Morty-with-SpongeBob?branch=master)
Demo page: [End-point](https://master-gpt2-rick-n-morty-with-sponge-bob-fpem123.endpoint.ainize.ai/)
### ===Teachable NLP=== ###
To train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the model here for free.
Teachable NLP: [Teachable NLP](https://ainize.ai/teachable-nlp)
Tutorial: [Tutorial](https://forum.ainetwork.ai/t/teachable-nlp-how-to-use-teachable-nlp/65?utm_source=community&utm_medium=huggingface&utm_campaign=model&utm_content=teachable%20nlp)
|
ainize/gpt2-rnm-with-only-rick
|
ainize
| 2021-05-21T12:06:44Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
### Model information
Fine tuning data 1: https://www.kaggle.com/andradaolteanu/rickmorty-scripts
Base model: e-tony/gpt2-rnm
Epoch: 1
Train runtime: 3.4982 secs
Loss: 3.0894
Training notebook: [Colab](https://colab.research.google.com/drive/1RawVxulLETFicWMY0YANUdP-H-e7Eeyc)
### ===Teachable NLP=== ###
To train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the model here for free.
Teachable NLP: [Teachable NLP](https://ainize.ai/teachable-nlp)
Tutorial: [Tutorial](https://forum.ainetwork.ai/t/teachable-nlp-how-to-use-teachable-nlp/65?utm_source=community&utm_medium=huggingface&utm_campaign=model&utm_content=teachable%20nlp)
|
TheBakerCat/2chan_ruGPT3_small
|
TheBakerCat
| 2021-05-21T11:26:24Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
ruGPT3-small model, trained on some 2chan posts
|
SIC98/GPT2-python-code-generator
|
SIC98
| 2021-05-21T11:13:58Z | 17 | 9 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
Github
- https://github.com/SIC98/GPT2-python-code-generator
|
Ochiroo/tiny_mn_gpt
|
Ochiroo
| 2021-05-21T10:59:47Z | 6 | 1 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"mn",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language: mn
---
# GPT2-Mongolia
## Model description
GPT-2 is a transformers model pretrained on a very small corpus of Mongolian news data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences.
## How to use
```python
import tensorflow as tf
from transformers import GPT2Config, TFGPT2LMHeadModel, GPT2Tokenizer
from transformers import WEIGHTS_NAME, CONFIG_NAME
tokenizer = GPT2Tokenizer.from_pretrained('Ochiroo/tiny_mn_gpt')
model = TFGPT2LMHeadModel.from_pretrained('Ochiroo/tiny_mn_gpt')
text = "Намайг Эрдэнэ-Очир гэдэг. Би"
input_ids = tokenizer.encode(text, return_tensors='tf')
beam_outputs = model.generate(
input_ids,
max_length = 25,
num_beams = 5,
temperature = 0.7,
no_repeat_ngram_size=2,
num_return_sequences=5
)
print(tokenizer.decode(beam_outputs[0]))
```
## Training data and biases
Trained on 500MB of Mongolian news dataset (IKON) on RTX 2060.
|
HooshvareLab/gpt2-fa-poetry
|
HooshvareLab
| 2021-05-21T10:50:14Z | 65 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language: fa
license: apache-2.0
widget:
- text: "<s>رودکی<|startoftext|>"
- text: "<s>فردوسی<|startoftext|>"
- text: "<s>خیام<|startoftext|>"
- text: "<s>عطار<|startoftext|>"
- text: "<s>نظامی<|startoftext|>"
---
# Persian Poet GPT2
## Poets
The model can generate poetry based on your favorite poet, and you need to add one of the following lines as the input the box on the right side or follow the [fine-tuning notebook](https://colab.research.google.com/github/hooshvare/parsgpt/blob/master/notebooks/Persian_Poetry_FineTuning.ipynb).
```text
<s>رودکی<|startoftext|>
<s>فردوسی<|startoftext|>
<s>کسایی<|startoftext|>
<s>ناصرخسرو<|startoftext|>
<s>منوچهری<|startoftext|>
<s>فرخی سیستانی<|startoftext|>
<s>مسعود سعد سلمان<|startoftext|>
<s>ابوسعید ابوالخیر<|startoftext|>
<s>باباطاهر<|startoftext|>
<s>فخرالدین اسعد گرگانی<|startoftext|>
<s>اسدی توسی<|startoftext|>
<s>هجویری<|startoftext|>
<s>خیام<|startoftext|>
<s>نظامی<|startoftext|>
<s>عطار<|startoftext|>
<s>سنایی<|startoftext|>
<s>خاقانی<|startoftext|>
<s>انوری<|startoftext|>
<s>عبدالواسع جبلی<|startoftext|>
<s>نصرالله منشی<|startoftext|>
<s>مهستی گنجوی<|startoftext|>
<s>باباافضل کاشانی<|startoftext|>
<s>مولوی<|startoftext|>
<s>سعدی<|startoftext|>
<s>خواجوی کرمانی<|startoftext|>
<s>عراقی<|startoftext|>
<s>سیف فرغانی<|startoftext|>
<s>حافظ<|startoftext|>
<s>اوحدی<|startoftext|>
<s>شیخ محمود شبستری<|startoftext|>
<s>عبید زاکانی<|startoftext|>
<s>امیرخسرو دهلوی<|startoftext|>
<s>سلمان ساوجی<|startoftext|>
<s>شاه نعمتالله ولی<|startoftext|>
<s>جامی<|startoftext|>
<s>هلالی جغتایی<|startoftext|>
<s>وحشی<|startoftext|>
<s>محتشم کاشانی<|startoftext|>
<s>شیخ بهایی<|startoftext|>
<s>عرفی<|startoftext|>
<s>رضیالدین آرتیمانی<|startoftext|>
<s>صائب تبریزی<|startoftext|>
<s>فیض کاشانی<|startoftext|>
<s>بیدل دهلوی<|startoftext|>
<s>هاتف اصفهانی<|startoftext|>
<s>فروغی بسطامی<|startoftext|>
<s>قاآنی<|startoftext|>
<s>ملا هادی سبزواری<|startoftext|>
<s>پروین اعتصامی<|startoftext|>
<s>ملکالشعرای بهار<|startoftext|>
<s>شهریار<|startoftext|>
<s>رهی معیری<|startoftext|>
<s>اقبال لاهوری<|startoftext|>
<s>خلیلالله خلیلی<|startoftext|>
<s>شاطرعباس صبوحی<|startoftext|>
<s>نیما یوشیج ( آوای آزاد )<|startoftext|>
<s>احمد شاملو<|startoftext|>
<s>سهراب سپهری<|startoftext|>
<s>فروغ فرخزاد<|startoftext|>
<s>سیمین بهبهانی<|startoftext|>
<s>مهدی اخوان ثالث<|startoftext|>
<s>محمدحسن بارق شفیعی<|startoftext|>
<s>شیون فومنی<|startoftext|>
<s>کامبیز صدیقی کسمایی<|startoftext|>
<s>بهرام سالکی<|startoftext|>
<s>عبدالقهّار عاصی<|startoftext|>
<s>اِ لیـــار (جبار محمدی )<|startoftext|>
```
## Questions?
Post a Github issue on the [ParsGPT2 Issues](https://github.com/hooshvare/parsgpt/issues) repo.
|
HooshvareLab/gpt2-fa-comment
|
HooshvareLab
| 2021-05-21T10:47:25Z | 30 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language: fa
license: apache-2.0
widget:
- text: "<s>نمونه دیدگاه هم خوب هم بد به طور کلی <sep>"
- text: "<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و طعم <sep>"
- text: "<s>نمونه دیدگاه خوب از نظر بازی و کارگردانی <sep>"
- text: "<s>نمونه دیدگاه خیلی خوب از نظر بازی و صحنه و داستان <sep>"
- text: "<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و طعم و کیفیت <sep>"
---
# Persian Comment Generator
The model can generate comments based on your aspects, and the model was fine-tuned on [persiannlp/parsinlu](https://github.com/persiannlp/parsinlu). Currently, the model only supports aspects in the food and movie scope. You can see the whole aspects in the following section.
