๐Ÿค– AI CYBORG ๐Ÿค–
๐Ÿ‡น๐Ÿ‡ญ๐Ÿ‘ธ๐Ÿฝโ™ ๏ธ Thai Queen of Spades โ™ ๏ธ๐Ÿ‘ธ๐Ÿฝ๐Ÿ‡น๐Ÿ‡ญ 7.25K & Hanna โ™  & โ™ ๏ธ Hayley โ™ ๏ธ
@hannabbc-hfrost3000-thaiqos

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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 ๐Ÿ‡น๐Ÿ‡ญ๐Ÿ‘ธ๐Ÿฝโ™ ๏ธ Thai Queen of Spades โ™ ๏ธ๐Ÿ‘ธ๐Ÿฝ๐Ÿ‡น๐Ÿ‡ญ 7.25K & Hanna โ™  & โ™ ๏ธ Hayley โ™ ๏ธ.

Data ๐Ÿ‡น๐Ÿ‡ญ๐Ÿ‘ธ๐Ÿฝโ™ ๏ธ Thai Queen of Spades โ™ ๏ธ๐Ÿ‘ธ๐Ÿฝ๐Ÿ‡น๐Ÿ‡ญ 7.25K Hanna โ™  โ™ ๏ธ Hayley โ™ ๏ธ
Tweets downloaded 639 1044 365
Retweets 247 0 114
Short tweets 37 164 19
Tweets kept 355 880 232

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 @hannabbc-hfrost3000-thaiqos'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/hannabbc-hfrost3000-thaiqos')
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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