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---
license: apache-2.0
tags:
- generated_from_keras_callback
datasets:
- fka/awesome-chatgpt-prompts
base_model: BART-large
model-index:
- name: chatgpt-prompts-bart-long
results: []
---
# 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 |