## Comments Aspects
```text
<s>نمونه دیدگاه هم خوب هم بد به طور کلی <sep>
<s>نمونه دیدگاه خوب به طور کلی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر ارزش غذایی و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر طعم <sep>
<s>نمونه دیدگاه خیلی خوب به طور کلی <sep>
<s>نمونه دیدگاه خوب از نظر بسته بندی <sep>
<s>نمونه دیدگاه منفی از نظر کیفیت و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارسال و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و طعم <sep>
<s>نمونه دیدگاه منفی به طور کلی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر ارسال <sep>
<s>نمونه دیدگاه منفی از نظر طعم <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و ارزش خرید <sep>
<s>نمونه دیدگاه نظری ندارم به طور کلی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم <sep>
<s>نمونه دیدگاه خیلی منفی به طور کلی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و کیفیت و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر طعم و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر طعم و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارسال <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و طعم <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بسته بندی و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و بسته بندی و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و بسته بندی و ارسال <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر طعم و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر طعم و کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر ارسال و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و بسته بندی و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه منفی از نظر ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و طعم <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و طعم <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر طعم و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر طعم و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی و ارسال و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و بسته بندی و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی و کیفیت و طعم <sep>
<s>نمونه دیدگاه خوب از نظر ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و بسته بندی و ارزش غذایی و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش غذایی و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر طعم و ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر بسته بندی و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش غذایی و طعم <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش غذایی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش غذایی و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارسال <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و طعم <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش غذایی و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و کیفیت و ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و بسته بندی و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر بسته بندی و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر ارسال و طعم <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و ارسال <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش غذایی و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و طعم و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بسته بندی و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و کیفیت و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و کیفیت و ارزش خرید و بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش غذایی و ارسال <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و طعم و ارزش خرید و ارسال <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارسال و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارسال و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و ارزش خرید و ارسال <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خوب از نظر بسته بندی و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بسته بندی و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و بسته بندی و ارسال <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بسته بندی و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه نظری ندارم از نظر بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و بسته بندی و طعم <sep>
<s>نمونه دیدگاه خوب از نظر طعم و بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و ارزش خرید و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و ارزش غذایی <sep>
<s>نمونه دیدگاه منفی از نظر طعم و بسته بندی <sep>
<s>نمونه دیدگاه منفی از نظر کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارزش غذایی و بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر ارسال و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارسال <sep>
<s>نمونه دیدگاه نظری ندارم از نظر طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه منفی از نظر ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارسال و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و کیفیت و بسته بندی و ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر طعم و بسته بندی و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و کیفیت و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارسال و ارزش خرید <sep>
<s>نمونه دیدگاه نظری ندارم از نظر ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارسال و ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارسال و بسته بندی <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی و ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارسال و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و ارسال <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش خرید و ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش غذایی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و ارزش غذایی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارسال و بسته بندی و کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی و طعم <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی و ارزش غذایی <sep>
<s>نمونه دیدگاه منفی از نظر طعم و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و ارزش غذایی و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش غذایی و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و طعم و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش خرید و کیفیت و طعم <sep>
<s>نمونه دیدگاه منفی از نظر ارزش خرید و کیفیت و طعم <sep>
<s>نمونه دیدگاه منفی از نظر کیفیت و طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارسال و کیفیت و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش غذایی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و بسته بندی و ارسال <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش غذایی و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش غذایی و کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر ارزش خرید و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و طعم و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارسال و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و ارسال <sep>
<s>نمونه دیدگاه منفی از نظر موسیقی و بازی <sep>
<s>نمونه دیدگاه منفی از نظر داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر صدا <sep>
<s>نمونه دیدگاه خیلی منفی از نظر داستان <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و فیلمبرداری و کارگردانی و بازی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بازی <sep>
<s>نمونه دیدگاه منفی از نظر داستان و بازی <sep>
<s>نمونه دیدگاه منفی از نظر بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر داستان و کارگردانی و بازی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر داستان و بازی <sep>
<s>نمونه دیدگاه خوب از نظر بازی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بازی و داستان و کارگردانی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی <sep>
<s>نمونه دیدگاه خوب از نظر بازی و داستان <sep>
<s>نمونه دیدگاه خوب از نظر داستان و بازی <sep>
<s>نمونه دیدگاه خوب از نظر داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر داستان و بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و داستان <sep>
<s>نمونه دیدگاه خیلی منفی از نظر داستان و کارگردانی و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بازی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کارگردانی <sep>
<s>نمونه دیدگاه منفی از نظر کارگردانی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و بازی <sep>
<s>نمونه دیدگاه خوب از نظر کارگردانی و بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر صحنه و کارگردانی <sep>
<s>نمونه دیدگاه منفی از نظر بازی و کارگردانی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و داستان و کارگردانی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و کارگردانی و فیلمبرداری و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و بازی و موسیقی <sep>
<s>نمونه دیدگاه خوب از نظر صحنه و بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و موسیقی و کارگردانی <sep>
<s>نمونه دیدگاه خوب از نظر داستان و کارگردانی <sep>
<s>نمونه دیدگاه خوب از نظر بازی و کارگردانی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بازی و کارگردانی <sep>
<s>نمونه دیدگاه منفی از نظر کارگردانی و موسیقی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بازی و داستان <sep>
<s>نمونه دیدگاه خوب از نظر کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بازی و کارگردانی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و داستان <sep>
<s>نمونه دیدگاه خیلی منفی از نظر داستان و کارگردانی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر داستان و کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان <sep>
<s>نمونه دیدگاه خوب از نظر بازی و داستان و موسیقی و کارگردانی و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی منفی از نظر داستان و بازی و کارگردانی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بازی و داستان <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و بازی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و بازی و کارگردانی <sep>
<s>نمونه دیدگاه منفی از نظر بازی و داستان <sep>
<s>نمونه دیدگاه خوب از نظر فیلمبرداری و صحنه و موسیقی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و کارگردانی و بازی <sep>
<s>نمونه دیدگاه نظری ندارم از نظر بازی <sep>
<s>نمونه دیدگاه منفی از نظر داستان و کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و بازی و صحنه <sep>
<s>نمونه دیدگاه خوب از نظر کارگردانی و داستان و بازی و فیلمبرداری <sep>
<s>نمونه دیدگاه خوب از نظر بازی و صحنه و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و صحنه و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و موسیقی و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و صحنه <sep>
<s>نمونه دیدگاه خیلی خوب از نظر فیلمبرداری و صحنه و داستان و کارگردانی <sep>
<s>نمونه دیدگاه منفی از نظر کارگردانی و بازی <sep>
<s>نمونه دیدگاه منفی از نظر کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و بازی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر فیلمبرداری و بازی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و بازی و داستان و صحنه <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر موسیقی و کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کارگردانی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر موسیقی و صحنه <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر صحنه و فیلمبرداری و داستان و بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و داستان و موسیقی و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کارگردانی و صدا و صحنه و داستان <sep>
<s>نمونه دیدگاه خوب از نظر داستان و کارگردانی و بازی <sep>
<s>نمونه دیدگاه منفی از نظر داستان و بازی و کارگردانی <sep>
<s>نمونه دیدگاه خوب از نظر داستان و بازی و موسیقی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کارگردانی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کارگردانی و بازی و صحنه <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کارگردانی و بازی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر صحنه و فیلمبرداری و داستان <sep>
<s>نمونه دیدگاه خوب از نظر موسیقی و داستان <sep>
<s>نمونه دیدگاه منفی از نظر موسیقی و بازی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر صدا و بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و صحنه و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بازی و فیلمبرداری و داستان و کارگردانی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر صحنه <sep>
<s>نمونه دیدگاه منفی از نظر داستان و صحنه <sep>
<s>نمونه دیدگاه منفی از نظر بازی و صحنه و صدا <sep>
<s>نمونه دیدگاه خیلی منفی از نظر فیلمبرداری و صدا <sep>
<s>نمونه دیدگاه خیلی خوب از نظر موسیقی <sep>
<s>نمونه دیدگاه خوب از نظر بازی و کارگردانی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و فیلمبرداری و موسیقی و کارگردانی و داستان <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر فیلمبرداری و داستان و بازی <sep>
<s>نمونه دیدگاه منفی از نظر صحنه و فیلمبرداری و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و کارگردانی و داستان <sep>
```
## Questions?
Post a Github issue on the [ParsGPT2 Issues](https://github.com/hooshvare/parsgpt/issues) repo.
|
HScomcom/gpt2-fairytales
|
HScomcom
| 2021-05-21T10:16:43Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
### Model information
Fine tuning data: https://www.kaggle.com/cuddlefish/fairy-tales
License: CC0: Public Domain
Base model: gpt-2 large
Epoch: 30
Train runtime: 17861.6048 secs
Loss: 0.0412
API page: [Ainize](https://ainize.ai/fpem123/GPT2-FairyTales?branch=master)
Demo page: [End-point](https://master-gpt2-fairy-tales-fpem123.endpoint.ainize.ai/)
### ===Teachable NLP=== ###
To train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the model here for free.
Teachable NLP: [Teachable NLP](https://ainize.ai/teachable-nlp)
Tutorial: [Tutorial](https://forum.ainetwork.ai/t/teachable-nlp-how-to-use-teachable-nlp/65?utm_source=community&utm_medium=huggingface&utm_campaign=model&utm_content=teachable%20nlp)
And my other fairytale model: [showcase](https://forum.ainetwork.ai/t/teachable-nlp-gpt-2-fairy-tales/68)
|
Davlan/mt5_base_eng_yor_mt
|
Davlan
| 2021-05-21T10:14:10Z | 54 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2103.08647",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# mT5_base_eng_yor_mt
## Model description
**mT5_base_yor_eng_mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned mT5-base model. It establishes a **strong baseline** for automatically translating texts from English to Yorùbá.
Specifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for MT.
```python
from transformers import MT5ForConditionalGeneration, T5Tokenizer
model = MT5ForConditionalGeneration.from_pretrained("Davlan/mt5_base_eng_yor_mt")
tokenizer = T5Tokenizer.from_pretrained("google/mt5-base")
input_string = "Where are you?"
inputs = tokenizer.encode(input_string, return_tensors="pt")
generated_tokens = model.generate(inputs)
results = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(results)
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
9.82 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647)
### BibTeX entry and citation info
By David Adelani
```
```
|
HScomcom/gpt2-MyLittlePony
|
HScomcom
| 2021-05-21T10:09:36Z | 12 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
The model that generates the My little pony script
Fine tuning data: [Kaggle](https://www.kaggle.com/liury123/my-little-pony-transcript?select=clean_dialog.csv)
API page: [Ainize](https://ainize.ai/fpem123/GPT2-MyLittlePony)
Demo page: [End point](https://master-gpt2-my-little-pony-fpem123.endpoint.ainize.ai/)
### Model information
Base model: gpt-2 large
Epoch: 30
Train runtime: 4943.9641 secs
Loss: 0.0291
###===Teachable NLP===
To train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the model here for free.
Teachable NLP: [Teachable NLP](https://ainize.ai/teachable-nlp)
Tutorial: [Tutorial](https://forum.ainetwork.ai/t/teachable-nlp-how-to-use-teachable-nlp/65?utm_source=community&utm_medium=huggingface&utm_campaign=model&utm_content=teachable%20nlp)
|
HJK/PickupLineGenerator
|
HJK
| 2021-05-21T10:05:21Z | 12 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
basically, it makes pickup lines
https://huggingface.co/gpt2
|
lg/ghpy_40k
|
lg
| 2021-05-20T23:37:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# This model is probably not what you're looking for.
|
verissimomanoel/RobertaTwitterBR
|
verissimomanoel
| 2021-05-20T22:53:32Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
### Twitter RoBERTa BR
This is a RoBERTa Twitter in Portuguese model trained on ~7M tweets.
The results will be posted in the future.
### Example of using
```
tokenizer = AutoTokenizer.from_pretrained("verissimomanoel/RobertaTwitterBR")
model = AutoModel.from_pretrained("verissimomanoel/RobertaTwitterBR")
```
|
urduhack/roberta-urdu-small
|
urduhack
| 2021-05-20T22:52:23Z | 884 | 8 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"roberta-urdu-small",
"urdu",
"ur",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: ur
thumbnail: https://raw.githubusercontent.com/urduhack/urduhack/master/docs/_static/urduhack.png
tags:
- roberta-urdu-small
- urdu
- transformers
license: mit
---
## roberta-urdu-small
[](https://github.com/urduhack/urduhack/blob/master/LICENSE)
### Overview
**Language model:** roberta-urdu-small
**Model size:** 125M
**Language:** Urdu
**Training data:** News data from urdu news resources in Pakistan
### About roberta-urdu-small
roberta-urdu-small is a language model for urdu language.
```
from transformers import pipeline
fill_mask = pipeline("fill-mask", model="urduhack/roberta-urdu-small", tokenizer="urduhack/roberta-urdu-small")
```
## Training procedure
roberta-urdu-small was trained on urdu news corpus. Training data was normalized using normalization module from
urduhack to eliminate characters from other languages like arabic.
### About Urduhack
Urduhack is a Natural Language Processing (NLP) library for urdu language.
Github: https://github.com/urduhack/urduhack
|
tlemberger/sd-ner
|
tlemberger
| 2021-05-20T22:31:05Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"token-classification",
"token classification",
"dataset:EMBO/sd-panels",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- english
thumbnail:
tags:
- token classification
license:
datasets:
- EMBO/sd-panels
metrics:
-
---
# sd-ner
## Model description
This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang) and fine-tuned for token classification on the SourceData [sd-panels](https://huggingface.co/datasets/EMBO/sd-panels) dataset to perform Named Entity Recognition of bioentities.
## Intended uses & limitations
#### How to use
The intended use of this model is for Named Entity Recognition of biological entitie used in SourceData annotations (https://sourcedata.embo.org), including small molecules, gene products (genes and proteins), subcellular components, cell line and cell types, organ and tissues, species as well as experimental methods.
To have a quick check of the model:
```python
from transformers import pipeline, RobertaTokenizerFast, RobertaForTokenClassification
example = """<s> F. Western blot of input and eluates of Upf1 domains purification in a Nmd4-HA strain. The band with the # might corresponds to a dimer of Upf1-CH, bands marked with a star correspond to residual signal with the anti-HA antibodies (Nmd4). Fragments in the eluate have a smaller size because the protein A part of the tag was removed by digestion with the TEV protease. G6PDH served as a loading control in the input samples </s>"""
tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512)
model = RobertaForTokenClassification.from_pretrained('EMBO/sd-ner')
ner = pipeline('ner', model, tokenizer=tokenizer)
res = ner(example)
for r in res:
print(r['word'], r['entity'])
```
#### Limitations and bias
The model must be used with the `roberta-base` tokenizer.
