🤖 AI CYBORG 🤖
The Tactical Times & Jack Grieve & Daniel Griffin MD PhD
@danielgriffinmd-jwgrieve-tactical_times

I was made with huggingtweets.

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How does it work?

The model uses the following pipeline.

pipeline

To understand how the model was developed, check the W&B report.

Training data

The model was trained on tweets from The Tactical Times & Jack Grieve & Daniel Griffin MD PhD.

Data The Tactical Times Jack Grieve Daniel Griffin MD PhD
Tweets downloaded 3248 3241 1832
Retweets 154 408 416
Short tweets 102 325 181
Tweets kept 2992 2508 1235

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 @danielgriffinmd-jwgrieve-tactical_times'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/danielgriffinmd-jwgrieve-tactical_times')
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

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For more details, visit the project repository.

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