# huggingtweets /4by3animetits

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 8c3a439   09ba7bd 8c3a439                                                                                                                                                                   1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283848586 --- language: en thumbnail: https://www.huggingtweets.com/4by3animetits/1631600106043/predictions.png tags: - huggingtweets widget: - text: "My dream is" ---
🤖 AI BOT 🤖
Numb
@4by3animetits
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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Numb. | Data | Numb | | --- | --- | | Tweets downloaded | 3206 | | Retweets | 1497 | | Short tweets | 491 | | Tweets kept | 1218 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3pdw5mgr/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 @4by3animetits's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/5yrdnbzr) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/5yrdnbzr/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/4by3animetits') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)