I was made with huggingtweets.
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The model uses the following pipeline.
To understand how the model was developed, check the W&B report.
The model was trained on tweets from NaN & MaX 🎤 & ivy 🥩🎙️.
|Data||NaN||MaX 🎤||ivy 🥩🎙️|
The model is based on a pre-trained GPT-2 which is fine-tuned on @formernumber-wmason_iv-wyattmaxon's tweets.
Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.
At the end of training, the final model is logged and versioned.
You can use this model directly with a pipeline for text generation:
from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/formernumber-wmason_iv-wyattmaxon') generator("My dream is", num_return_sequences=5)
The model suffers from the same limitations and bias as GPT-2.
In addition, the data present in the user's tweets further affects the text generated by the model.
Built by Boris Dayma
For more details, visit the project repository.
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