I was made with huggingtweets.
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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 luisfer nando & nomes foda de dj & nomes de gato.
| Data | luisfer nando | nomes foda de dj | nomes de gato |
|---|---|---|---|
| Tweets downloaded | 2357 | 3250 | 3211 |
| Retweets | 365 | 6 | 69 |
| Short tweets | 503 | 632 | 1710 |
| Tweets kept | 1489 | 2612 | 1432 |
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 @_luisinhobr-nomesdegato-nomesdj'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/_luisinhobr-nomesdegato-nomesdj')
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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