
I was made with huggingtweets .
Create your own bot based on your favorite user with the demo !
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 Dovey "Rug The Fiat" Wan & BTC Times & ETH Zürich.
| Data | Dovey "Rug The Fiat" Wan | BTC Times | ETH Zürich |
|---|---|---|---|
| Tweets downloaded | 3244 | 3241 | 3246 |
| Retweets | 311 | 1215 | 1023 |
| Short tweets | 264 | 35 | 34 |
| Tweets kept | 2669 | 1991 | 2189 |
Explore the data , which is tracked with W&B artifacts at every step of the pipeline.
The model is based on a pre-trained GPT-2 which is fine-tuned on @btc-doveywan-eth'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/btc-doveywan-eth')
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.