## Training data
The model was trained for token classification using the [EMBO/sd-panels dataset](https://huggingface.co/datasets/EMBO/biolang) wich includes manually annotated examples.
## Training procedure
The training was run on a NVIDIA DGX Station with 4XTesla V100 GPUs.
Training code is available at https://github.com/source-data/soda-roberta
- Command: `python -m tokcl.train /data/json/sd_panels NER --num_train_epochs=3.5`
- Tokenizer vocab size: 50265
- Training data: EMBO/biolang MLM
- Training with 31410 examples.
- Evaluating on 8861 examples.
- Training on 15 features: O, I-SMALL_MOLECULE, B-SMALL_MOLECULE, I-GENEPROD, B-GENEPROD, I-SUBCELLULAR, B-SUBCELLULAR, I-CELL, B-CELL, I-TISSUE, B-TISSUE, I-ORGANISM, B-ORGANISM, I-EXP_ASSAY, B-EXP_ASSAY
- Epochs: 3.5
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 0.0001
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
## Eval results
On test set with `sklearn.metrics`:
```
precision recall f1-score support
CELL 0.77 0.81 0.79 3477
EXP_ASSAY 0.71 0.70 0.71 7049
GENEPROD 0.86 0.90 0.88 16140
ORGANISM 0.80 0.82 0.81 2759
SMALL_MOLECULE 0.78 0.82 0.80 4446
SUBCELLULAR 0.71 0.75 0.73 2125
TISSUE 0.70 0.75 0.73 1971
micro avg 0.79 0.82 0.81 37967
macro avg 0.76 0.79 0.78 37967
weighted avg 0.79 0.82 0.81 37967
```
|
thatdramebaazguy/roberta-base-wikimovies
|
thatdramebaazguy
| 2021-05-20T22:29:54Z | 4 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"roberta-base",
"masked-language-modeling",
"dataset:wikimovies",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
datasets:
- wikimovies
language:
- English
thumbnail:
tags:
- roberta
- roberta-base
- masked-language-modeling
license: cc-by-4.0
---
# roberta-base for MLM
```
model_name = "thatdramebaazguy/roberta-base-wikimovies"
pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="Fill-Mask")
```
## Overview
**Language model:** roberta-base
**Language:** English
**Downstream-task:** Fill-Mask
**Training data:** wikimovies
**Eval data:** wikimovies
**Infrastructure**: 2x Tesla v100
**Code:** See [example](https://github.com/adityaarunsinghal/Domain-Adaptation/blob/master/shell_scripts/train_movie_roberta.sh)
## Hyperparameters
```
num_examples = 4346
batch_size = 16
n_epochs = 3
base_LM_model = "roberta-base"
learning_rate = 5e-05
max_query_length=64
Gradient Accumulation steps = 1
Total optimization steps = 816
evaluation_strategy=IntervalStrategy.NO
prediction_loss_only=False
per_device_train_batch_size=8
per_device_eval_batch_size=8
adam_beta1=0.9
adam_beta2=0.999
adam_epsilon=1e-08,
max_grad_norm=1.0
lr_scheduler_type=SchedulerType.LINEAR
warmup_ratio=0.0
seed=42
eval_steps=500
metric_for_best_model=None
greater_is_better=None
label_smoothing_factor=0.0
```
## Performance
perplexity = 4.3808
Some of my work:
- [Domain-Adaptation Project](https://github.com/adityaarunsinghal/Domain-Adaptation/)
---
|
textattack/roberta-base-rotten_tomatoes
|
textattack
| 2021-05-20T22:18:23Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
## roberta-base fine-tuned with TextAttack on the rotten_tomatoes dataset
This `roberta-base` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 10 epochs with a batch size of 128, a learning
rate of 5e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.9033771106941839, as measured by the
eval set accuracy, found after 9 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/roberta-base-rotten-tomatoes
|
textattack
| 2021-05-20T22:17:29Z | 34 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `roberta-base` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 10 epochs with a batch size of 64, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.9033771106941839, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/roberta-base-imdb
|
textattack
| 2021-05-20T22:16:19Z | 207 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `roberta-base` model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.91436, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/roberta-base-ag-news
|
textattack
| 2021-05-20T22:15:20Z | 487 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model CardThis `roberta-base` model was fine-tuned for sequence classification using TextAttack
and the ag_news dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 5e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.9469736842105263, as measured by the
eval set accuracy, found after 4 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/roberta-base-RTE
|
textattack
| 2021-05-20T22:10:37Z | 122 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `roberta-base` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.7942238267148014, as measured by the
eval set accuracy, found after 3 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/roberta-base-MRPC
|
textattack
| 2021-05-20T22:07:47Z | 206 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `roberta-base` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 3e-05, and a maximum sequence length of 256.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.9117647058823529, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
pulp/CHILDES-ParentBERTo
|
pulp
| 2021-05-20T19:46:06Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
The language model trained on a fill-mask task with all the North American parent's data in CHILDES.
The parent's data can be found here: https://github.com/xiaomeng-ma/CHILDES
|
pradhyra/AWSBlogBert
|
pradhyra
| 2021-05-20T19:30:09Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
This model is pre-trained on blog articles from AWS Blogs.
## Pre-training corpora
The input text contains around 3000 blog articles on [AWS Blogs website](https://aws.amazon.com/blogs/) technical subject matter including AWS products, tools and tutorials.
## Pre-training details
I picked a Roberta architecture for masked language modeling (6-layer, 768-hidden, 12-heads, 82M parameters) and its corresponding ByteLevelBPE tokenization strategy. I then followed HuggingFace's Transformers [blog post](https://huggingface.co/blog/how-to-train) to train the model.
I chose to follow the following training set-up: 28k training steps with batches of 64 sequences of length 512 with an initial learning rate 5e-5. The model acheived a training loss of 3.6 on the MLM task over 10 epochs.
|
pedropei/question-intimacy
|
pedropei
| 2021-05-20T19:25:02Z | 28 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"en",
"autotrain_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
inference: false
---
|
osanseviero/upload-to-hub
|
osanseviero
| 2021-05-20T19:13:12Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
Example card
Second modification
|
nyu-mll/roberta-med-small-1M-3
|
nyu-mll
| 2021-05-20T19:09:09Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.
### Hyperparameters and Validation Perplexity
The hyperparameters and validation perplexities corresponding to each model are as follows:
| Model Name | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity |
|--------------------------|---------------|------------|-----------|------------|-----------------------|
| [roberta-base-1B-1][link-roberta-base-1B-1] | 1B | BASE | 100K | 512 | 3.93 |
| [roberta-base-1B-2][link-roberta-base-1B-2] | 1B | BASE | 31K | 1024 | 4.25 |
| [roberta-base-1B-3][link-roberta-base-1B-3] | 1B | BASE | 31K | 4096 | 3.84 |
| [roberta-base-100M-1][link-roberta-base-100M-1] | 100M | BASE | 100K | 512 | 4.99 |
| [roberta-base-100M-2][link-roberta-base-100M-2] | 100M | BASE | 31K | 1024 | 4.61 |
| [roberta-base-100M-3][link-roberta-base-100M-3] | 100M | BASE | 31K | 512 | 5.02 |
| [roberta-base-10M-1][link-roberta-base-10M-1] | 10M | BASE | 10K | 1024 | 11.31 |
| [roberta-base-10M-2][link-roberta-base-10M-2] | 10M | BASE | 10K | 512 | 10.78 |
| [roberta-base-10M-3][link-roberta-base-10M-3] | 10M | BASE | 31K | 512 | 11.58 |
| [roberta-med-small-1M-1][link-roberta-med-small-1M-1] | 1M | MED-SMALL | 100K | 512 | 153.38 |
| [roberta-med-small-1M-2][link-roberta-med-small-1M-2] | 1M | MED-SMALL | 10K | 512 | 134.18 |
| [roberta-med-small-1M-3][link-roberta-med-small-1M-3] | 1M | MED-SMALL | 31K | 512 | 139.39 |
The hyperparameters corresponding to model sizes mentioned above are as follows:
| Model Size | L | AH | HS | FFN | P |
|------------|----|----|-----|------|------|
| BASE | 12 | 12 | 768 | 3072 | 125M |
| MED-SMALL | 6 | 8 | 512 | 2048 | 45M |
(AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.)
For other hyperparameters, we select:
- Peak Learning rate: 5e-4
- Warmup Steps: 6% of max steps
- Dropout: 0.1
[link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1
[link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2
[link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3
[link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1
[link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2
[link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3
[link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1
[link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2
[link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3
[link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1
[link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2
[link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3
|
nyu-mll/roberta-med-small-1M-2
|
nyu-mll
| 2021-05-20T19:07:56Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.
### Hyperparameters and Validation Perplexity
The hyperparameters and validation perplexities corresponding to each model are as follows:
| Model Name | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity |
|--------------------------|---------------|------------|-----------|------------|-----------------------|
| [roberta-base-1B-1][link-roberta-base-1B-1] | 1B | BASE | 100K | 512 | 3.93 |
| [roberta-base-1B-2][link-roberta-base-1B-2] | 1B | BASE | 31K | 1024 | 4.25 |
| [roberta-base-1B-3][link-roberta-base-1B-3] | 1B | BASE | 31K | 4096 | 3.84 |
| [roberta-base-100M-1][link-roberta-base-100M-1] | 100M | BASE | 100K | 512 | 4.99 |
| [roberta-base-100M-2][link-roberta-base-100M-2] | 100M | BASE | 31K | 1024 | 4.61 |
| [roberta-base-100M-3][link-roberta-base-100M-3] | 100M | BASE | 31K | 512 | 5.02 |
| [roberta-base-10M-1][link-roberta-base-10M-1] | 10M | BASE | 10K | 1024 | 11.31 |
| [roberta-base-10M-2][link-roberta-base-10M-2] | 10M | BASE | 10K | 512 | 10.78 |
| [roberta-base-10M-3][link-roberta-base-10M-3] | 10M | BASE | 31K | 512 | 11.58 |
| [roberta-med-small-1M-1][link-roberta-med-small-1M-1] | 1M | MED-SMALL | 100K | 512 | 153.38 |
| [roberta-med-small-1M-2][link-roberta-med-small-1M-2] | 1M | MED-SMALL | 10K | 512 | 134.18 |
| [roberta-med-small-1M-3][link-roberta-med-small-1M-3] | 1M | MED-SMALL | 31K | 512 | 139.39 |
The hyperparameters corresponding to model sizes mentioned above are as follows:
| Model Size | L | AH | HS | FFN | P |
|------------|----|----|-----|------|------|
| BASE | 12 | 12 | 768 | 3072 | 125M |
| MED-SMALL | 6 | 8 | 512 | 2048 | 45M |
(AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.)
For other hyperparameters, we select:
- Peak Learning rate: 5e-4
- Warmup Steps: 6% of max steps
- Dropout: 0.1
[link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1
[link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2
[link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3
[link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1
[link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2
[link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3
[link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1
[link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2
[link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3
[link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1
[link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2
[link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3
|
nyu-mll/roberta-base-1B-2
|
nyu-mll
| 2021-05-20T19:04:39Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.
