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How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the W&B report.
Training data
The model was trained on tweets from Humongous Ape MP & ste 🍊 & Fake Showbiz News & Ninja Sex Party but AI & gpt up a guy(?) & waint.
| Data | Humongous Ape MP | ste 🍊 | Fake Showbiz News | Ninja Sex Party but AI | gpt up a guy(?) | waint |
|---|---|---|---|---|---|---|
| Tweets downloaded | 3245 | 3193 | 3250 | 692 | 3250 | 103 |
| Retweets | 196 | 302 | 1 | 13 | 16 | 11 |
| Short tweets | 609 | 488 | 1 | 44 | 10 | 2 |
| Tweets kept | 2440 | 2403 | 3248 | 635 | 3224 | 90 |
Explore the data, which is tracked with W&B artifacts at every step of the pipeline.
Training procedure
The model is based on a pre-trained GPT-2 which is fine-tuned on @apesahoy-chai_ste-fakeshowbiznews-gptupaguy-nsp_gpt2-powerdril_gpt2'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.
How to use
You can use this model directly with a pipeline for text generation:
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/apesahoy-chai_ste-fakeshowbiznews-gptupaguy-nsp_gpt2-powerdril_gpt2')
generator("My dream is", num_return_sequences=5)
Limitations and bias
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.
About
Built by Boris Dayma
For more details, visit the project repository.
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