

# 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
{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "datasets": ["fka/awesome-chatgpt-prompts"], "model-index": [{"name": "chatgpt-prompts-bart-long", "results": []}]}
transformers tf bart text2text-generation generated_from_keras_callback dataset:fka/awesome-chatgpt-prompts license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #tf #bart #text2text-generation #generated_from_keras_callback #dataset-fka/awesome-chatgpt-prompts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

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:


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



### Framework versions


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



### 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 ### Framework versions


* Transformers 4.26.0
* TensorFlow 2.9.2
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #tf #bart #text2text-generation #generated_from_keras_callback #dataset-fka/awesome-chatgpt-prompts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### 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 ### Framework versions


* Transformers 4.26.0
* TensorFlow 2.9.2
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #tf #bart #text2text-generation #generated_from_keras_callback #dataset-fka/awesome-chatgpt-prompts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
### 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### Framework versions


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