readme: ---
  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](https://huggingface.co/facebook/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