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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 Product Hunt 😸 & Kate Rooney & NerdWallet.
| Data | Product Hunt 😸 | Kate Rooney | NerdWallet |
|---|---|---|---|
| Tweets downloaded | 3250 | 2622 | 3234 |
| Retweets | 92 | 896 | 713 |
| Short tweets | 234 | 125 | 22 |
| Tweets kept | 2924 | 1601 | 2499 |
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 @kr00ney-nerdwallet-producthunt'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/kr00ney-nerdwallet-producthunt')
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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