🤖 AI CYBORG 🤖
greg & John W. Rich (Fake Tech Exec) & Dr. Parik Patel, BA, CFA, ACCA Esq. (drpatel.eth)
@cokedupoptions-greg16676935420-parikpatelcfa
I was made with
huggingtweets.
Create your own bot based on your favorite user with
the demo!
How does it work?
The model uses the following pipeline.
pipelineTo understand how the model was developed, check the
W&B report.
Training data
The model was trained on tweets from greg & John W. Rich (Fake Tech Exec) & Dr. Parik Patel, BA, CFA, ACCA Esq. (drpatel.eth).
| Data | greg | John W. Rich (Fake Tech Exec) | Dr. Parik Patel, BA, CFA, ACCA Esq. (drpatel.eth) |
|---|
| Tweets downloaded | 3247 | 3247 | 3250 |
| Retweets | 27 | 202 | 22 |
| Short tweets | 664 | 331 | 719 |
| Tweets kept | 2556 | 2714 | 2509 |
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 @cokedupoptions-greg16676935420-parikpatelcfa'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:
1from transformers import pipeline
2generator = pipeline('text-generation',
3 model='huggingtweets/cokedupoptions-greg16676935420-parikpatelcfa')
4generator("My dream is", num_return_sequences=5)
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
For more details, visit the project repository.