### Hyperparameters and Validation Perplexity
The hyperparameters and validation perplexities corresponding to each model are as follows:
| Model Name | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity |
|--------------------------|---------------|------------|-----------|------------|-----------------------|
| [roberta-base-1B-1][link-roberta-base-1B-1] | 1B | BASE | 100K | 512 | 3.93 |
| [roberta-base-1B-2][link-roberta-base-1B-2] | 1B | BASE | 31K | 1024 | 4.25 |
| [roberta-base-1B-3][link-roberta-base-1B-3] | 1B | BASE | 31K | 4096 | 3.84 |
| [roberta-base-100M-1][link-roberta-base-100M-1] | 100M | BASE | 100K | 512 | 4.99 |
| [roberta-base-100M-2][link-roberta-base-100M-2] | 100M | BASE | 31K | 1024 | 4.61 |
| [roberta-base-100M-3][link-roberta-base-100M-3] | 100M | BASE | 31K | 512 | 5.02 |
| [roberta-base-10M-1][link-roberta-base-10M-1] | 10M | BASE | 10K | 1024 | 11.31 |
| [roberta-base-10M-2][link-roberta-base-10M-2] | 10M | BASE | 10K | 512 | 10.78 |
| [roberta-base-10M-3][link-roberta-base-10M-3] | 10M | BASE | 31K | 512 | 11.58 |
| [roberta-med-small-1M-1][link-roberta-med-small-1M-1] | 1M | MED-SMALL | 100K | 512 | 153.38 |
| [roberta-med-small-1M-2][link-roberta-med-small-1M-2] | 1M | MED-SMALL | 10K | 512 | 134.18 |
| [roberta-med-small-1M-3][link-roberta-med-small-1M-3] | 1M | MED-SMALL | 31K | 512 | 139.39 |
The hyperparameters corresponding to model sizes mentioned above are as follows:
| Model Size | L | AH | HS | FFN | P |
|------------|----|----|-----|------|------|
| BASE | 12 | 12 | 768 | 3072 | 125M |
| MED-SMALL | 6 | 8 | 512 | 2048 | 45M |
(AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.)
For other hyperparameters, we select:
- Peak Learning rate: 5e-4
- Warmup Steps: 6% of max steps
- Dropout: 0.1
[link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1
[link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2
[link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3
[link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1
[link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2
[link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3
[link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1
[link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2
[link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3
[link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1
[link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2
[link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3
|
nyu-mll/roberta-base-1B-1
|
nyu-mll
| 2021-05-20T19:03:06Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.
### Hyperparameters and Validation Perplexity
The hyperparameters and validation perplexities corresponding to each model are as follows:
| Model Name | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity |
|--------------------------|---------------|------------|-----------|------------|-----------------------|
| [roberta-base-1B-1][link-roberta-base-1B-1] | 1B | BASE | 100K | 512 | 3.93 |
| [roberta-base-1B-2][link-roberta-base-1B-2] | 1B | BASE | 31K | 1024 | 4.25 |
| [roberta-base-1B-3][link-roberta-base-1B-3] | 1B | BASE | 31K | 4096 | 3.84 |
| [roberta-base-100M-1][link-roberta-base-100M-1] | 100M | BASE | 100K | 512 | 4.99 |
| [roberta-base-100M-2][link-roberta-base-100M-2] | 100M | BASE | 31K | 1024 | 4.61 |
| [roberta-base-100M-3][link-roberta-base-100M-3] | 100M | BASE | 31K | 512 | 5.02 |
| [roberta-base-10M-1][link-roberta-base-10M-1] | 10M | BASE | 10K | 1024 | 11.31 |
| [roberta-base-10M-2][link-roberta-base-10M-2] | 10M | BASE | 10K | 512 | 10.78 |
| [roberta-base-10M-3][link-roberta-base-10M-3] | 10M | BASE | 31K | 512 | 11.58 |
| [roberta-med-small-1M-1][link-roberta-med-small-1M-1] | 1M | MED-SMALL | 100K | 512 | 153.38 |
| [roberta-med-small-1M-2][link-roberta-med-small-1M-2] | 1M | MED-SMALL | 10K | 512 | 134.18 |
| [roberta-med-small-1M-3][link-roberta-med-small-1M-3] | 1M | MED-SMALL | 31K | 512 | 139.39 |
The hyperparameters corresponding to model sizes mentioned above are as follows:
| Model Size | L | AH | HS | FFN | P |
|------------|----|----|-----|------|------|
| BASE | 12 | 12 | 768 | 3072 | 125M |
| MED-SMALL | 6 | 8 | 512 | 2048 | 45M |
(AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.)
For other hyperparameters, we select:
- Peak Learning rate: 5e-4
- Warmup Steps: 6% of max steps
- Dropout: 0.1
[link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1
[link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2
[link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3
[link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1
[link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2
[link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3
[link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1
[link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2
[link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3
[link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1
[link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2
[link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3
|
nyu-mll/roberta-base-10M-3
|
nyu-mll
| 2021-05-20T19:00:36Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.
### Hyperparameters and Validation Perplexity
The hyperparameters and validation perplexities corresponding to each model are as follows:
| Model Name | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity |
|--------------------------|---------------|------------|-----------|------------|-----------------------|
| [roberta-base-1B-1][link-roberta-base-1B-1] | 1B | BASE | 100K | 512 | 3.93 |
| [roberta-base-1B-2][link-roberta-base-1B-2] | 1B | BASE | 31K | 1024 | 4.25 |
| [roberta-base-1B-3][link-roberta-base-1B-3] | 1B | BASE | 31K | 4096 | 3.84 |
| [roberta-base-100M-1][link-roberta-base-100M-1] | 100M | BASE | 100K | 512 | 4.99 |
| [roberta-base-100M-2][link-roberta-base-100M-2] | 100M | BASE | 31K | 1024 | 4.61 |
| [roberta-base-100M-3][link-roberta-base-100M-3] | 100M | BASE | 31K | 512 | 5.02 |
| [roberta-base-10M-1][link-roberta-base-10M-1] | 10M | BASE | 10K | 1024 | 11.31 |
| [roberta-base-10M-2][link-roberta-base-10M-2] | 10M | BASE | 10K | 512 | 10.78 |
| [roberta-base-10M-3][link-roberta-base-10M-3] | 10M | BASE | 31K | 512 | 11.58 |
| [roberta-med-small-1M-1][link-roberta-med-small-1M-1] | 1M | MED-SMALL | 100K | 512 | 153.38 |
| [roberta-med-small-1M-2][link-roberta-med-small-1M-2] | 1M | MED-SMALL | 10K | 512 | 134.18 |
| [roberta-med-small-1M-3][link-roberta-med-small-1M-3] | 1M | MED-SMALL | 31K | 512 | 139.39 |
The hyperparameters corresponding to model sizes mentioned above are as follows:
| Model Size | L | AH | HS | FFN | P |
|------------|----|----|-----|------|------|
| BASE | 12 | 12 | 768 | 3072 | 125M |
| MED-SMALL | 6 | 8 | 512 | 2048 | 45M |
(AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.)
For other hyperparameters, we select:
- Peak Learning rate: 5e-4
- Warmup Steps: 6% of max steps
- Dropout: 0.1
[link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1
[link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2
[link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3
[link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1
[link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2
[link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3
[link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1
[link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2
[link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3
[link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1
[link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2
[link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3
|
nyu-mll/roberta-base-10M-2
|
nyu-mll
| 2021-05-20T18:58:09Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.
### Hyperparameters and Validation Perplexity
The hyperparameters and validation perplexities corresponding to each model are as follows:
| Model Name | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity |
|--------------------------|---------------|------------|-----------|------------|-----------------------|
| [roberta-base-1B-1][link-roberta-base-1B-1] | 1B | BASE | 100K | 512 | 3.93 |
| [roberta-base-1B-2][link-roberta-base-1B-2] | 1B | BASE | 31K | 1024 | 4.25 |
| [roberta-base-1B-3][link-roberta-base-1B-3] | 1B | BASE | 31K | 4096 | 3.84 |
| [roberta-base-100M-1][link-roberta-base-100M-1] | 100M | BASE | 100K | 512 | 4.99 |
| [roberta-base-100M-2][link-roberta-base-100M-2] | 100M | BASE | 31K | 1024 | 4.61 |
| [roberta-base-100M-3][link-roberta-base-100M-3] | 100M | BASE | 31K | 512 | 5.02 |
| [roberta-base-10M-1][link-roberta-base-10M-1] | 10M | BASE | 10K | 1024 | 11.31 |
| [roberta-base-10M-2][link-roberta-base-10M-2] | 10M | BASE | 10K | 512 | 10.78 |
| [roberta-base-10M-3][link-roberta-base-10M-3] | 10M | BASE | 31K | 512 | 11.58 |
| [roberta-med-small-1M-1][link-roberta-med-small-1M-1] | 1M | MED-SMALL | 100K | 512 | 153.38 |
| [roberta-med-small-1M-2][link-roberta-med-small-1M-2] | 1M | MED-SMALL | 10K | 512 | 134.18 |
| [roberta-med-small-1M-3][link-roberta-med-small-1M-3] | 1M | MED-SMALL | 31K | 512 | 139.39 |
The hyperparameters corresponding to model sizes mentioned above are as follows:
| Model Size | L | AH | HS | FFN | P |
|------------|----|----|-----|------|------|
| BASE | 12 | 12 | 768 | 3072 | 125M |
| MED-SMALL | 6 | 8 | 512 | 2048 | 45M |
(AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.)
For other hyperparameters, we select:
- Peak Learning rate: 5e-4
- Warmup Steps: 6% of max steps
- Dropout: 0.1
[link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1
[link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2
[link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3
[link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1
[link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2
[link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3
[link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1
[link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2
[link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3
[link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1
[link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2
[link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3
|
nyu-mll/roberta-base-100M-3
|
nyu-mll
| 2021-05-20T18:56:02Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.
### Hyperparameters and Validation Perplexity
The hyperparameters and validation perplexities corresponding to each model are as follows:
| Model Name | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity |
|--------------------------|---------------|------------|-----------|------------|-----------------------|
| [roberta-base-1B-1][link-roberta-base-1B-1] | 1B | BASE | 100K | 512 | 3.93 |
| [roberta-base-1B-2][link-roberta-base-1B-2] | 1B | BASE | 31K | 1024 | 4.25 |
| [roberta-base-1B-3][link-roberta-base-1B-3] | 1B | BASE | 31K | 4096 | 3.84 |
| [roberta-base-100M-1][link-roberta-base-100M-1] | 100M | BASE | 100K | 512 | 4.99 |
| [roberta-base-100M-2][link-roberta-base-100M-2] | 100M | BASE | 31K | 1024 | 4.61 |
| [roberta-base-100M-3][link-roberta-base-100M-3] | 100M | BASE | 31K | 512 | 5.02 |
| [roberta-base-10M-1][link-roberta-base-10M-1] | 10M | BASE | 10K | 1024 | 11.31 |
| [roberta-base-10M-2][link-roberta-base-10M-2] | 10M | BASE | 10K | 512 | 10.78 |
| [roberta-base-10M-3][link-roberta-base-10M-3] | 10M | BASE | 31K | 512 | 11.58 |
| [roberta-med-small-1M-1][link-roberta-med-small-1M-1] | 1M | MED-SMALL | 100K | 512 | 153.38 |
| [roberta-med-small-1M-2][link-roberta-med-small-1M-2] | 1M | MED-SMALL | 10K | 512 | 134.18 |
| [roberta-med-small-1M-3][link-roberta-med-small-1M-3] | 1M | MED-SMALL | 31K | 512 | 139.39 |
The hyperparameters corresponding to model sizes mentioned above are as follows:
| Model Size | L | AH | HS | FFN | P |
|------------|----|----|-----|------|------|
| BASE | 12 | 12 | 768 | 3072 | 125M |
| MED-SMALL | 6 | 8 | 512 | 2048 | 45M |
(AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.)
