boris commited on
Commit
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New model from https://wandb.ai/wandb/huggingtweets/runs/10wax6wi

Browse files
README.md CHANGED
@@ -1,28 +1,27 @@
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  ---
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  language: en
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- thumbnail: https://www.huggingtweets.com/_bravit/1603725464691/predictions.png
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  tags:
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  - huggingtweets
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  widget:
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  - text: "My dream is"
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  ---
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- <link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
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-
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- <style>
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- @media (prefers-color-scheme: dark) {
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- .prose { color: #E2E8F0 !important; }
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- .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
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- }
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- </style>
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-
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- <section class='prose'>
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-
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- <div>
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- <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1173227862519963650/4YGv_qlD_400x400.jpg')">
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- </div>
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- <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Виталий Брагилевский 🤖 AI Bot </div>
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- <div style="font-size: 15px; color: #657786">@_bravit bot</div>
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  </div>
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  I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
@@ -35,62 +34,41 @@ The model uses the following pipeline.
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  ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)
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- 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).
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  ## Training data
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- The model was trained on [@_bravit's tweets](https://twitter.com/_bravit).
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-
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- <table style='border-width:0'>
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- <thead style='border-width:0'>
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- <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
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- <th style='border-width:0'>Data</th>
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- <th style='border-width:0'>Quantity</th>
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- </tr>
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- </thead>
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- <tbody style='border-width:0'>
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- <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
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- <td style='border-width:0'>Tweets downloaded</td>
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- <td style='border-width:0'>3226</td>
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- </tr>
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- <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
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- <td style='border-width:0'>Retweets</td>
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- <td style='border-width:0'>800</td>
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- </tr>
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- <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
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- <td style='border-width:0'>Short tweets</td>
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- <td style='border-width:0'>505</td>
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- </tr>
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- <tr style='border-width:0'>
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- <td style='border-width:0'>Tweets kept</td>
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- <td style='border-width:0'>1921</td>
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- </tr>
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- </tbody>
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- </table>
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-
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- [Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/2aavgmq5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
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  ## Training procedure
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  The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @_bravit's tweets.
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- Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/2j6zoydv) for full transparency and reproducibility.
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-
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- At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/2j6zoydv/artifacts) is logged and versioned.
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- ## Intended uses & limitations
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- ### How to use
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  You can use this model directly with a pipeline for text generation:
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- <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline
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- generator = pipeline(<span style="color:#FF9800">'text-generation'</span>,
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- model=<span style="color:#FF9800">'huggingtweets/_bravit'</span>)
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- generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
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-
 
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- ### Limitations and bias
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  The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
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@@ -100,14 +78,8 @@ In addition, the data present in the user's tweets further affects the text gene
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  *Built by Boris Dayma*
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- </section>
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-
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  [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)
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- <section class='prose'>
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  For more details, visit the project repository.
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- </section>
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  [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
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-
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- <!--- random size file -->
 
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  ---
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  language: en
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+ thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
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  tags:
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  - huggingtweets
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  widget:
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  - text: "My dream is"
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  ---
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+ <div class="inline-flex flex-col" style="line-height: 1.5;">
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+ <div class="flex">
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+ style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1322230137493065729/-h1nJf6U_400x400.jpg&#39;)">
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+ </div>
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+ style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)">
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+ </div>
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+ <div
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+ style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)">
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+ </div>
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+ </div>
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+ <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
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+ <div style="text-align: center; font-size: 16px; font-weight: 800">Виталий Брагилевский</div>
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+ <div style="text-align: center; font-size: 14px;">@_bravit</div>
 
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  </div>
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  I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
 
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35
  ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)
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+ To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
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  ## Training data
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+ The model was trained on tweets from Виталий Брагилевский.
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+
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+ | Data | Виталий Брагилевский |
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+ | --- | --- |
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+ | Tweets downloaded | 3233 |
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+ | Retweets | 884 |
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+ | Short tweets | 489 |
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+ | Tweets kept | 1860 |
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+
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+ [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ekzbpfn/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Training procedure
53
 
54
  The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @_bravit's tweets.
55
 
56
+ Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/10wax6wi) for full transparency and reproducibility.
 
 
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+ At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/10wax6wi/artifacts) is logged and versioned.
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+ ## How to use
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62
  You can use this model directly with a pipeline for text generation:
63
 
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+ ```python
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+ from transformers import pipeline
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+ generator = pipeline('text-generation',
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+ model='huggingtweets/_bravit')
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+ generator("My dream is", num_return_sequences=5)
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+ ```
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+ ## Limitations and bias
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73
  The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
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  *Built by Boris Dayma*
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  [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma)
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  For more details, visit the project repository.
 
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  [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
 
 
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