---
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