For other hyperparameters, we select:
- Peak Learning rate: 5e-4
- Warmup Steps: 6% of max steps
- Dropout: 0.1
[link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1
[link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2
[link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3
[link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1
[link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2
[link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3
[link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1
[link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2
[link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3
[link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1
[link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2
[link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3
|
nyu-mll/roberta-base-100M-1
|
nyu-mll
| 2021-05-20T18:53:55Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.
### Hyperparameters and Validation Perplexity
The hyperparameters and validation perplexities corresponding to each model are as follows:
| Model Name | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity |
|--------------------------|---------------|------------|-----------|------------|-----------------------|
| [roberta-base-1B-1][link-roberta-base-1B-1] | 1B | BASE | 100K | 512 | 3.93 |
| [roberta-base-1B-2][link-roberta-base-1B-2] | 1B | BASE | 31K | 1024 | 4.25 |
| [roberta-base-1B-3][link-roberta-base-1B-3] | 1B | BASE | 31K | 4096 | 3.84 |
| [roberta-base-100M-1][link-roberta-base-100M-1] | 100M | BASE | 100K | 512 | 4.99 |
| [roberta-base-100M-2][link-roberta-base-100M-2] | 100M | BASE | 31K | 1024 | 4.61 |
| [roberta-base-100M-3][link-roberta-base-100M-3] | 100M | BASE | 31K | 512 | 5.02 |
| [roberta-base-10M-1][link-roberta-base-10M-1] | 10M | BASE | 10K | 1024 | 11.31 |
| [roberta-base-10M-2][link-roberta-base-10M-2] | 10M | BASE | 10K | 512 | 10.78 |
| [roberta-base-10M-3][link-roberta-base-10M-3] | 10M | BASE | 31K | 512 | 11.58 |
| [roberta-med-small-1M-1][link-roberta-med-small-1M-1] | 1M | MED-SMALL | 100K | 512 | 153.38 |
| [roberta-med-small-1M-2][link-roberta-med-small-1M-2] | 1M | MED-SMALL | 10K | 512 | 134.18 |
| [roberta-med-small-1M-3][link-roberta-med-small-1M-3] | 1M | MED-SMALL | 31K | 512 | 139.39 |
The hyperparameters corresponding to model sizes mentioned above are as follows:
| Model Size | L | AH | HS | FFN | P |
|------------|----|----|-----|------|------|
| BASE | 12 | 12 | 768 | 3072 | 125M |
| MED-SMALL | 6 | 8 | 512 | 2048 | 45M |
(AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.)
For other hyperparameters, we select:
- Peak Learning rate: 5e-4
- Warmup Steps: 6% of max steps
- Dropout: 0.1
[link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1
[link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2
[link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3
[link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1
[link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2
[link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3
[link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1
[link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2
[link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3
[link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1
[link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2
[link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3
|
mrm8488/roberta-base-1B-1-finetuned-squadv2
|
mrm8488
| 2021-05-20T18:27:20Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"question-answering",
"en",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: en
---
# RoBERTa-base (1B-1) + SQuAD v2 ❓
[roberta-base-1B-1](https://huggingface.co/nyu-mll/roberta-base-1B-1) fine-tuned on [SQUAD v2 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/) for **Q&A** downstream task.
## Details of the downstream task (Q&A) - Model 🧠
RoBERTa Pretrained on Smaller Datasets
[NYU Machine Learning for Language](https://huggingface.co/nyu-mll) pretrained RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). They released 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: They combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.
## Details of the downstream task (Q&A) - Dataset 📚
**S**tanford **Q**uestion **A**nswering **D**ataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
**SQuAD2.0** combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.
## Model training 🏋️
The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:
```bash
python transformers/examples/question-answering/run_squad.py \
--model_type roberta \
--model_name_or_path 'nyu-mll/roberta-base-1B-1' \
--do_eval \
--do_train \
--do_lower_case \
--train_file /content/dataset/train-v2.0.json \
--predict_file /content/dataset/dev-v2.0.json \
--per_gpu_train_batch_size 16 \
--learning_rate 3e-5 \
--num_train_epochs 10 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /content/output \
--overwrite_output_dir \
--save_steps 1000 \
--version_2_with_negative
```
## Test set Results 🧾
| Metric | # Value |
| ------ | --------- |
| **EM** | **64.86** |
| **F1** | **68.99** |
```json
{
'exact': 64.86145034953255,
'f1': 68.9902640378272,
'total': 11873,
'HasAns_exact': 64.03508771929825,
'HasAns_f1': 72.3045554860189,
'HasAns_total': 5928,
'NoAns_exact': 65.68544995794785,
'NoAns_f1': 65.68544995794785,
'NoAns_total': 5945,
'best_exact': 64.86987282068559,
'best_exact_thresh': 0.0,
'best_f1': 68.99868650898054,
'best_f1_thresh': 0.0
}
```
### Model in action 🚀
Fast usage with **pipelines**:
```python
from transformers import pipeline
QnA_pipeline = pipeline('question-answering', model='mrm8488/roberta-base-1B-1-finetuned-squadv2')
QnA_pipeline({
'context': 'A new strain of flu that has the potential to become a pandemic has been identified in China by scientists.',
'question': 'What has been discovered by scientists from China ?'
})
# Output:
{'answer': 'A new strain of flu', 'end': 19, 'score': 0.7145650685380576,'start': 0}
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
mrm8488/roberta-base-1B-1-finetuned-squadv1
|
mrm8488
| 2021-05-20T18:26:13Z | 35 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"question-answering",
"en",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: en
---
# RoBERTa-base (1B-1) + SQuAD v1 ❓
[roberta-base-1B-1](https://huggingface.co/nyu-mll/roberta-base-1B-1) fine-tuned on [SQUAD v1.1 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/) for **Q&A** downstream task.
## Details of the downstream task (Q&A) - Model 🧠
RoBERTa Pretrained on Smaller Datasets
[NYU Machine Learning for Language](https://huggingface.co/nyu-mll) pretrained RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). They released 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: They combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.
## Details of the downstream task (Q&A) - Dataset 📚
**S**tanford **Q**uestion **A**nswering **D**ataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
SQuAD v1.1 contains **100,000+** question-answer pairs on **500+** articles.
## Model training 🏋️
The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:
```bash
python transformers/examples/question-answering/run_squad.py \
--model_type roberta \
--model_name_or_path 'nyu-mll/roberta-base-1B-1' \
--do_eval \
--do_train \
--do_lower_case \
--train_file /content/dataset/train-v1.1.json \
--predict_file /content/dataset/dev-v1.1.json \
--per_gpu_train_batch_size 16 \
--learning_rate 3e-5 \
--num_train_epochs 10 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /content/output \
--overwrite_output_dir \
--save_steps 1000
```
## Test set Results 🧾
| Metric | # Value |
| ------ | --------- |
| **EM** | **72.62** |
| **F1** | **82.19** |
```json
{
'exact': 72.62062440870388,
'f1': 82.19430877136834,
'total': 10570,
'HasAns_exact': 72.62062440870388,
'HasAns_f1': 82.19430877136834,
'HasAns_total': 10570,
'best_exact': 72.62062440870388,
'best_exact_thresh': 0.0,
'best_f1': 82.19430877136834,
'best_f1_thresh': 0.0
}
```
### Model in action 🚀
Fast usage with **pipelines**:
```python
from transformers import pipeline
QnA_pipeline = pipeline('question-answering', model='mrm8488/roberta-base-1B-1-finetuned-squadv1')
QnA_pipeline({
'context': 'A new strain of flu that has the potential to become a pandemic has been identified in China by scientists.',
'question': 'What has been discovered by scientists from China ?'
})
# Output:
{'answer': 'A new strain of flu', 'end': 19, 'score': 0.04702283976040074, 'start': 0}
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
mrm8488/RuPERTa-base-finetuned-squadv2
|
mrm8488
| 2021-05-20T18:14:42Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"question-answering",
"es",
"dataset:squad_v2",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: es
datasets:
- squad_v2
---
|
mrm8488/RuPERTa-base-finetuned-squadv1
|
mrm8488
| 2021-05-20T18:13:28Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"question-answering",
"es",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: es
datasets:
- squad
---
|
mrm8488/RuPERTa-base-finetuned-pos
|
mrm8488
| 2021-05-20T18:08:34Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"token-classification",
"es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language: es
thumbnail:
---
# RuPERTa-base (Spanish RoBERTa) + POS 🎃🏷
This model is a fine-tuned on [CONLL CORPORA](https://www.kaggle.com/nltkdata/conll-corpora) version of [RuPERTa-base](https://huggingface.co/mrm8488/RuPERTa-base) for **POS** downstream task.
## Details of the downstream task (POS) - Dataset
- [Dataset: CONLL Corpora ES](https://www.kaggle.com/nltkdata/conll-corpora) 📚
| Dataset | # Examples |
| ---------------------- | ----- |
| Train | 445 K |
| Dev | 55 K |
- [Fine-tune on NER script provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner_old.py)
- Labels covered:
```
ADJ
ADP
ADV
AUX
CCONJ
DET
INTJ
NOUN
NUM
PART
PRON
PROPN
PUNCT
SCONJ
SYM
VERB
```
## Metrics on evaluation set 🧾
| Metric | # score |
| :------------------------------------------------------------------------------------: | :-------: |
| F1 | **97.39**
| Precision | **97.47** |
| Recall | **9732** |
## Model in action 🔨
Example of usage
```python
import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('mrm8488/RuPERTa-base-finetuned-pos')
model = AutoModelForTokenClassification.from_pretrained('mrm8488/RuPERTa-base-finetuned-pos')
id2label = {
"0": "O",
"1": "ADJ",
"2": "ADP",
"3": "ADV",
"4": "AUX",
"5": "CCONJ",
"6": "DET",
"7": "INTJ",
"8": "NOUN",
"9": "NUM",
"10": "PART",
"11": "PRON",
"12": "PROPN",
"13": "PUNCT",
"14": "SCONJ",
"15": "SYM",
"16": "VERB"
}
text ="Mis amigos están pensando viajar a Londres este verano."
input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0)
outputs = model(input_ids)
last_hidden_states = outputs[0]
for m in last_hidden_states:
for index, n in enumerate(m):
if(index > 0 and index <= len(text.split(" "))):
print(text.split(" ")[index-1] + ": " + id2label[str(torch.argmax(n).item())])
'''
Output:
--------
Mis: NUM
amigos: PRON
están: AUX
pensando: ADV
viajar: VERB
a: ADP
Londres: PROPN
este: DET
verano..: NOUN
'''
```
Yeah! Not too bad 🎉
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
mrm8488/RuPERTa-base-finetuned-pawsx-es
|
mrm8488
| 2021-05-20T18:07:14Z | 25 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"nli",
"es",
"dataset:xtreme",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: es
datasets:
- xtreme
tags:
- nli
widget:
- text: "En 2009 se mudó a Filadelfia y en la actualidad vive en Nueva York. Se mudó nuevamente a Filadelfia en 2009 y ahora vive en la ciudad de Nueva York."
