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 🚀 Altcoin Gem God 💎 & Crypto GEMs 📈🚀 & SharkCoins 🦈 #BSC #ETH Gem Hunter.

Data	🚀 Altcoin Gem God 💎	Crypto GEMs 📈🚀	SharkCoins 🦈 #BSC #ETH Gem Hunter
Tweets downloaded	490	3215	3229
Retweets	23	763	109
Short tweets	247	779	62
Tweets kept	220	1673	3058

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 @altcoingemgod-cryptogems555-sharkscoins'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/altcoingemgod-cryptogems555-sharkscoins')
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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