| Site Reliability Engineering | |
| --- | |
| language: en | |
| thumbnail: http://www.huggingtweets.com/slime_machine/1640253262516/predictions.png | |
| tags: | |
| - huggingtweets | |
| widget: | |
| - text: "My dream is" | |
| --- | |
| <div class="inline-flex flex-col" style="line-height: 1.5;"> | |
| <div class="flex"> | |
| <div | |
| style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1468034520326701062/LDp_yytu_400x400.jpg')"> | |
| </div> | |
| <div | |
| style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> | |
| </div> | |
| <div | |
| style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> | |
| </div> | |
| </div> | |
| <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> | |
| <div style="text-align: center; font-size: 16px; font-weight: 800">rich homie cron</div> | |
| <div style="text-align: center; font-size: 14px;">@slime_machine</div> | |
| </div> | |
| I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). | |
| Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! | |
| ## How does it work? | |
| The model uses the following pipeline. | |
|  | |
| To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). | |
| ## Training data | |
| The model was trained on tweets from rich homie cron. | |
| | Data | rich homie cron | | |
| | --- | --- | | |
| | Tweets downloaded | 3234 | | |
| | Retweets | 590 | | |
| | Short tweets | 494 | | |
| | Tweets kept | 2150 | | |
| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/28uf2bgx/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. | |
| ## Training procedure | |
| The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @slime_machine's tweets. | |
| Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3h5ua6ik) for full transparency and reproducibility. | |
| At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3h5ua6ik/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/slime_machine') | |
| 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) | |