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ChatGPT Prompt Generator v12

This model is a fine-tuned version of BART-large on a ChatGPT prompts dataset. It achieves the following results on the evaluation set: It achieves the following results on the evaluation set:

  • Train Loss: 2.4800
  • Validation Loss: 2.7320
  • Epoch: 4

Intended uses & limitations

You can use this to generate ChatGPT personas. Simply input a persona like below:

from transformers import BartForConditionalGeneration, BartTokenizer

example_english_phrase = "photographer"
batch = tokenizer(example_english_phrase, return_tensors="pt")
generated_ids = model.generate(batch["input_ids"], max_new_tokens=150)
output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
  • training_precision: float32

Training results

Train Loss Validation Loss Epoch
5.3808 3.3133 0
3.2642 3.0104 1
2.8886 2.8600 2
2.6594 2.7949 3
2.4800 2.7320 4

Framework versions

  • Transformers 4.26.1
  • TensorFlow 2.11.0
  • Datasets 2.10.1
  • Tokenizers 0.13.2
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