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README.md
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---
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language: en
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thumbnail:
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tags:
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- huggingtweets
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widget:
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To understand how the model was developed, check the [W&B report](https://
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## Training data
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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'>
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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'>
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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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</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'>
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</tr>
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</tbody>
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</table>
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[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/
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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 @l2k's tweets.
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Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/
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At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/
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## Intended uses & limitations
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---
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language: en
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thumbnail: http://res.cloudinary.com/huggingtweets/image/upload/v1599871089/l2k.jpg
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tags:
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- huggingtweets
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widget:
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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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<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'>2580</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'>598</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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</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'>1894</td>
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</tr>
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</tbody>
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</table>
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[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/17e2cw73/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 @l2k's tweets.
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Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/10mi5zis) for full transparency and reproducibility.
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At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/10mi5zis/artifacts) is logged and versioned.
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## Intended uses & limitations
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