I was made with huggingtweets.
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The model uses the following pipeline.
To understand how the model was developed, check the W&B report.
The model was trained on tweets from Ho3K | Daramgar 🔜 CROSSxUP & clementine!!!! 𓃠 & camera! (low tier).
| Data | Ho3K | Daramgar 🔜 CROSSxUP | clementine!!!! 𓃠 | camera! (low tier) | | --- | --- | --- | --- | | Tweets downloaded | 3249 | 3185 | 3211 | | Retweets | 30 | 439 | 1053 | | Short tweets | 807 | 719 | 556 | | Tweets kept | 2412 | 2027 | 1602 |
The model is based on a pre-trained GPT-2 which is fine-tuned on @clamtime-daramgaria-ledgeguard'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.
You can use this model directly with a pipeline for text generation:
from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/clamtime-daramgaria-ledgeguard') generator("My dream is", num_return_sequences=5)
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.
Built by Boris Dayma
For more details, visit the project repository.
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