---
# RuPERTa-base fine-tuned on PAWS-X-es for Paraphrase Identification (NLI)
|
julien-c/dummy-unknown
|
julien-c
| 2021-05-20T17:31:14Z | 61,031 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"ci",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
tags:
- ci
---
## Dummy model used for unit testing and CI
```python
import json
import os
from transformers import RobertaConfig, RobertaForMaskedLM, TFRobertaForMaskedLM
DIRNAME = "./dummy-unknown"
config = RobertaConfig(10, 20, 1, 1, 40)
model = RobertaForMaskedLM(config)
model.save_pretrained(DIRNAME)
tf_model = TFRobertaForMaskedLM.from_pretrained(DIRNAME, from_pt=True)
tf_model.save_pretrained(DIRNAME)
# Tokenizer:
vocab = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
vocab_file = os.path.join(DIRNAME, "vocab.json")
merges_file = os.path.join(DIRNAME, "merges.txt")
with open(vocab_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
with open(merges_file, "w", encoding="utf-8") as fp:
fp.write("\n".join(merges))
```
|
iarfmoose/roberta-small-bulgarian
|
iarfmoose
| 2021-05-20T16:54:01Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"bg",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: bg
---
# RoBERTa-small-bulgarian
The RoBERTa model was originally introduced in [this paper](https://arxiv.org/abs/1907.11692). This is a smaller version of [RoBERTa-base-bulgarian](https://huggingface.co/iarfmoose/roberta-small-bulgarian) with only 6 hidden layers, but similar performance.
## Intended uses
This model can be used for cloze tasks (masked language modeling) or finetuned on other tasks in Bulgarian.
## Limitations and bias
The training data is unfiltered text from the internet and may contain all sorts of biases.
## Training data
This model was trained on the following data:
- [bg_dedup from OSCAR](https://oscar-corpus.com/)
- [Newscrawl 1 million sentences 2017 from Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download/bulgarian)
- [Wikipedia 1 million sentences 2016 from Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download/bulgarian)
## Training procedure
The model was pretrained using a masked language-modeling objective with dynamic masking as described [here](https://huggingface.co/roberta-base#preprocessing)
It was trained for 160k steps. The batch size was limited to 8 due to GPU memory limitations.
|
hashk1/EsperBERTo-malgranda
|
hashk1
| 2021-05-20T16:38:46Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"eo",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: eo
thumbnail: https://huggingface.co/blog/assets/01_how-to-train/EsperBERTo-thumbnail-v2.png
widget:
- text: "Ĉu vi paloras la <mask> Esperanto?"
---
## EsperBERTo: RoBERTa-like Language model trained on Esperanto
|
deepampatel/roberta-mlm-marathi
|
deepampatel
| 2021-05-20T15:58:32Z | 13 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"mr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: "mr"
---
# Welcome to Roberta-Marathi-MLM
## Model Description
> This is a small language model for [Marathi](https://en.wikipedia.org/wiki/Marathi) language with 1M data samples taken from
[OSCAR page](https://oscar-public.huma-num.fr/shuffled/mr_dedup.txt.gz)
## Training params
- **Dataset** - 1M data samples are used to train this model from OSCAR page(https://oscar-corpus.com/) eventhough data set is of 2.7 GB due to resource constraint to train
I have picked only 1M data from the total 2.7GB data set. If you are interested in collaboration and have computational resources to train on you are most welcome to do so.
- **Preprocessing** - ByteLevelBPETokenizer is used to tokenize the sentences at character level and vocabulary size is set to 52k as per standard values given by 🤗
<!-- - **Hyperparameters** - __ByteLevelBPETokenizer__ : vocabulary size = 52_000 and min_frequency = 2
__Trainer__ : num_train_epochs=12 - trained for 12 epochs
per_gpu_train_batch_size=64 - batch size for the datasamples is 64
save_steps=10_000 - save model for every 10k steps
save_total_limit=2 - save limit is set for 2 -->
**Intended uses & limitations**
this is for anyone who wants to make use of marathi language models for various tasks like language generation, translation and many more use cases.
**Whatever else is helpful!**
If you are intersted in collaboration feel free to reach me [Deepam](mailto:deepam8155@gmail.com)
|
dbernsohn/roberta-java
|
dbernsohn
| 2021-05-20T15:54:29Z | 13 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# roberta-java
---
language: Java
datasets:
- code_search_net
---
This is a [roberta](https://arxiv.org/pdf/1907.11692.pdf) pre-trained version on the [CodeSearchNet dataset](https://github.com/github/CodeSearchNet) for **Java** Mask Language Model mission.
To load the model:
(necessary packages: !pip install transformers sentencepiece)
```python
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
tokenizer = AutoTokenizer.from_pretrained("dbernsohn/roberta-java")
model = AutoModelWithLMHead.from_pretrained("dbernsohn/roberta-java")
fill_mask = pipeline(
"fill-mask",
model=model,
tokenizer=tokenizer
)
```
You can then use this model to fill masked words in a Java code.
```python
code = """
String[] cars = {"Volvo", "BMW", "Ford", "Mazda"};
for (String i : cars) {
System.out.<mask>(i);
}
""".lstrip()
pred = {x["token_str"].replace("Ġ", ""): x["score"] for x in fill_mask(code)}
sorted(pred.items(), key=lambda kv: kv[1], reverse=True)
# [('println', 0.32571351528167725),
# ('get', 0.2897663116455078),
# ('remove', 0.0637081190943718),
# ('exit', 0.058875661343336105),
# ('print', 0.034190207719802856)]
```
The whole training process and hyperparameters are in my [GitHub repo](https://github.com/DorBernsohn/CodeLM/tree/main/CodeMLM)
> Created by [Dor Bernsohn](https://www.linkedin.com/in/dor-bernsohn-70b2b1146/)
|
clue/roberta_chinese_base
|
clue
| 2021-05-20T15:23:58Z | 317 | 7 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"zh",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: zh
---
## roberta_chinese_base
### Overview
**Language model:** roberta-base
**Model size:** 392M
**Language:** Chinese
**Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020)
**Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE)
### Results
For results on downstream tasks like text classification, please refer to [this repository](https://github.com/CLUEbenchmark/CLUE).
### Usage
**NOTE:** You have to call **BertTokenizer** instead of RobertaTokenizer !!!
```
import torch
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained("clue/roberta_chinese_base")
roberta = BertModel.from_pretrained("clue/roberta_chinese_base")
```
### About CLUE benchmark
Organization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.
Github: https://github.com/CLUEbenchmark
Website: https://www.cluebenchmarks.com/
|
clue/roberta_chinese_3L312_clue_tiny
|
clue
| 2021-05-20T15:22:48Z | 4 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"zh",
"arxiv:2003.01355",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: zh
---
# Introduction
This model was trained on TPU and the details are as follows:
## Model
##
| Model_name | params | size | Training_corpus | Vocab |
| :------------------------------------------ | :----- | :------- | :----------------- | :-----------: |
| **`RoBERTa-tiny-clue`** <br/>Super_small_model | 7.5M | 28.3M | **CLUECorpus2020** | **CLUEVocab** |
| **`RoBERTa-tiny-pair`** <br/>Super_small_sentence_pair_model | 7.5M | 28.3M | **CLUECorpus2020** | **CLUEVocab** |
| **`RoBERTa-tiny3L768-clue`** <br/>small_model | 38M | 110M | **CLUECorpus2020** | **CLUEVocab** |
| **`RoBERTa-tiny3L312-clue`** <br/>small_model | <7.5M | 24M | **CLUECorpus2020** | **CLUEVocab** |
| **`RoBERTa-large-clue`** <br/> Large_model | 290M | 1.20G | **CLUECorpus2020** | **CLUEVocab** |
| **`RoBERTa-large-pair`** <br/>Large_sentence_pair_model | 290M | 1.20G | **CLUECorpus2020** | **CLUEVocab** |
### Usage
With the help of[Huggingface-Transformers 2.5.1](https://github.com/huggingface/transformers), you could use these model as follows
```
tokenizer = BertTokenizer.from_pretrained("MODEL_NAME")
model = BertModel.from_pretrained("MODEL_NAME")
```
`MODEL_NAME`：
| Model_NAME | MODEL_LINK |
| -------------------------- | ------------------------------------------------------------ |
| **RoBERTa-tiny-clue** | [`clue/roberta_chinese_clue_tiny`](https://huggingface.co/clue/roberta_chinese_clue_tiny) |
| **RoBERTa-tiny-pair** | [`clue/roberta_chinese_pair_tiny`](https://huggingface.co/clue/roberta_chinese_pair_tiny) |
| **RoBERTa-tiny3L768-clue** | [`clue/roberta_chinese_3L768_clue_tiny`](https://huggingface.co/clue/roberta_chinese_3L768_clue_tiny) |
| **RoBERTa-tiny3L312-clue** | [`clue/roberta_chinese_3L312_clue_tiny`](https://huggingface.co/clue/roberta_chinese_3L312_clue_tiny) |
| **RoBERTa-large-clue** | [`clue/roberta_chinese_clue_large`](https://huggingface.co/clue/roberta_chinese_clue_large) |
| **RoBERTa-large-pair** | [`clue/roberta_chinese_pair_large`](https://huggingface.co/clue/roberta_chinese_pair_large) |
## Details
Please read <a href='https://arxiv.org/pdf/2003.01355'>https://arxiv.org/pdf/2003.01355.
Please visit our repository: https://github.com/CLUEbenchmark/CLUEPretrainedModels.git
|
castorini/ance-msmarco-doc-firstp
|
castorini
| 2021-05-20T15:17:20Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"arxiv:2007.00808",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
This model is converted from the original ANCE [repo](https://github.com/microsoft/ANCE) and fitted into Pyserini:
> Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, Arnold Overwijk. [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://arxiv.org/pdf/2007.00808.pdf)
For more details on how to use it, check our experiments in [Pyserini](https://github.com/castorini/pyserini/blob/master/docs/experiments-ance.md)
|
cahya/roberta-base-indonesian-522M
|
cahya
| 2021-05-20T14:41:00Z | 338 | 6 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"id",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: "id"
license: "mit"
datasets:
- Indonesian Wikipedia
widget:
- text: "Ibu ku sedang bekerja <mask> supermarket."
---
# Indonesian RoBERTa base model (uncased)
## Model description
It is RoBERTa-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This
model is uncased: it does not make a difference between indonesia and Indonesia.
This is one of several other language models that have been pre-trained with indonesian datasets. More detail about
its usage on downstream tasks (text classification, text generation, etc) is available at [Transformer based Indonesian Language Models](https://github.com/cahya-wirawan/indonesian-language-models/tree/master/Transformers)
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='cahya/roberta-base-indonesian-522M')
>>> unmasker("Ibu ku sedang bekerja <mask> supermarket")
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
model_name='cahya/roberta-base-indonesian-522M'
tokenizer = RobertaTokenizer.from_pretrained(model_name)
model = RobertaModel.from_pretrained(model_name)
text = "Silakan diganti dengan text apa saja."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in Tensorflow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
model_name='cahya/roberta-base-indonesian-522M'
tokenizer = RobertaTokenizer.from_pretrained(model_name)
model = TFRobertaModel.from_pretrained(model_name)
text = "Silakan diganti dengan text apa saja."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
This model was pre-trained with 522MB of indonesian Wikipedia.
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are
then of the form:
```<s> Sentence A </s> Sentence B </s>```
|
aravind-812/roberta-train-json
|
aravind-812
| 2021-05-20T14:12:53Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
datasets:
- squad
widget:
- text: "Which name is also used to describe the Amazon rainforest in English?"
context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."
