πŸ€– AI CYBORG πŸ€–
AimbotAimy πŸžπŸ”ž NSFW V-Tuber & Poe's Law πŸ‡·πŸ‡Ί: 3.33 You can (not) redo & Demi 'ドダ鑔' Naga
@aimbotaimy-demi_naga-livingscribe

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 AimbotAimy πŸžπŸ”ž NSFW V-Tuber & Poe's Law πŸ‡·πŸ‡Ί: 3.33 You can (not) redo & Demi 'ドダ鑔' Naga.

Data AimbotAimy πŸžπŸ”ž NSFW V-Tuber Poe's Law πŸ‡·πŸ‡Ί: 3.33 You can (not) redo Demi 'ドダ鑔' Naga
Tweets downloaded 497 3242 3234
Retweets 60 433 909
Short tweets 125 564 341
Tweets kept 312 2245 1984

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 @aimbotaimy-demi_naga-livingscribe'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/aimbotaimy-demi_naga-livingscribe')
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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