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metadata
license: apache-2.0
tags:
  - generated_from_keras_callback
model-index:
  - name: chatgpt-prompts-bart-long
    results: []
datasets:
  - fka/awesome-chatgpt-prompts

ChatGPT Prompt Generator

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

  • Train Loss: 2.8329
  • Validation Loss: 2.5015
  • 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
8.4973 6.3592 0
5.3145 3.2640 1
3.5899 2.8350 2
3.1044 2.6154 3
2.8329 2.5015 4

Framework versions

  • Transformers 4.26.0
  • TensorFlow 2.9.2
  • Datasets 2.8.0
  • Tokenizers 0.13.2