- text: "How many square kilometers of rainforest is covered in the basin?"
context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."
|
Naveen-k/KanBERTo
|
Naveen-k
| 2021-05-20T12:16:02Z | 13 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"kn",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language: kn
---
# Welcome to KanBERTo (ಕನ್ಬರ್ಟೋ)
## Model Description
> This is a small language model for [Kannada](https://en.wikipedia.org/wiki/Kannada) language with 1M data samples taken from
[OSCAR page](https://traces1.inria.fr/oscar/files/compressed-orig/kn.txt.gz)
## Training params
- **Dataset** - 1M data samples are used to train this model from OSCAR page(https://traces1.inria.fr/oscar/) eventhough data set is of 1.7 GB due to resource constraint to train
I have picked only 1M data from the total 1.7GB data set. If you are interested in collaboration and have computational resources to train on you are most welcome to do so.
- **Preprocessing** - ByteLevelBPETokenizer is used to tokenize the sentences at character level and vocabulary size is set to 52k as per standard values given by 🤗
- **Hyperparameters** - __ByteLevelBPETokenizer__ : vocabulary size = 52_000 and min_frequency = 2
__Trainer__ : num_train_epochs=12 - trained for 12 epochs
per_gpu_train_batch_size=64 - batch size for the datasamples is 64
save_steps=10_000 - save model for every 10k steps
save_total_limit=2 - save limit is set for 2
**Intended uses & limitations**
this is for anyone who wants to make use of kannada language models for various tasks like language generation, translation and many more use cases.
**Whatever else is helpful!**
If you are intersted in collaboration feel free to reach me [Naveen](mailto:naveen.maltesh@gmail.com)
|
MoseliMotsoehli/zuBERTa
|
MoseliMotsoehli
| 2021-05-20T12:14:07Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"zu",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language: zu
---
# zuBERTa
zuBERTa is a RoBERTa style transformer language model trained on zulu text.
## Intended uses & limitations
The model can be used for getting embeddings to use on a down-stream task such as question answering.
#### How to use
```python
>>> from transformers import pipeline
>>> from transformers import AutoTokenizer, AutoModelWithLMHead
>>> tokenizer = AutoTokenizer.from_pretrained("MoseliMotsoehli/zuBERTa")
>>> model = AutoModelWithLMHead.from_pretrained("MoseliMotsoehli/zuBERTa")
>>> unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer)
>>> unmasker("Abafika eNkandla bafika sebeholwa <mask> uMpongo kaZingelwayo.")
[
{
"sequence": "<s>Abafika eNkandla bafika sebeholwa khona uMpongo kaZingelwayo.</s>",
"score": 0.050459690392017365,
"token": 555,
"token_str": "Ġkhona"
},
{
"sequence": "<s>Abafika eNkandla bafika sebeholwa inkosi uMpongo kaZingelwayo.</s>",
"score": 0.03668094798922539,
"token": 2321,
"token_str": "Ġinkosi"
},
{
"sequence": "<s>Abafika eNkandla bafika sebeholwa ubukhosi uMpongo kaZingelwayo.</s>",
"score": 0.028774697333574295,
"token": 5101,
"token_str": "Ġubukhosi"
}
]
```
## Training data
1. 30k sentences of text, came from the [Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download) of zulu 2018. These were collected from news articles and creative writtings.
2. ~7500 articles of human generated translations were scraped from the zulu [wikipedia](https://zu.wikipedia.org/wiki/Special:AllPages).
### BibTeX entry and citation info
```bibtex
@inproceedings{author = {Moseli Motsoehli},
title = {Towards transformation of Southern African language models through transformers.},
year={2020}
}
```
|
MoritzLaurer/covid-policy-roberta-21
|
MoritzLaurer
| 2021-05-20T12:11:07Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
language:
- en
tags:
- text-classification
metrics:
- accuracy (balanced)
- F1 (weighted)
widget:
- text: "All non-essential work activity will stop in Spain from tomorrow until 9 April but there is some confusion as to which jobs can continue under the new lockdown restrictions"
---
# Covid-Policy-RoBERTa-21
This model is currently in development at the Centre for European Policy Studies (CEPS).
The model is not yet recommended for use. A more detailed description will follow.
If you are interested in using deep learning to identify 20 different types policy measures against COVID-19 in text (NPIs, "non-pharmaceutical interventions") don't hesitate to [contact me](https://www.ceps.eu/ceps-staff/moritz-laurer/).
|
HooshvareLab/roberta-fa-zwnj-base-ner
|
HooshvareLab
| 2021-05-20T11:55:34Z | 113 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"token-classification",
"fa",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
---
language: fa
---
# RobertaNER
This model fine-tuned for the Named Entity Recognition (NER) task on a mixed NER dataset collected from [ARMAN](https://github.com/HaniehP/PersianNER), [PEYMA](http://nsurl.org/2019-2/tasks/task-7-named-entity-recognition-ner-for-farsi/), and [WikiANN](https://elisa-ie.github.io/wikiann/) that covered ten types of entities:
- Date (DAT)
- Event (EVE)
- Facility (FAC)
- Location (LOC)
- Money (MON)
- Organization (ORG)
- Percent (PCT)
- Person (PER)
- Product (PRO)
- Time (TIM)
## Dataset Information
| | Records | B-DAT | B-EVE | B-FAC | B-LOC | B-MON | B-ORG | B-PCT | B-PER | B-PRO | B-TIM | I-DAT | I-EVE | I-FAC | I-LOC | I-MON | I-ORG | I-PCT | I-PER | I-PRO | I-TIM |
|:------|----------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
| Train | 29133 | 1423 | 1487 | 1400 | 13919 | 417 | 15926 | 355 | 12347 | 1855 | 150 | 1947 | 5018 | 2421 | 4118 | 1059 | 19579 | 573 | 7699 | 1914 | 332 |
| Valid | 5142 | 267 | 253 | 250 | 2362 | 100 | 2651 | 64 | 2173 | 317 | 19 | 373 | 799 | 387 | 717 | 270 | 3260 | 101 | 1382 | 303 | 35 |
| Test | 6049 | 407 | 256 | 248 | 2886 | 98 | 3216 | 94 | 2646 | 318 | 43 | 568 | 888 | 408 | 858 | 263 | 3967 | 141 | 1707 | 296 | 78 |
## Evaluation
The following tables summarize the scores obtained by model overall and per each class.
**Overall**
| Model | accuracy | precision | recall | f1 |
|:----------:|:--------:|:---------:|:--------:|:--------:|
| Roberta | 0.994849 | 0.949816 | 0.960235 | 0.954997 |
**Per entities**
| | number | precision | recall | f1 |
|:---: |:------: |:---------: |:--------: |:--------: |
| DAT | 407 | 0.844869 | 0.869779 | 0.857143 |
| EVE | 256 | 0.948148 | 1.000000 | 0.973384 |
| FAC | 248 | 0.957529 | 1.000000 | 0.978304 |
| LOC | 2884 | 0.965422 | 0.968100 | 0.966759 |
| MON | 98 | 0.937500 | 0.918367 | 0.927835 |
| ORG | 3216 | 0.943662 | 0.958333 | 0.950941 |
| PCT | 94 | 1.000000 | 0.968085 | 0.983784 |
| PER | 2646 | 0.957030 | 0.959562 | 0.958294 |
| PRO | 318 | 0.963636 | 1.000000 | 0.981481 |
| TIM | 43 | 0.739130 | 0.790698 | 0.764045 |
## How To Use
You use this model with Transformers pipeline for NER.
### Installing requirements
```bash
pip install transformers
```
### How to predict using pipeline
```python
from transformers import AutoTokenizer
from transformers import AutoModelForTokenClassification # for pytorch
from transformers import TFAutoModelForTokenClassification # for tensorflow
from transformers import pipeline
model_name_or_path = "HooshvareLab/roberta-fa-zwnj-base-ner"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForTokenClassification.from_pretrained(model_name_or_path) # Pytorch
# model = TFAutoModelForTokenClassification.from_pretrained(model_name_or_path) # Tensorflow
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "در سال ۲۰۱۳ درگذشت و آندرتیکر و کین برای او مراسم یادبود گرفتند."
ner_results = nlp(example)
print(ner_results)
```
## Questions?
Post a Github issue on the [ParsNER Issues](https://github.com/hooshvare/parsner/issues) repo.
|
zanelim/singbert
|
zanelim
| 2021-05-20T09:38:41Z | 6 | 4 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"pretraining",
"singapore",
"sg",
"singlish",
"malaysia",
"ms",
"manglish",
"bert-base-uncased",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
tags:
- singapore
- sg
- singlish
- malaysia
- ms
- manglish
- bert-base-uncased
license: mit
datasets:
- reddit singapore, malaysia
- hardwarezone
widget:
- text: "kopi c siew [MASK]"
- text: "die [MASK] must try"
---
# Model name
SingBert - Bert for Singlish (SG) and Manglish (MY).
## Model description
[BERT base uncased](https://github.com/google-research/bert#pre-trained-models), with pre-training finetuned on
[singlish](https://en.wikipedia.org/wiki/Singlish) and [manglish](https://en.wikipedia.org/wiki/Manglish) data.
## Intended uses & limitations
#### How to use
```python
>>> from transformers import pipeline
>>> nlp = pipeline('fill-mask', model='zanelim/singbert')
>>> nlp("kopi c siew [MASK]")
[{'sequence': '[CLS] kopi c siew dai [SEP]',
'score': 0.5092713236808777,
'token': 18765,
'token_str': 'dai'},
{'sequence': '[CLS] kopi c siew mai [SEP]',
'score': 0.3515934646129608,
'token': 14736,
'token_str': 'mai'},
{'sequence': '[CLS] kopi c siew bao [SEP]',
'score': 0.05576375499367714,
'token': 25945,
'token_str': 'bao'},
{'sequence': '[CLS] kopi c siew. [SEP]',
'score': 0.006019321270287037,
'token': 1012,
'token_str': '.'},
{'sequence': '[CLS] kopi c siew sai [SEP]',
'score': 0.0038361591286957264,
'token': 18952,
'token_str': 'sai'}]
>>> nlp("one teh c siew dai, and one kopi [MASK].")
