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 @danellisscience's tweets.

Data	Quantity
Tweets downloaded	532
Retweets	107
Short tweets	30
Tweets kept	395

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 @danellisscience'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.

Intended uses & limitations

How to use

You can use this model directly with a pipeline for text generation:

[object Object] transformers [object Object] pipeline
generator = pipeline([object Object],
                     model=[object Object])
generator([object Object], num_return_sequences=[object Object])

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.

huggingtweets/danellisscience