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--- |
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language: en |
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thumbnail: http://www.huggingtweets.com/sarahschauer/1664420129547/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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<div class="inline-flex flex-col" style="line-height: 1.5;"> |
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<div class="flex"> |
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<div |
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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('https://pbs.twimg.com/profile_images/1495140751058997250/oC5BO6Di_400x400.jpg')"> |
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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('')"> |
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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('')"> |
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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">sarah schauer 🦂</div> |
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<div style="text-align: center; font-size: 14px;">@sarahschauer</div> |
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</div> |
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I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). |
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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)! |
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## How does it work? |
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The model uses the following pipeline. |
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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 sarah schauer 🦂. |
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| Data | sarah schauer 🦂 | |
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| --- | --- | |
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| Tweets downloaded | 3243 | |
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| Retweets | 212 | |
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| Short tweets | 482 | |
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| Tweets kept | 2549 | |
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[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1soke9pb/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 @sarahschauer's tweets. |
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Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1xa4vhrq) for full transparency and reproducibility. |
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At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1xa4vhrq/artifacts) is logged and versioned. |
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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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```python |
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from transformers import pipeline |
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generator = pipeline('text-generation', |
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model='huggingtweets/sarahschauer') |
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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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The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). |
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In addition, the data present in the user's tweets further affects the text generated by the model. |
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## About |
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*Built by Boris Dayma* |
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[](https://twitter.com/intent/follow?screen_name=borisdayma) |
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For more details, visit the project repository. |
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[](https://github.com/borisdayma/huggingtweets) |
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