[{'sequence': '[CLS] one teh c siew dai, and one kopi c [SEP]',
'score': 0.6176503300666809,
'token': 1039,
'token_str': 'c'},
{'sequence': '[CLS] one teh c siew dai, and one kopi o [SEP]',
'score': 0.21094971895217896,
'token': 1051,
'token_str': 'o'},
{'sequence': '[CLS] one teh c siew dai, and one kopi. [SEP]',
'score': 0.13027705252170563,
'token': 1012,
'token_str': '.'},
{'sequence': '[CLS] one teh c siew dai, and one kopi! [SEP]',
'score': 0.004680239595472813,
'token': 999,
'token_str': '!'},
{'sequence': '[CLS] one teh c siew dai, and one kopi w [SEP]',
'score': 0.002034128177911043,
'token': 1059,
'token_str': 'w'}]
>>> nlp("dont play [MASK] leh")
[{'sequence': '[CLS] dont play play leh [SEP]',
'score': 0.9281464219093323,
'token': 2377,
'token_str': 'play'},
{'sequence': '[CLS] dont play politics leh [SEP]',
'score': 0.010990909300744534,
'token': 4331,
'token_str': 'politics'},
{'sequence': '[CLS] dont play punk leh [SEP]',
'score': 0.005583590362221003,
'token': 7196,
'token_str': 'punk'},
{'sequence': '[CLS] dont play dirty leh [SEP]',
'score': 0.0025784350000321865,
'token': 6530,
'token_str': 'dirty'},
{'sequence': '[CLS] dont play cheat leh [SEP]',
'score': 0.0025066907983273268,
'token': 21910,
'token_str': 'cheat'}]
>>> nlp("catch no [MASK]")
[{'sequence': '[CLS] catch no ball [SEP]',
'score': 0.7922210693359375,
'token': 3608,
'token_str': 'ball'},
{'sequence': '[CLS] catch no balls [SEP]',
'score': 0.20503675937652588,
'token': 7395,
'token_str': 'balls'},
{'sequence': '[CLS] catch no tail [SEP]',
'score': 0.0006608376861549914,
'token': 5725,
'token_str': 'tail'},
{'sequence': '[CLS] catch no talent [SEP]',
'score': 0.0002158183924620971,
'token': 5848,
'token_str': 'talent'},
{'sequence': '[CLS] catch no prisoners [SEP]',
'score': 5.3481446229852736e-05,
'token': 5895,
'token_str': 'prisoners'}]
>>> nlp("confirm plus [MASK]")
[{'sequence': '[CLS] confirm plus chop [SEP]',
'score': 0.992355227470398,
'token': 24494,
'token_str': 'chop'},
{'sequence': '[CLS] confirm plus one [SEP]',
'score': 0.0037301010452210903,
'token': 2028,
'token_str': 'one'},
{'sequence': '[CLS] confirm plus minus [SEP]',
'score': 0.0014284878270700574,
'token': 15718,
'token_str': 'minus'},
{'sequence': '[CLS] confirm plus 1 [SEP]',
'score': 0.0011354683665558696,
'token': 1015,
'token_str': '1'},
{'sequence': '[CLS] confirm plus chopped [SEP]',
'score': 0.0003804611915256828,
'token': 24881,
'token_str': 'chopped'}]
>>> nlp("die [MASK] must try")
[{'sequence': '[CLS] die die must try [SEP]',
'score': 0.9552758932113647,
'token': 3280,
'token_str': 'die'},
{'sequence': '[CLS] die also must try [SEP]',
'score': 0.03644804656505585,
'token': 2036,
'token_str': 'also'},
{'sequence': '[CLS] die liao must try [SEP]',
'score': 0.003282855963334441,
'token': 727,
'token_str': 'liao'},
{'sequence': '[CLS] die already must try [SEP]',
'score': 0.0004937972989864647,
'token': 2525,
'token_str': 'already'},
{'sequence': '[CLS] die hard must try [SEP]',
'score': 0.0003659659414552152,
'token': 2524,
'token_str': 'hard'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('zanelim/singbert')
model = BertModel.from_pretrained("zanelim/singbert")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained("zanelim/singbert")
model = TFBertModel.from_pretrained("zanelim/singbert")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
#### Limitations and bias
This model was finetuned on colloquial Singlish and Manglish corpus, hence it is best applied on downstream tasks involving the main
constituent languages- english, mandarin, malay. Also, as the training data is mainly from forums, beware of existing inherent bias.
## Training data
Colloquial singlish and manglish (both are a mixture of English, Mandarin, Tamil, Malay, and other local dialects like Hokkien, Cantonese or Teochew)
corpus. The corpus is collected from subreddits- `r/singapore` and `r/malaysia`, and forums such as `hardwarezone`.
## Training procedure
Initialized with [bert base uncased](https://github.com/google-research/bert#pre-trained-models) vocab and checkpoints (pre-trained weights).
Top 1000 custom vocab tokens (non-overlapped with original bert vocab) were further extracted from training data and filled into unused tokens in original bert vocab.
Pre-training was further finetuned on training data with the following hyperparameters
* train_batch_size: 512
* max_seq_length: 128
* num_train_steps: 300000
* num_warmup_steps: 5000
* learning_rate: 2e-5
* hardware: TPU v3-8
|
zanelim/singbert-large-sg
|
zanelim
| 2021-05-20T09:36:17Z | 9 | 4 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"pretraining",
"singapore",
"sg",
"singlish",
"malaysia",
"ms",
"manglish",
"bert-large-uncased",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
tags:
- singapore
- sg
- singlish
- malaysia
- ms
- manglish
- bert-large-uncased
license: mit
datasets:
- reddit singapore, malaysia
- hardwarezone
widget:
- text: "kopi c siew [MASK]"
- text: "die [MASK] must try"
---
# Model name
SingBert Large - Bert for Singlish (SG) and Manglish (MY).
## Model description
Similar to [SingBert](https://huggingface.co/zanelim/singbert) but the large version, which was initialized from [BERT large uncased (whole word masking)](https://github.com/google-research/bert#pre-trained-models), with pre-training finetuned on
[singlish](https://en.wikipedia.org/wiki/Singlish) and [manglish](https://en.wikipedia.org/wiki/Manglish) data.
## Intended uses & limitations
#### How to use
```python
>>> from transformers import pipeline
>>> nlp = pipeline('fill-mask', model='zanelim/singbert-large-sg')
>>> nlp("kopi c siew [MASK]")
[{'sequence': '[CLS] kopi c siew dai [SEP]',
'score': 0.9003700017929077,
'token': 18765,
'token_str': 'dai'},
{'sequence': '[CLS] kopi c siew mai [SEP]',
'score': 0.0779474675655365,
'token': 14736,
'token_str': 'mai'},
{'sequence': '[CLS] kopi c siew. [SEP]',
'score': 0.0032227332703769207,
'token': 1012,
'token_str': '.'},
{'sequence': '[CLS] kopi c siew bao [SEP]',
'score': 0.0017727474914863706,
'token': 25945,
'token_str': 'bao'},
{'sequence': '[CLS] kopi c siew peng [SEP]',
'score': 0.0012526646023616195,
'token': 26473,
'token_str': 'peng'}]
>>> nlp("one teh c siew dai, and one kopi [MASK]")
[{'sequence': '[CLS] one teh c siew dai, and one kopi. [SEP]',
'score': 0.5249741077423096,
'token': 1012,
'token_str': '.'},
{'sequence': '[CLS] one teh c siew dai, and one kopi o [SEP]',
'score': 0.27349168062210083,
'token': 1051,
'token_str': 'o'},
{'sequence': '[CLS] one teh c siew dai, and one kopi peng [SEP]',
'score': 0.057190295308828354,
'token': 26473,
'token_str': 'peng'},
{'sequence': '[CLS] one teh c siew dai, and one kopi c [SEP]',
'score': 0.04022320732474327,
'token': 1039,
'token_str': 'c'},
{'sequence': '[CLS] one teh c siew dai, and one kopi? [SEP]',
'score': 0.01191170234233141,
'token': 1029,
'token_str': '?'}]
>>> nlp("die [MASK] must try")
[{'sequence': '[CLS] die die must try [SEP]',
'score': 0.9921030402183533,
'token': 3280,
'token_str': 'die'},
{'sequence': '[CLS] die also must try [SEP]',
'score': 0.004993876442313194,
'token': 2036,
'token_str': 'also'},
{'sequence': '[CLS] die liao must try [SEP]',
'score': 0.000317625846946612,
'token': 727,
'token_str': 'liao'},
{'sequence': '[CLS] die still must try [SEP]',
'score': 0.0002260878391098231,
'token': 2145,
'token_str': 'still'},
{'sequence': '[CLS] die i must try [SEP]',
'score': 0.00016935862367972732,
'token': 1045,
'token_str': 'i'}]
>>> nlp("dont play [MASK] leh")
[{'sequence': '[CLS] dont play play leh [SEP]',
'score': 0.9079819321632385,
'token': 2377,
'token_str': 'play'},
{'sequence': '[CLS] dont play punk leh [SEP]',
'score': 0.006846973206847906,
'token': 7196,
'token_str': 'punk'},
{'sequence': '[CLS] dont play games leh [SEP]',
'score': 0.004041737411171198,
'token': 2399,
'token_str': 'games'},
{'sequence': '[CLS] dont play politics leh [SEP]',
'score': 0.003728888463228941,
'token': 4331,
'token_str': 'politics'},
{'sequence': '[CLS] dont play cheat leh [SEP]',
'score': 0.0032805048394948244,
'token': 21910,
'token_str': 'cheat'}]
>>> nlp("confirm plus [MASK]")
{'sequence': '[CLS] confirm plus chop [SEP]',
'score': 0.9749826192855835,
'token': 24494,
'token_str': 'chop'},
{'sequence': '[CLS] confirm plus chopped [SEP]',
'score': 0.017554156482219696,
'token': 24881,
'token_str': 'chopped'},
{'sequence': '[CLS] confirm plus minus [SEP]',
'score': 0.002725469646975398,
'token': 15718,
'token_str': 'minus'},
{'sequence': '[CLS] confirm plus guarantee [SEP]',
'score': 0.000900257145985961,
'token': 11302,
'token_str': 'guarantee'},
{'sequence': '[CLS] confirm plus one [SEP]',
'score': 0.0004384620988275856,
'token': 2028,
'token_str': 'one'}]
>>> nlp("catch no [MASK]")
[{'sequence': '[CLS] catch no ball [SEP]',
'score': 0.9381157159805298,
'token': 3608,
'token_str': 'ball'},
{'sequence': '[CLS] catch no balls [SEP]',
'score': 0.060842301696538925,
'token': 7395,
'token_str': 'balls'},
{'sequence': '[CLS] catch no fish [SEP]',
'score': 0.00030917322146706283,
'token': 3869,
'token_str': 'fish'},
{'sequence': '[CLS] catch no breath [SEP]',
'score': 7.552534952992573e-05,
'token': 3052,
'token_str': 'breath'},
{'sequence': '[CLS] catch no tail [SEP]',
'score': 4.208395694149658e-05,
'token': 5725,
'token_str': 'tail'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('zanelim/singbert-large-sg')
model = BertModel.from_pretrained("zanelim/singbert-large-sg")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained("zanelim/singbert-large-sg")
model = TFBertModel.from_pretrained("zanelim/singbert-large-sg")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
#### Limitations and bias
This model was finetuned on colloquial Singlish and Manglish corpus, hence it is best applied on downstream tasks involving the main
constituent languages- english, mandarin, malay. Also, as the training data is mainly from forums, beware of existing inherent bias.
## Training data
Colloquial singlish and manglish (both are a mixture of English, Mandarin, Tamil, Malay, and other local dialects like Hokkien, Cantonese or Teochew)
corpus. The corpus is collected from subreddits- `r/singapore` and `r/malaysia`, and forums such as `hardwarezone`.
## Training procedure
Initialized with [bert large uncased (whole word masking)](https://github.com/google-research/bert#pre-trained-models) vocab and checkpoints (pre-trained weights).
Top 1000 custom vocab tokens (non-overlapped with original bert vocab) were further extracted from training data and filled into unused tokens in original bert vocab.
Pre-training was further finetuned on training data with the following hyperparameters
* train_batch_size: 512
* max_seq_length: 128
* num_train_steps: 300000
* num_warmup_steps: 5000
* learning_rate: 2e-5
* hardware: TPU v3-8
|
ykacer/bert-base-cased-imdb-sequence-classification
|
ykacer
| 2021-05-20T09:31:37Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"sequence",
"classification",
"en",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
thumbnail: https://raw.githubusercontent.com/JetRunner/BERT-of-Theseus/master/bert-of-theseus.png
tags:
- sequence
- classification
license: apache-2.0
datasets:
- imdb
metrics:
- accuracy
---
|
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